Presentation by the CEO of ImpactScope, Sean Murphy and Steve Cracknell, Chief Product Officer &amp; Founder Director of INSIG AI at the World Artificial Intelligence Cannes Festival on using AI to help financial regulators detect greenwashing.

Using-AI-to-help-financial-regulators-detect-greenwashing

Using AI to help financial regulators detect greenwashing.  Presentation with ImpactScope at World AI Cannes Festival 2024

<h2>Introduction</h2>
In the rapidly evolving intersection of technology and corporate sustainability reporting, there is a growing role for specialised Large Language Models (LLMs).
Open-source generalist LLMs like GPT-4 are ground-breaking.  And the next natural step is to explore the unique value that topic specific LLMs can bring.
While not suitable for many tasks, there are exciting possibilities to use LLMs to consolidate vast amounts of information to support human analysis across business, finance and sustainability.
This article explains the differences between generic and specialised LLMs and the rationale and process for creating them, focussing on use cases in sustainable business and investment.
<h2>Why are LLMs a good fit for corporate sustainability?</h2>
Experts bridging AI and corporate sustainability recognise that the application of LLMs in corporate sustainability is not just part of the Generative AI buzz, but practical and, while it’s early days, likely to become the norm.
Companies are under huge pressure from investors, regulators and consumers to publish ever more environmental and social sustainability disclosure alongside financial and governance reporting.
This creates an enormous burden to report comprehensively and accurately, and a burden also for stakeholders to make sense of that information in context.  Disclosure should explain what difference the company’s sustainability initiatives make to their risk and impact on people and planet, but everyone working in this space knows how hard it is see the wood for the trees.
With supply chain (also known as value chain or Scope 3) information now included under EU and other reporting regulation, the challenge of keeping data cohesive and meaningful is only growing.  Analysts are already drowning in data and overwhelmed with a massive research burden, further compounded by concerns over greenwashing.
<h3>Specialised LLMs models offer:</h3>
&#8211; Expertise for nuanced outputs: A topic centric LLM can be trained in social impact indicators, environmental technologies, risk and impact frameworks or the latest regulations.  Humans can’t be experts at everything in a fast-moving landscape, and a specialised LLM’s outputs can save research time and enhance a human team’s efforts in analysis.
&#8211; Better decision-making: For businesses, navigating the increasingly complex web of corporate reporting, and encompassing supplier performance into this, requires informed and evidence-based decisions. An LLM focused on this area can synthesise vast, messy datasets into actionable insights.
&#8211; Driving innovation: The dynamic and complex field of corporate sustainability and investment is ripe for the predictive, big data capabilities of this technology. When applied responsibly and trained expertly, LLMs in this space are a great example of using AI for good.
<h2>What’s the difference between a generic and specialised LLM? </h2>
<h3>Generic LLMs</h3>
 Broad knowledge base: Generic LLMs like BERT, GPT-4, Bard and Claude (to name a few) are trained on a vast array of topics. They have a wide-ranging but sometimes superficial understanding of many subjects, from everyday knowledge to specific fields.
 General purpose: Designed for a variety of applications, they are versatile. Whether it&#8217;s writing content, coding, answering trivia or providing business advice, they can handle a multitude of tasks.
 Flexibility: Due to their broad training, generic LLMs can pivot between topics easily, making them useful for unpredictable queries.
 Training data: They are trained on diverse datasets, often encompassing a large chunk of the publicly available internet text, which includes books, websites, articles and more.
 Limitations in specialisation: While capable in many areas, breadth can lead to a lack of depth. They may not always provide the most up-to-date or in-depth information on specialised topics.
<h3>Specialised LLMs</h3>
 Focused expertise: These models are fine-tuned on a specific subject area and this training allows them to provide more detailed and accurate responses in their area. FinBERT, ESGBERT and ClimateBERT are good examples of more topic centric models, which can form the basis of even more specialised models.
 Targeted use: Their use can be better targeted. A sustainability focussed LLM could be trained on international ESG frameworks, or be more tailored to handle queries relating to the particular priorities of the user, such as supplier community and human rights performance in line with international standards.
 Depth over breadth: While they might not possess the same versatility across a wide range of topics, their capacity for accuracy in a particular field is significantly greater.
 Customised training data: The training dataset is curated to include extensive information about the particular topic. This might involve academic articles, industry reports and high-quality disclosures.
 Precision and relevance: Due to their specialised nature, these models are more likely to provide up-to-date, nuanced, and contextually relevant information in their area of specialism.
<h3>In Summary&#8230;</h3>
While a generic LLM is a ‘jack of all trades’, a topic specific LLM is a ‘master’ in its domain, providing depth and precision in its outputs in a particular field. The choice between the two depends on the needs and goals of the user or application.
<h2>How to build a topic specific LLM</h2>
Creating an LLM suitable for supporting sustainable business or finance involves several key steps:
1. Data gathering: The foundation for any model is a comprehensive dataset encompassing robust sources such as scientific research, industry reports, regulatory documents and reliable news in the sector.
2. Data pre-processing: This step involves cleaning, standardising, tagging and labelling the data and ensuring it&#8217;s relevant and unbiased.
3. Model training: Utilising machine learning techniques, the model is trained on this curated dataset. This phase demands significant computational resources, AI expertise and supervision.
4. Fine-tuning and ongoing evaluation: Post-training, the model undergoes fine-tuning with more specific datasets and use cases. Regular evaluations and updates are essential to maintain its accuracy and relevance.
5. Ethical alignment: It’s vital to ensure the model&#8217;s adherence to ethical standards, particularly in data sensitivity and bias mitigation.
<h2>Real-world applications of a specialised LLM</h2>
The potential use cases of a specialised LLM in sustainable business and finance are vast.  Here are a few examples:
&#8211; Sustainable investment research: By incorporating large datasets, an LLM can inform sustainable investment strategies, identify risks and highlight opportunities in line with a framework such as the SDGs.
&#8211; Navigating regulatory compliance: Businesses can use a bespoke model to stay abreast of and comply with the latest sustainability regulations.
&#8211; Supply chain sustainability assessment: Companies can track the environmental and social profile and performance of suppliers to monitor and report their own sustainability footprint.
&#8211; Identify and monitor greenwashing: A specially trained model can aid in identifying potential flags for corporate and financial greenwashing when mapped to a defined framework.
&#8211; Aiding public policy: Governments can use these models to inform policymaking, based on environmental data analysis and impact forecasting.
<h2>Conclusion</h2>
The development of a specialised LLMs is not a stand-alone ‘clever’ technological advancement to be adopted for the sake of it.  When done well, it is a step towards more research-driven, evidence-based decision making in business and finance.
We believe the role of this technology will become a driver for sustainable growth, centred on high quality data, targeted design and ethical practice.  When applied responsibly, specialised LLMs will support innovation, adaptation and improvement in the challenge of achieving our shared global goals for sustainable business and investment.

specialised-LLMs-for-corporate-sustainability-reporting

Harnessing specialised large language models for corporate sustainability reporting

Following the launch in early November of the Transparency &amp; Disclosure Index (TDI) covering UK large cap companies, Insig AI has added further coverage of UK mid-cap listed businesses.  Insig’s ESG Team share their key findings from the data comparison between the two groups of companies.
<h3>Documents being published: a growing haystack</h3>
Mirroring the strong trend seen among large-cap companies, there has been significant growth (around 500%) in the number of ESG-related documents published by the mid-cap companies between 2015 and 2022.  The volume nearly doubled over two years 2020-2022, indicating that mid-cap companies have more recently felt the pressure ramping up to publish more documents to meet growing demand for ESG-related information from investors, ratings agencies and other stakeholders.
The average number of documents published by a mid-cap UK company has gone from 5 in 2020 to 10 in 2022, compared to an increase from 11 to 17 published on average by a large-cap company.  This is being driven by various factors including regulation, often designed to trickle down from larger companies to smaller.
<img decoding="async" class="aligncenter wp-image-3259 size-fusion-800" src="https://insigai.io/wp-content/uploads/2023/11/Largecap-v1-800x484.png" alt="" width="800" height="484" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Largecap-v1.png?resize=200%2C121&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Largecap-v1.png?resize=300%2C182&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Largecap-v1.png?resize=400%2C242&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Largecap-v1.png?resize=600%2C363&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Largecap-v1.png?resize=768%2C465&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Largecap-v1.png?resize=800%2C484&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Largecap-v1.png?resize=1024%2C620&amp;ssl=1 1024w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Largecap-v1.png?resize=1200%2C727&amp;ssl=1 1200w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Largecap-v1.png?resize=1536%2C930&amp;ssl=1 1536w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Largecap-v1.png?fit=1952%2C1182&amp;ssl=1 1952w" sizes="(max-width: 800px) 100vw, 800px" />
<img decoding="async" class="aligncenter wp-image-3260 size-fusion-800" src="https://insigai.io/wp-content/uploads/2023/11/Midcap-v1-800x518.png" alt="" width="800" height="518" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Midcap-v1.png?resize=200%2C129&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Midcap-v1.png?resize=300%2C194&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Midcap-v1.png?resize=400%2C259&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Midcap-v1.png?resize=600%2C388&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Midcap-v1.png?resize=768%2C497&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Midcap-v1.png?resize=800%2C518&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Midcap-v1.png?resize=1024%2C663&amp;ssl=1 1024w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Midcap-v1.png?resize=1200%2C777&amp;ssl=1 1200w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Midcap-v1.png?resize=1536%2C994&amp;ssl=1 1536w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Midcap-v1.png?fit=1883%2C1219&amp;ssl=1 1883w" sizes="(max-width: 800px) 100vw, 800px" />
This implies greater transparency around non-financial information through disclosure, which is welcome.  But there are two risks: companies reacting (or over-reacting) and publishing in a scattergun approach regardless of what’s material; and an overwhelming burden on analysts of collecting these documents and sifting through the haystack.
The TDI illuminates where gaps in reporting persist despite volume going up.  It seeks to help companies take stock, benchmark themselves and navigate this fine line of getting the balance right between expected, comprehensive disclosure and obscuring information amid noise.
<h3>Disclosure vs market cap trend: a long way from convergence</h3>
There is a stark difference in the trends seen in the data between the UK large-cap and mid-cap groups, when looking at the number of documents published, the volume of sentences in these documents, and the size of the company.
<img decoding="async" class="aligncenter wp-image-3261 size-full" src="https://insigai.io/wp-content/uploads/2023/11/Picture-3-TDI-blog.png" alt="" width="754" height="454" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Picture-3-TDI-blog.png?resize=200%2C120&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Picture-3-TDI-blog.png?resize=300%2C181&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Picture-3-TDI-blog.png?resize=400%2C241&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Picture-3-TDI-blog.png?resize=600%2C361&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Picture-3-TDI-blog.png?fit=754%2C454&amp;ssl=1 754w" sizes="(max-width: 754px) 100vw, 754px" />
<img decoding="async" class="aligncenter size-full wp-image-3262" src="https://insigai.io/wp-content/uploads/2023/11/Picture-4-TDI-blog.png" alt="" width="754" height="500" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Picture-4-TDI-blog.png?resize=200%2C133&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Picture-4-TDI-blog.png?resize=300%2C199&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Picture-4-TDI-blog.png?resize=400%2C265&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Picture-4-TDI-blog.png?resize=600%2C398&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/11/Picture-4-TDI-blog.png?fit=754%2C500&amp;ssl=1 754w" sizes="(max-width: 754px) 100vw, 754px" />
Across the group of companies with larger market cap, there is wide dispersion across all factors, revealing a complete lack of consistency in meeting stakeholder demands for sustainability information.
Documents published range from 2 to 47; and among the UK’s largest companies alone, disclosure volumes across a company’s reports in a year swing between around 7,500 to 16,500 sentences.  (Note &#8211; we estimate it would take an analyst a year and a half to read all these sentences published in 2022 by the large-caps).
Looking at the mid-cap group of companies, the trend shows that overall levels of disclosure are more consistently lower, with all but one (Investec) publishing less than 10,000 sentences.  However, there is still a broad range in number of documents put out in the public domain, ranging from 1 to 30 reports, policies and other documents.
<h3>A waste of words?</h3>
The intention of the convergence to a global framework for non-financial reporting with the International Sustainability Standards Board (ISSB), and all the talk of interoperability between these international voluntary standards and emerging regulation, is to drive consistency, comparability and conciseness.  This is ultimately so that both companies and investors are on the same page about what matters and should be reported, so that together they can drive long-term value tied to stewardship of people and planet.  Without this goal in sight, the proliferation of words is a waste of time.
The data confirms that consolidation is urgently needed, and that we still have a long way to go.  The ESG Team at Insig AI will be tracking these trends to answer two big questions:
&#8211; As regulation and stakeholder pressure trickles down, can this group of UK mid-cap companies (and others following) avoid the pitfalls that some of the first movers of the large-cap companies have fallen into?
&#8211; Are global standards and regulation having the desired effect on transparency and disclosure, or unintended consequences?
Visit the TDI to interact with the live data behind the analysis: <a href="https://tdi-demo.insg.ai/">www.tdi-demo.insg.ai</a>

TDI-reveals-reporting-patterns-acorss-UK-largest-companies

Transparency & Disclosure Index reveals stark differences in reporting patterns across UK's largest companies

<h3>Insig AI’s ESG disclosure data supports EY findings on lack of credible FTSE100 climate transition plans and reveals other sectoral and reporting trends.</h3>
Introduction
The Financial Times reported that, according to a study by EY, only 5% of FTSE100 companies have ‘credible’ climate transition plans in line with the UK’s Transition Plan Taskforce’s draft disclosure framework for large companies (3rd April 2023).
The gap between Net Zero ambitions and transparent transition plans to achieve them is of concern to investors who need to make decisions based on detailed information on adaptations to business operations, products and services, and the financial plans behind this.
Insig AI analysis
In response to the FT article, our Insig AI ESG analysts ran some keyword analytics over our machine-readable library of corporate financial reports and ESG documents that were published by the FTSE100 in 2021/22.
We searched for the terms ‘climate transition’, ‘carbon transition’ and ‘energy transition’ (grouped as ‘transition mentions’ in our analysis) as well as the term ‘net zero’ across all documents published in 2021 and 2022*.
The table below summarises the trends we found.  The data is averaged for the number of FTSE100 companies belonging to a sector. It shows ‘transition mentions’ in pink alongside ‘net zero’ mentions in green.
<img decoding="async" class="alignnone wp-image-3048" src="https://insigai.io/wp-content/uploads/2023/04/Screenshot-2023-04-04-at-15.56.21.png" alt="" width="951" height="567" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2023/04/Screenshot-2023-04-04-at-15.56.21.png?resize=200%2C119&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/04/Screenshot-2023-04-04-at-15.56.21.png?resize=300%2C179&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/04/Screenshot-2023-04-04-at-15.56.21.png?resize=400%2C239&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/04/Screenshot-2023-04-04-at-15.56.21.png?resize=600%2C358&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/04/Screenshot-2023-04-04-at-15.56.21.png?resize=768%2C458&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/04/Screenshot-2023-04-04-at-15.56.21.png?resize=800%2C477&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/04/Screenshot-2023-04-04-at-15.56.21.png?resize=1024%2C611&amp;ssl=1 1024w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/04/Screenshot-2023-04-04-at-15.56.21.png?resize=1200%2C716&amp;ssl=1 1200w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/04/Screenshot-2023-04-04-at-15.56.21.png?resize=1536%2C916&amp;ssl=1 1536w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/04/Screenshot-2023-04-04-at-15.56.21.png?fit=1754%2C1046&amp;ssl=1 1754w" sizes="(max-width: 951px) 100vw, 951px" />
Our top three trends that leap out of the data:
1. Laggards
The Health Care sector made no ‘transition mentions’ in any of their public disclosures over the past 2 years.  This indicates they are lagging in preparedness for both reporting compliance and decarbonisation itself.
Consumer Discretionary, Consumer Staples, Communications Services and Real Estate all published an average of less than 10 ‘transition mentions’ across their reports.  The average number of ‘transition mentions’ per FTSE100 company over the two years is 47, pulled up by the Energy and Utilities sectors.
2. Net Zero vs Transition
The data shows a gap between &#8216;net zero’ and ‘transition mentions’ that could indicate greenwashing; getting credit from consumers and investors today for promises but without a plan to fulfil them.
This gap is particularly noticeable in the Real Estate sector, in which the average company mentions ‘net zero’ 169 times across its reports, alongside only 3 ‘transition mentions’.  The exception to the trend is the Energy sector.
3. Scattered Evidence
We found that the highest proportion of ‘transition mentions’ were found in Annual Reports, which is where investors should expect to find auditable and robust disclosure on the subject.
However, the key information investors are calling for is still scattered.  The next group of documents that were the richest in transition information were ESG documents, which can be reported in very different formats and locations and take the form of Climate Change Strategies, Policies, Statements, Reports etc.  This body of documents contain more than double the number of ‘transition mentions’ than the Sustainability and ESG Reports themselves which are published annually.
Conclusions
The data highlights the variance between sectors in the FTSE100 in their preparedness to meet the UK’s Transition Plan Taskforce’s disclosure expectations, as well as how this information is reported to assure stakeholders of their strategic and financial plans to meet Net Zero ambitions.
*Report type and number of sentences analysed: Financial Report (Annual) 649,662 | Earnings Transcript 396,682 | ESG Document 158,861 | Financial Document 119,397 | ESG Report (Annual) 99,213

FTSE100-Climate-Transition-Plans

Only 5% of FTSE100 have credible climate transitions plans according to EY: Insig AI's response

<h2>Generative AI: The game-changing access to advanced technology</h2>
The hype around Generative AI (GenAI) is real, and the advancements we will start to see with this technology being made widely accessible are going to be exponential.
AI has always been veiled within its infamous ‘black box’, with most people being concerned and dubious about what is going on under the hood.
With GenAI and specifically ChatGPT however, you see its behaviour unfold in front of your eyes and you can question its logic and challenge its responses. In addition to being able to ask complex questions, you can also ask it to perform useful sorting, summarising and explanation tasks of your own data.
Contextual data and text-based algorithms like ChatGPT are insanely difficult to build and train (let alone use) and have historically only been available to a very small and specialist audience of proverbial ‘rocket scientists’.
The excitement being seen in the market around GenAI is a testament to making complex technology easy to use and widely accessible. In fact, I would go as far as suggesting the rebranding of AI from ‘Artificial Intelligence’ to ‘Accessible Intelligence’.
This really is going to be a game changer for businesses.
<h3>How we make GenAI complement our own technology</h3>
As a technology company that develops our own machine learning algorithms, NLP (natural language processing) classifiers and AI tools to help asset managers look for better and faster ways to find alpha and attract capital with high quality ESG credentials, the most interesting thing about GenAI is the application of these sophisticated contextual models to our own datasets.
We have over 100 million corporate ESG disclosure sentences in our database, covering 4k major global companies and 160k unique documents. With all our data stored in a machine-readable format, the utility of ChatGPT and other GenAI tools is vast.
We have solved the data collection, transformation out of PDF and storage problem for corporate ESG disclosures. Our 15 NLP classifiers help group the disclosures into more manageable and consumable topics, e.g. climate change, water, supply chain management and workforce.
Applying the contextual data management and sorting capabilities of ChatGPT to our filtered dataset helps to deliver the ‘last mile’ when it comes to providing and packaging actionable insights out of our data for our clients.
<h3>Scrutinising huge volumes of corporate disclosure</h3>
Looking at this more practically, here are some examples.
At the most basic level, you can ask ChatGPT to summarise the key points from a body of text. The Chairmans’ Statement in an Annual Report is a good example. Once you get the summary back, you can ask ChatGPT to split the sentences into positive and negative sentiment blocks.
Original Annual Report in PDF format<img decoding="async" class="alignnone wp-image-2997" src="https://insigai.io/wp-content/uploads/2023/03/Picture-1.png" alt="" width="838" height="594" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-1.png?resize=200%2C142&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-1.png?resize=300%2C214&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-1.png?resize=400%2C283&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-1.png?resize=600%2C425&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-1.png?resize=768%2C544&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-1.png?resize=800%2C567&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-1.png?fit=892%2C632&amp;ssl=1 892w" sizes="(max-width: 838px) 100vw, 838px" />
Insig.AI’s machine readable text extracted from the PDF<img decoding="async" class="alignnone wp-image-2998 " src="https://insigai.io/wp-content/uploads/2023/03/Picture-2.png" alt="" width="837" height="530" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-2.png?resize=200%2C126&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-2.png?resize=300%2C190&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-2.png?resize=320%2C202&amp;ssl=1 320w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-2.png?resize=400%2C253&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-2.png?resize=600%2C379&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-2.png?resize=768%2C486&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-2.png?resize=800%2C506&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-2.png?resize=1024%2C648&amp;ssl=1 1024w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-2.png?resize=1200%2C759&amp;ssl=1 1200w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-2.png?fit=1420%2C898&amp;ssl=1 1420w" sizes="(max-width: 837px) 100vw, 837px" />
ChatGPT data sorting and summarising capability
<img decoding="async" class="alignnone wp-image-3004" src="https://insigai.io/wp-content/uploads/2023/03/Picture-3.png" alt="" width="492" height="387" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-3.png?resize=200%2C157&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-3.png?resize=300%2C236&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-3.png?resize=400%2C314&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-3.png?resize=600%2C471&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-3.png?resize=768%2C603&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-3.png?resize=800%2C629&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-3.png?fit=840%2C660&amp;ssl=1 840w" sizes="(max-width: 492px) 100vw, 492px" /><img decoding="async" class="alignnone wp-image-3003" src="https://insigai.io/wp-content/uploads/2023/03/Picture-4.png" alt="" width="435" height="385" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-4.png?resize=200%2C177&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-4.png?resize=300%2C266&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-4.png?resize=400%2C354&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-4.png?resize=600%2C532&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-4.png?fit=745%2C660&amp;ssl=1 745w" sizes="(max-width: 435px) 100vw, 435px" />
Another functional use case is asking ChatGPT to return references to “all net zero targets being set in the future by a company, but do so in a tabular format, splitting text from dates”.
Insig.AI NLP classifiers returns sentences about ‘Climate Change’<img decoding="async" class="alignnone wp-image-3002 " src="https://insigai.io/wp-content/uploads/2023/03/Picture-5-1024x743.png" alt="" width="838" height="608" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-5.png?resize=200%2C145&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-5.png?resize=300%2C218&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-5.png?resize=400%2C290&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-5.png?resize=600%2C436&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-5.png?resize=768%2C557&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-5.png?resize=800%2C581&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-5.png?resize=1024%2C743&amp;ssl=1 1024w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-5.png?resize=1200%2C871&amp;ssl=1 1200w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-5.png?fit=1430%2C1038&amp;ssl=1 1430w" sizes="(max-width: 838px) 100vw, 838px" />
ChatGPT data formatting for easy reference
<img decoding="async" class="alignnone wp-image-3001 " src="https://insigai.io/wp-content/uploads/2023/03/Picture-6-1024x774.png" alt="" width="503" height="380" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-6.png?resize=200%2C151&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-6.png?resize=300%2C227&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-6.png?resize=400%2C302&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-6.png?resize=600%2C453&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-6.png?resize=768%2C580&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-6.png?resize=800%2C604&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-6.png?resize=1024%2C774&amp;ssl=1 1024w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-6.png?fit=1063%2C803&amp;ssl=1 1063w" sizes="(max-width: 503px) 100vw, 503px" />
From a productivity perspective, GenAI will help remove many of the mundane tasks required to surface and make sense of the data you care about most.
These examples still only scratch the surface of what is possible. With regulation starting to bite on requirements around Article 8 and 9 funds, tracking the SFDR Principal Adverse Impacts (PIA) indicators is front of mind for many of our clients.
“Summarise the key SFDR disclosures that talk about activities negatively affecting biodiversity sensitive sites. Return the results in a table that can be exported as a .CSV.”
Insig.AI search quickly narrows core analysis data set<img decoding="async" class="alignnone wp-image-3000 " src="https://insigai.io/wp-content/uploads/2023/03/Picture-7.png" alt="" width="829" height="494" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-7.png?resize=200%2C119&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-7.png?resize=300%2C179&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-7.png?resize=400%2C238&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-7.png?resize=600%2C358&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-7.png?resize=768%2C458&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-7.png?resize=800%2C477&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-7.png?fit=852%2C508&amp;ssl=1 852w" sizes="(max-width: 829px) 100vw, 829px" />
ChatGPT smart summarizing and data formatting
<img decoding="async" class="alignnone wp-image-2999" src="https://insigai.io/wp-content/uploads/2023/03/Picture-8.png" alt="" width="490" height="294" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-8.png?resize=200%2C120&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-8.png?resize=300%2C180&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-8.png?resize=400%2C240&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-8.png?resize=600%2C360&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-8.png?resize=768%2C461&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-8.png?resize=800%2C480&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2023/03/Picture-8.png?fit=836%2C502&amp;ssl=1 836w" sizes="(max-width: 490px) 100vw, 490px" />
<h3>How to make ChatGPT work for you</h3>
There are a few fundamental points to consider to when using ChatGPT, especially if you want to use it at scale:
The quality of the question matters:  You pay for every token (word) that the machine reads. If you ask a dumb question, you will get a dumb answer…and pay for it!
Being suitably skilled to interpret an AI generated answer:  If you have zero domain expertise on your topic of choice, you won’t be able to differentiate a good response from a bad one.
ChatGPT does not currently access a real-time source of up-to-date information:  Having an accessible repository of machine-readable data will allow you to interrogate and surface current content most relevant to you.
Knowing how to integrate this new technology into your current workflow:  To use ChatGPT at scale and affordably requires experienced DevOps engineers and extensive cloud expertise.
These points will ultimately separate ‘hobby’ users of ChatGPT from those who can truly take business advantage of it.
For Insig.AI, we see the advancement of this type of text-based analysis being a perfect overlay to our vast repository of machine-readable ESG and corporate disclosure data.
Combining the deep contextual understanding that ChatGPT brings with our highly focused ESG NLP classifiers will be differential in surfacing the most relevant ESG disclosures to our clients and increase analysis potential and productivity.

Generative-AI

Generative AI: The game-changing access to advanced technology

<h3>The Brief</h3>
<ul>
<li>Build a proprietary ESG risk scoring model for debt instruments for a portfolio of private entities.</li>
<li>Combine ESG scores from a major ratings agency with alternative ESG data available for private entities not covered by the ratings agency.</li>
<li>Support the build of an investment manager’s portfolio that beats the benchmark index’s ESG score while delivering competitive returns.</li>
<li>Apply data science, AI and optimisation techniques to construct efficient portfolios.</li>
<li>Deliver a market-leading solution, the first of its kind, fully auditable that attracts capital.</li>
</ul>
<h3>Approach to ESG risk</h3>
Every organisation is exposed to ESG risk by the nature and scale of their operations.  Companies can mitigate this risk to avoid material risk.  To assess if ESG risk has been mitigated sufficiently, there are two measurable risk components: Exposure and Management.
<ul>
<li>ESG Exposure is fundamentally a factor of the industry and region in which the company operates, and is based on a system of materiality.</li>
<li>ESG Management is company-specific and assessed through analysing a company’s risk awareness and mitigation, gathered primarily from evidence published in reports.</li>
<li>These two components can be combined to reflect proportionality and produce a single ESG Risk Score that can be integrated into investment decision-making.</li>
</ul>
<h3>When data is lacking</h3>
When modelling company risk, ESG Exposure can to a reasonable degree be estimated by its industry average as it is a factor of operations, size and location.  So, in cases where a company is not covered by a ratings agency, a score can be estimated as the best available technique.  This approach is less reliable for assessing ESG Management, where companies diverge greatly in their approaches to addressing risk exposure. 
Therefore, Insig AI integrated alternative sources of ESG data to assess ESG Management.  Our data compliments the ESG ratings agency score and enhances the industry average scores for companies not covered by the ratings agency.
<h3>Part 1:  Building an ESG Exposure scoring model</h3>
<h4>Re-build an ESG Exposure scoring model from scratch in line with client’s risk methodology.</h4>
<ol>
<li>Disaggregate, re-build and re-weight elements of the ESG ratings agency data into a new scoring model to give the manager greater control over risk factors at a more granular level.</li>
<li>Forensic entity mapping between data providers which use different identifiers and formats, including MSCI, Capital IQ and S&amp;P to improve data reliability.</li>
<li>Implement ESG risk factors specific to debt instruments such as loan maturity in line with investment manager’s proprietary methodology.</li>
</ol>
<h3>Part 2:  Developing a new ESG Management score</h3>
<h4>Create an innovative ESG Management scoring methodology based on company disclosures that is appropriate for entities not covered by the ESG ratings agency.</h4>
<ol>
<li>Collect, tag and validate all Annual Reports and ESG/Sustainability Reports published on portfolio and benchmark fund companies’ websites since 2019.</li>
<li>Convert all documents (mostly in .pdf format) to machine readable text. Split text into sentences, tag and store in a cloud-based database.</li>
<li>Apply a ESG issue-specific Natural Language Processing (NLP) models over the text. NLP models are expert-trained to assess sentences for their relevance to ESG risk issues such as climate change, water, human rights and workforce.</li>
<li>NLP models generate an ESG Disclosure metric based on volume of relevant sentences in the company’s disclosures.</li>
<li>This ESG Disclosure metric is mapped and weighted with the ESG ratings agency data to produce the combined ESG Management score.</li>
</ol>
<h3>Part 3: Generating the final ESG Risk Score</h3>
<h4>Combine ESG Exposure and ESG Management into a final ESG Risk Score for the portfolio manager to assess in a web-based application.</h4>
<ol>
<li>ESG Exposure and ESG Management scores combine into a single, company specific ESG Risk score.</li>
<li>The scoring model is first developed with the investment manager, then coded into Python and finally delivered in a web-based application.</li>
<li>Design a graphic front end that allows the investment manager to compare the portfolio against the benchmark and adjust parameters.</li>
<li>The code, methodology and application are fully transparent and auditable and by third</li>
<li>ESG Risk score is integrated with fundamental investment data in Insig AI’s machine-learning portfolio optimisation tool.</li>
</ol>
<h3>Outcomes</h3>
An innovative fund is launched which aims to build integrated ESG portfolios, establish the investment manager as a leader in ESG investment and successfully attract capital to sustainable methodologies.  The model can be applied to the clients&#8217; other debt and equity products.
Get in touch to find out if our approach could be applied to your credit or debt portfolio, and accelerate your ESG investing journey: <a href="mailto:info@insg.ai">info@insg.ai</a>

building-best-practice-esg-risk-scoring-for-private-entities

Building a best practice ESG risk scoring system for private entities

The Insig AI &#8216;UK Financial Services Sector Gender Pay Gap ‘Walk vs Talk’ Report&#8217; uses a combination of technology and manual data validation, to analyse the relationship between pay gap disclosure levels and the reported hourly gender pay gap in the financial services sector and has been featured in:
IFA Magazine: <a style="color: #0000ff;" href="https://ifamagazine.com/article/financial-services-sector-not-walking-the-talk-on-gender-pay-gap/">Link</a>
Markets Media: <a style="color: #0000ff;" href="https://www.marketsmedia.com/financial-services-not-walking-the-talk-on-gender-pay-gap/">Link</a>
Commercial Risk: <a style="color: #0000ff;" href="https://www.commercialriskonline.com/uk-financial-services-in-danger-of-social-washing-gender-pay-gap/">Link</a>
The full report can be found below:
&nbsp;
For UK Large Cap companies in the Financial Services sector, Insig AI’s ESG data specialists asked – &#8220;What is the relationship between the actual Gender Pay Gap, and levels of disclosure around the issue?&#8221;
The data revealed that
<ul>
<li>14 of the 20 FTSE 100 companies in the Financial Services sector currently have a higher than average gender pay gap</li>
<li>Of these, 9 also show higher than average pay gap disclosure levels</li>
<li>Large dispersion and little consistency for the sector on both ‘Walk’ and ‘Talk’</li>
</ul>
Sector trends
We analysed the relationship between pay gap disclosure levels based on keywords and phrases (‘Talk’) and the reported mean hourly gender pay gap % (‘Walk’) for FTSE 100 companies between 2017 and 2021/22 to examine trends (see Appendix E for methodology).
We found that 14 of the 20 FTSE 100 Financial Services companies that disclosed in 2021/22 have higher than average gender pay gaps (‘Walk&#8217;) compared to the FTSE 100.  Of these 14 with high pay gaps, the level of disclosure on the issues (‘Talk’) is split half and half when compared to the FTSE100 average (Appendix A).
This indicates that overall, the sector is talking a lot about the issue, but still struggling to reduce its pay gap in line with other large businesses in the UK (Appendix D).
Companies vary widely when it comes to their relationship between ‘Walk’ vs ‘Talk’ (Appendix C), with some in danger of being called out for ‘social washing’ or on the other hand, lack of engagement.
 
On the agenda since 2017
Since 2017, companies of a certain size in the UK have been required to report their gender pay gap metrics.  The intention is that transparency will drive accountability and action among businesses to address the issue and reduce inequality.
In the 5 years since, Insig AI’s data shows that while ‘Talk’ among the Financial Services sector has more than doubled from an average of 106 mentions to 232, the actual pay gap has reduced from 29.5% to 24.7%, still above the 19% FTSE100 average.
&nbsp;
What GPG means in ESG
The gender pay gap metric is used by investors and ESG ratings agencies as one of the indicators of how a company manages its workforce risk and opportunity as part of ESG assessment.  Research repeatedly shows that an equitable and diverse workforce is ‘good for business’.
The two companies that did not disclose their numbers either to Gov.UK or through their own reporting are opting out based on number of UK sub-entity employees falling below the regulatory threshold of 250.
&nbsp;
Lacking consistency
The research shows that the Financial Services sector has one of the largest ranges between individual companies’ gender pay gaps (‘Walk’) of all sectors, along with the Consumer Discretionary and Consumer Staples sectors.  The data also reveals a high dispersion on levels of Talk, second only to Consumer Staples (Appendix B).
This suggests this peer group of companies are very taking different approaches to the issue, which may impact their reputation through an ESG lens.
&nbsp;
Commentary (Diana Rose, Head of ESG Research)
We know that different sectors have unique profiles when it comes to gender and diversity, bringing legacies and cultures that play out in metrics such as the gender pay gap. We also know this isn’t simple, and absolutely not about quick fixes.
But, it has been 5 years since regulation made it crystal clear to corporates that their stakeholders care about the gender pay gap, and want to see considered, committed, courageous action.
The FTSE 100 data appears to be moving in the right direction, but it seems the finance industry still has a long way to go.  There’s wide variety in how companies are tackling the issue.
When there is all the ‘Talk’ and the needle still isn’t moving on the ‘Walk’, then businesses must be called out as this can be evidence of ‘social washing’.  At the same time, some firms are lagging not only on bringing the ‘Walk’ % metric down, but also when it comes to ‘Talk’, which is just as concerning.
Suffragette Emily Davison famously said, ‘deeds, not words’. But words still matter here, as they wrap around the deed with the ‘why’, the ‘what’ and ‘how’.
The Government wants the metrics, but also encourages companies to disclose their strategies and action plans to address the pay gap, wherever it is.  Transparency through disclosure is essential to give context, clarity and accountability around the number, which can never tell the full story by itself.
From an investor’s perspective, if a finance company still isn’t engaging on gender diversity in 2022, then questions have to be raised about how well it is managing other workforce issues that will limit its talent pool, skills retention, diversity of thought, reputation and resilience (<a href="https://hbr.org/2018/07/the-other-diversity-dividend">https://hbr.org/2018/07/the-other-diversity-dividend</a>).
Gender is fundamental, but just the start; other diversity factors such as ethnicity, age, disability, and education that are not only fundamental to an equitable society, but important for building a brilliant and future-fit workforce.
&nbsp;
Appendix A
Scatter graph of all FTSE 100 companies by ‘Walk’ and ‘Talk’, divided in 4 groups in relation to the averages.
<a href="https://insigai.io/?attachment_id=2919" rel="attachment wp-att-2919"><img decoding="async" class="alignnone size-full wp-image-2919" src="https://insigai.io/wp-content/uploads/2022/11/Insig-1.jpg" alt="" width="903" height="665" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-1.jpg?resize=200%2C147&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-1.jpg?resize=300%2C221&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-1.jpg?resize=400%2C295&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-1.jpg?resize=600%2C442&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-1.jpg?resize=768%2C566&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-1.jpg?resize=800%2C589&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-1.jpg?fit=903%2C665&amp;ssl=1 903w" sizes="(max-width: 903px) 100vw, 903px" /></a>
&nbsp;
Table of FTSE 100 Financial Services companies, divided into 4 groups in relation to FTSE 100 average.
<a href="https://insigai.io/?attachment_id=2920" rel="attachment wp-att-2920"><img decoding="async" class="alignnone size-full wp-image-2920" src="https://insigai.io/wp-content/uploads/2022/11/Insig-2.jpg" alt="" width="797" height="279" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-2.jpg?resize=200%2C70&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-2.jpg?resize=300%2C105&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-2.jpg?resize=400%2C140&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-2.jpg?resize=600%2C210&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-2.jpg?resize=768%2C269&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-2.jpg?fit=797%2C279&amp;ssl=1 797w" sizes="(max-width: 797px) 100vw, 797px" /></a>
&nbsp;
Table of FTSE 100 Financial Services sector companies’ ‘Walk’ and ‘Talk’ data, 2021/22.
 <a href="https://insigai.io/?attachment_id=2921" rel="attachment wp-att-2921"><img decoding="async" class="alignnone size-full wp-image-2921" src="https://insigai.io/wp-content/uploads/2022/11/Insig-3.jpg" alt="" width="663" height="586" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-3.jpg?resize=200%2C177&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-3.jpg?resize=300%2C265&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-3.jpg?resize=400%2C354&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-3.jpg?resize=600%2C530&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-3.jpg?fit=663%2C586&amp;ssl=1 663w" sizes="(max-width: 663px) 100vw, 663px" /></a>
&nbsp;
Appendix B
Range of gender pay gap metric by sector (‘Walk’), 2021/22
<a href="https://insigai.io/?attachment_id=2922" rel="attachment wp-att-2922"><img decoding="async" class="alignnone size-full wp-image-2922" src="https://insigai.io/wp-content/uploads/2022/11/Insig-4.png" alt="" width="1379" height="776" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-4.png?resize=200%2C113&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-4.png?resize=300%2C169&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-4.png?resize=400%2C225&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-4.png?resize=600%2C338&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-4.png?resize=768%2C432&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-4.png?resize=800%2C450&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-4.png?resize=1024%2C576&amp;ssl=1 1024w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-4.png?resize=1200%2C675&amp;ssl=1 1200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-4.png?fit=1379%2C776&amp;ssl=1 1379w" sizes="(max-width: 1379px) 100vw, 1379px" /></a>
&nbsp;
Range of pay gap disclosure levels by sector (‘Talk’), 2021/22
<a href="https://insigai.io/?attachment_id=2923" rel="attachment wp-att-2923"><img decoding="async" class="alignnone size-full wp-image-2923" src="https://insigai.io/wp-content/uploads/2022/11/Insig-5.jpg" alt="" width="1379" height="782" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-5.jpg?resize=200%2C113&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-5.jpg?resize=300%2C170&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-5.jpg?resize=400%2C227&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-5.jpg?resize=600%2C340&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-5.jpg?resize=768%2C436&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-5.jpg?resize=800%2C454&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-5.jpg?resize=1024%2C581&amp;ssl=1 1024w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-5.jpg?resize=1200%2C680&amp;ssl=1 1200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-5.jpg?fit=1379%2C782&amp;ssl=1 1379w" sizes="(max-width: 1379px) 100vw, 1379px" /></a>
How the read the chart
The Box and Whisker chart shows the range of disclosure counts within a sector.  All values are ranked from smallest to largest.
&nbsp;
Appendix C
Scatter graph of all FTSE 100 companies in scope by Walk, Talk, and market cap, 2021/22
<a href="https://insigai.io/?attachment_id=2924" rel="attachment wp-att-2924"><img decoding="async" class="alignnone size-full wp-image-2924" src="https://insigai.io/wp-content/uploads/2022/11/Insig-6.jpg" alt="" width="1233" height="871" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-6.jpg?resize=200%2C141&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-6.jpg?resize=300%2C212&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-6.jpg?resize=400%2C283&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-6.jpg?resize=600%2C424&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-6.jpg?resize=768%2C543&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-6.jpg?resize=800%2C565&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-6.jpg?resize=1024%2C723&amp;ssl=1 1024w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-6.jpg?resize=1200%2C848&amp;ssl=1 1200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-6.jpg?fit=1233%2C871&amp;ssl=1 1233w" sizes="(max-width: 1233px) 100vw, 1233px" /></a>
&nbsp;
Appendix D
Sector averages for the FTSE 100 for ‘Walk’ and ‘Talk’, 2021/22
<a href="https://insigai.io/?attachment_id=2925" rel="attachment wp-att-2925"><img decoding="async" class="alignnone size-full wp-image-2925" src="https://insigai.io/wp-content/uploads/2022/11/Insig-7.jpg" alt="" width="903" height="555" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-7.jpg?resize=200%2C123&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-7.jpg?resize=300%2C184&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-7.jpg?resize=400%2C246&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-7.jpg?resize=600%2C369&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-7.jpg?resize=768%2C472&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-7.jpg?resize=800%2C492&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/11/Insig-7.jpg?fit=903%2C555&amp;ssl=1 903w" sizes="(max-width: 903px) 100vw, 903px" /></a>
&nbsp;
Appendix E 
Insig AI Methodology
Scope
We have analysed the companies that make up the UK FTSE 100 (as at May 2022) and collected data from 2018-2022.
&nbsp;
Data sources – quantitative and qualitative
<ol>
<li>Quantitative (‘Walk’)</li>
</ol>
We use the publicly available gender pay gap metrics as reported by each company to the UK Government (https://gender-pay-gap.service.gov.uk/).  This is a requirement for all companies in the UK with over 250 employees.
Eight pay gap metrics are reported, and we have chosen to use the mean pay gap based on hourly pay, which is explained on the website as providing a ‘good overall indication’ of the gap.
Where companies do not report on the Gov.UK platform, the metric is collected manually by searching for relevant pay gap reports in the public domain.
The gender pay gap is expressed as a % and referred to as ‘pay gap metric’ or ‘Walk’.
&nbsp;
<ol>
<li>Qualitative (‘Talk’)</li>
</ol>
We measure the volume of disclosure around pay gap terms and phrases, counted across each company’s body of corporate disclosures as published as PDFs on their websites.
The documents in scope include Annual Reports, Sustainability and ESG Reports, Diversity and Inclusion Reports, and Pay Gap Reports, as well as other financial and ESG related documents.  These are manually collected, tagged and validated by our analysts.
PDF documents are converted to machine readable text which is run through a search technology that returns counts of exact matches per document. These counts then summed to produce a total per company per year on which the analysis is based; referred to ‘pay gap disclosure or ‘Talk’.
The terms/phrases counted are as follows and are designed to capture how much a company is talking about these issues.  They are based on established and progressive ESG framework metrics:
<table>
<tbody>
<tr>
<td width="190">Gender related</td>
<td width="187">Generic talk</td>
<td width="190">Other diversity factors / emerging metrics</td>
</tr>
<tr>
<td width="190">Gender pay gap
Gender balance
Female average pay
Male average pay
Equal pay
Gender bonus gap
Gender diversity
Women on the board
Gender representation
Board representation
Bonus gap</td>
<td width="187">Inclusion
Diversity
Equality
Fair pay
Work life balance
Inclusive recruitment
Black Lives Matter
LGBTQ</td>
<td width="190">Living wage
Ethnicity pay gap
Diversity pay gap
Retention rate
Parental leave
Return to work rate
Age representation
Diversity representation
Diversity and inclusion training</td>
</tr>
</tbody>
</table>
&nbsp;
NOTE: We use a combined 2021/22 pay gap quantitative metric which takes the data point most recently available.  This is due to companies publishing at different times.  If a company’s 2021 or 2022 metric cannot be sourced, the company is excluded from the analysis.

uk-financial-services-sector-gender-pay-gap-walk-vs-talk-report

UK Financial Services Sector Gender Pay Gap ‘Walk vs Talk’ Report

ESG is creating data nightmare for companies and investors alike
In 2015, FTSE 100 companies published an average of 8 corporate disclosure  documents a year. Fast-forward to 2022, and the same companies are now publishing 13 documents a year!
There are now over 60 different document types being published, of which more than 50 are ESG-specific. The onus on companies to disclose ever more ESG information is creating a data nightmare, for themselves and their investors.
&nbsp;
If ever there’s time to standardize ESG reporting, it’s now..
One of the core principles behind ESG reporting is transparency. As it stands, it appears that companies are doing their best to make their ESG disclosures available and accessible, but with no formal reporting standard, it’s a free-for-all.
There are no agreed boundaries on when,where, how and in what format to publish, let alone style vs content.The challenges this creates for anyone trying to evaluate ESG disclosures are that it’s onerous, manual, unpredictable and can feel like an endless task. This is leading investors to the reliance on the assessment of an ESG ratings agency, which creates its own issues of subjectivity and lack of transparency.
With the onus on business to get ESG ‘right’, it’s just as difficult for companies to know what to report on and how. ESG is a growing concept, but it remains immature, with a lot of conflicting views and different approaches being suggested every year. Creating a one-size-fits-all reporting guide is difficult, because of a range of factors such as company size, group structure, and the materiality of ESG issues being so different across sectors.
As a result, the disclosure of non-financial information by companies often feels like guess work. Some companies choose to include an ESG section in their Annual Report, some choose to prepare a separate Sustainability Report, or create an Integrated Report that contains financials and non-financials combined. In some cases, companies publish up to a dozen reports each year, often duplicating content across their documents.
There is also confusion around accessibility of documents once published. A great example is the CDP platform. Corporates are tasked with completing very detailed disclosures to the CDP and the content is uploaded to the CDP platform. From a corporate perspective they have done their job, but the CDP charges for access to this data. Information that’s assumed to be ‘published’ may not in reality be available to investment decision-makers.
&nbsp;
Lost in Translation
When looking at the company’s own publishing platform, their websites, the work put into gathering and collating ESG disclosure can be “lost in translation” when the website isn’t built with the accessibility of content in mind.
It’s sometimes obvious that websites are built to prioritise design and flash over usability. The use of moving parts and appealing visuals often gets in the way of finding the actual content therein. By being really sophisticated and snazzy, the websites end up being confusing, inconsistent, sometimes, built like a maze of links and pages that refer and redirect to each other. The issues are often exacerbated with big consumer companies, with websites geared to attract customers as well as provide information. Those often end up being content black holes.
Another issue we come across is that published documents can disappear as soon as the company decides to remodel their website, change domain, or even just update the content. There are often clues about historical reports peppered around company websites, like press releases detailing that a company has published their first Integrated Report, only to offer the reader a broken link.
&nbsp;
Out of Sight 
We also commonly find a large amount of ESG information, such as privacy policies, published on company websites as a web page in html format, with no option to download the content. There is a danger in trusting that information, as web pages tend not to be dated, and they can easily be retroactively amended, or even deleted.
Companies tend only to surface their latest reports clearly on their websites. Of course, the latest Sustainability Report is the most relevant, and as such, is promoted by the company. This can make finding previous years difficult, or even impossible. There are cases where the new report replaces the previous one on the relevant webpage, even sometimes using the same url. In these cases, a user may assume that the previous report never existed, though that wouldn&#8217;t be accurate.
Anyone looking for information on a specific ESG issue faces additional hurdles given there isn’t a universal format that companies follow to disclose non-financial information. Someone interested in water metrics for example could hope to find the data in any number of reports. Has the company decided to include the information as part of their Annual Report? Or have they published a Sustainability Report which should include water related disclosures? Did they opt for the publication of a more specific Water Report? Released an annual ESG Data document?
The issue with a lack of consistent format also means there isn’t consistent titling across companies or even years. It’s sometimes difficult to know what document includes what information. Companies themselves often change how they report year on year, making tracking targets and performance difficult.
&nbsp;
Neverending Story 
The painful reality for anyone looking to make decisions on this information is that it feels  impossible to know for certain that you have all the relevant information until you’d explored the entirety of the website, downloaded all the reports you’d found and then read them, or at least looked at their contents table, assuming they provided one.
For our team of expert collectors, it takes an average of 45 minutes per company to gather the relevant documents from a website. This is before they’ve been organised, usefully categorised and tagged so that the all important analysis on the content can start.
&nbsp;
ESG v2.0
Having gone through the tough process of uncovering yet one data disclosure barrier after another to sound ESG reporting and research, we have harnessed expertise and technology to help dispel the nightmare of analysing ESG data to support the next generation of ESG, whatever that may look like.
Our platform provides users with a central repository of company ESG disclosures, categorised by report type and ESG issue. Using machine learning, each report is split into sentences and passed through AI models. Each sentence is scored against 15 issue-specific classifiers, so users can filter and surface relevant sentences out of the noise.
&nbsp;
APPENDIX<a href="https://insigai.io/?attachment_id=2899" rel="attachment wp-att-2899"><img decoding="async" class="wp-image-2899 aligncenter" src="https://insigai.io/wp-content/uploads/2022/10/Insig-Table.jpg" alt="" width="687" height="87" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2022/10/Insig-Table.jpg?resize=200%2C25&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/10/Insig-Table.jpg?resize=300%2C38&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/10/Insig-Table.jpg?resize=400%2C51&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/10/Insig-Table.jpg?resize=600%2C76&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/10/Insig-Table.jpg?fit=624%2C79&amp;ssl=1 624w" sizes="(max-width: 687px) 100vw, 687px" /></a>
&nbsp;
Report Types &#8211; 60 distinct report types
<table style="width: 97.75%; height: 1155px;" width="624">
<tbody>
<tr style="height: 53px;">
<td style="width: 209.219px; height: 53px;">
20-F
</td>
<td style="width: 203.672px; height: 53px;">Financial Statements</td>
<td style="width: 189.109px; height: 53px;">Privacy Policy</td>
</tr>
<tr style="height: 58px;">
<td style="width: 209.219px; height: 58px;">Annual report</td>
<td style="width: 203.672px; height: 58px;">Gender Pay Gap Report</td>
<td style="width: 189.109px; height: 58px;">Quarterly Results</td>
</tr>
<tr style="height: 58px;">
<td style="width: 209.219px; height: 58px;">Anti-Bribery and Corruption Policy</td>
<td style="width: 203.672px; height: 58px;">Governance Guidelines</td>
<td style="width: 189.109px; height: 58px;">Remuneration Policy</td>
</tr>
<tr style="height: 58px;">
<td style="width: 209.219px; height: 58px;">Audit and Risk Report</td>
<td style="width: 203.672px; height: 58px;">Governance Report</td>
<td style="width: 189.109px; height: 58px;">Remuneration Report</td>
</tr>
<tr style="height: 58px;">
<td style="width: 209.219px; height: 58px;">Climate Change Policy</td>
<td style="width: 203.672px; height: 58px;">Half-year Report</td>
<td style="width: 189.109px; height: 58px;">Social Impact Report</td>
</tr>
<tr style="height: 58px;">
<td style="width: 209.219px; height: 58px;">Climate Change Report</td>
<td style="width: 203.672px; height: 58px;">Health and Safety Policy</td>
<td style="width: 189.109px; height: 58px;">Sourcing/Supply Chain Policy</td>
</tr>
<tr style="height: 58px;">
<td style="width: 209.219px; height: 58px;">Code of Conduct</td>
<td style="width: 203.672px; height: 58px;">Human Rights Policy</td>
<td style="width: 189.109px; height: 58px;">Sourcing/Supply Chain Report</td>
</tr>
<tr style="height: 58px;">
<td style="width: 209.219px; height: 58px;">Code of Ethics</td>
<td style="width: 203.672px; height: 58px;">Human Rights Report</td>
<td style="width: 189.109px; height: 58px;">Stakeholder Report</td>
</tr>
<tr style="height: 58px;">
<td style="width: 209.219px; height: 58px;">Community Relations Policy</td>
<td style="width: 203.672px; height: 58px;">Integrated Report</td>
<td style="width: 189.109px; height: 58px;">Supplier Code of Conduct</td>
</tr>
<tr style="height: 58px;">
<td style="width: 209.219px; height: 58px;">Consumer Policy</td>
<td style="width: 203.672px; height: 58px;">Modern Slavery Statement</td>
<td style="width: 189.109px; height: 58px;">Sustainability Policy</td>
</tr>
<tr style="height: 58px;">
<td style="width: 209.219px; height: 58px;">Diversity Report</td>
<td style="width: 203.672px; height: 58px;">Other Environmental Document</td>
<td style="width: 189.109px; height: 58px;">Sustainability Report</td>
</tr>
<tr style="height: 58px;">
<td style="width: 209.219px; height: 58px;">Diversity Statement</td>
<td style="width: 203.672px; height: 58px;">Other Environmental Report</td>
<td style="width: 189.109px; height: 58px;">Sustainability Report Annex</td>
</tr>
<tr style="height: 58px;">
<td style="width: 209.219px; height: 58px;">Earnings Call</td>
<td style="width: 203.672px; height: 58px;">Other Financial Document</td>
<td style="width: 189.109px; height: 58px;">Sustainability Whitepaper</td>
</tr>
<tr style="height: 58px;">
<td style="width: 209.219px; height: 58px;">Ecology Policy</td>
<td style="width: 203.672px; height: 58px;">Other Governance Document</td>
<td style="width: 189.109px; height: 58px;">Tax Policy</td>
</tr>
<tr style="height: 58px;">
<td style="width: 209.219px; height: 58px;">Ecology Report</td>
<td style="width: 203.672px; height: 58px;">Other Governance Report</td>
<td style="width: 189.109px; height: 58px;">Tax Report</td>
</tr>
<tr style="height: 58px;">
<td style="width: 209.219px; height: 58px;">Employment Policy</td>
<td style="width: 203.672px; height: 58px;">Other Social Document</td>
<td style="width: 189.109px; height: 58px;">Waste Report</td>
</tr>
<tr style="height: 58px;">
<td style="width: 209.219px; height: 58px;">Environmental Policy</td>
<td style="width: 203.672px; height: 58px;">Other Social Report</td>
<td style="width: 189.109px; height: 58px;">Water Policy</td>
</tr>
<tr style="height: 58px;">
<td style="width: 209.219px; height: 58px;">Environmental Report</td>
<td style="width: 203.672px; height: 58px;">Other Sustainability Document</td>
<td style="width: 189.109px; height: 58px;">Water Report</td>
</tr>
<tr style="height: 58px;">
<td style="width: 209.219px; height: 58px;">ESG Data</td>
<td style="width: 203.672px; height: 58px;">Palm Oil Policy</td>
<td style="width: 189.109px; height: 58px;">Whitepaper (Environment)</td>
</tr>
<tr style="height: 58px;">
<td style="width: 209.219px; height: 58px;">ESG Report</td>
<td style="width: 203.672px; height: 58px;">Payments to Government Report</td>
<td style="width: 189.109px; height: 58px;">Wood and Pulp Policy</td>
</tr>
</tbody>
</table>

ESG-is-creating-data-nightmare-for-companies-and-investors-alike

ESG is creating data nightmare for companies and investors alike

esg-marketing-video-2

Video: Don’t spend hundreds of hours drowning in reports. Get everything at your fingertips.

esg-marketing-video-1

Video: ESG disclosures have exploded. But we’ve got you covered with everything at your fingertips.

According to our data, 70 per cent of the UK&#8217;s blue-chip firms were not meeting expectations on climate related disclosures. You can read our ESG Research Director, Diana Rose&#8217;s thoughts on this in the latest This is Money article below.
&nbsp;
<a href="https://www.thisismoney.co.uk/money/markets/article-11089707/Grocers-Tesco-Ocado-falling-short-green-promises.html">https://www.thisismoney.co.uk/money/markets/article-11089707/Grocers-Tesco-Ocado-falling-short-green-promises.html</a>

household-names-from-tesco-to-ocado-failing-to-give-enough-information-about-how-they-will-survive-in-a-greener-economy-

Household names from Tesco to Ocado failing to give enough information about how they will survive in a 'greener' economy

<a href="https://ifamagazine.com/article/lcaw2022-balancing-the-net-zero-ambition-with-the-greenwashing-tightrope-diana-rose/">https://ifamagazine.com/article/lcaw2022-balancing-the-net-zero-ambition-with-the-greenwashing-tightrope-diana-rose/</a>
Our ESG Research Director Diana Rose was quoted in IFA Magazine regarding her thoughts on the 4th annual London Climate Action Week and the increasing role of finance in achieving our net zero ambitions in the UK.
You can read the article in full using the link above.

balancing-the-net-zero-ambition-with-the-greenwashing-tightrope

Balancing the net-zero ambition with the greenwashing tightrope

ESG Research Director Diana Rose recently spoke with Elric Langton of Small Company Champion.
In his editorial for ShareScope, Elric looked at how ESG will impact lives, investments, and Corporates’ role in the implementation and consequences of attempts to bypass ESG. He also looked at how Insig AI is helping fund managers navigate and build wealth through the use of AI.
You can read Elric’s insights and Diana’s comments here:
<a href="https://knowledge.sharescope.co.uk/2022/06/29/how-important-is-esg-to-the-global-financial-institutions-and-global-business-and-are-we-ready/">https://knowledge.sharescope.co.uk/2022/06/29/how-important-is-esg-to-the-global-financial-institutions-and-global-business-and-are-we-ready/</a>

How-important-is-ESG-to-the-global-financial-institutions-and-global-business-and-are-we-ready

How important is ESG to the global financial institutions, and global business and are we ready?

tesla-kicked-out-of-s&p-ESG-index

Tesla kicked out of S&P’s ESG Index: What just happened

This case may be the first of its kind, but we all know it won&#8217;t be the last.  The SEC&#8217;s greenwashing taskforce is on a mission and this will be a wake up call to fund managers.  This particular case is around the claim that all stocks went through an ESG quality review as part of its investment process, which they apparently didn&#8217;t.
Overstating the ESG qualities of a fund to attract capital (ie greenwashing) is a very real problem which risks damaging not only the credibility of ESG, but its potential to actually get capital flowing in ways that will benefit society and help make the planet liveable for our children.
However, I must admit I don&#8217;t envy the regulators trying to police ESG space.  It often feels like it&#8217;s going through the tricky teenage years and right now they&#8217;re having a pretty wild party.  What is an &#8216;ESG quality review&#8217; in the first place?  Every fund will take a different approach to this process, but it&#8217;s guaranteed there will be subjectivity and maybe even smoke and mirrors.  What can bring integrity back to ESG funds, (aside from telling the truth about whether or not these quality reviews are being done at all) is oversight and transparency on the process; consistency and due diligence in the method.
Regulation is coming down the pipeline soon to crack down on the freeloaders at the party.
At Insig AI, we speak to a lot of asset managers anticipating regulation and striving for best practice.  They talk about the time pressures to turn around ESG analysis, and costs and expertise overheads of doing the research they want to on stocks as limiting factors.  This is why we&#8217;ve designed AI tools to take the pain out of doing due diligence on your portfolio&#8217;s public disclosures on ESG with transparency and consistency &#8211; to bring integrity to the ESG review process so that our clients aren&#8217;t looking over their shoulder for the greenwashing police.

sec-issues-first-greenwashing-fine-to-an-investment-adviser

SEC issues first 'greenwashing' fine to an investment adviser

Insig AI&#8217;s ESG Research Director <a href="https://www.linkedin.com/in/ACoAAAEN8ukB24Qgb2FV7o0onAJyWf9meMTRnpw" data-attribute-index="0" data-entity-hovercard-id="urn:li:fs_miniProfile:ACoAAAEN8ukB24Qgb2FV7o0onAJyWf9meMTRnpw" data-entity-type="MINI_PROFILE">Diana Rose</a> was quoted in the recent Financial Times Sustainable Views article regarding the need for increased transparency on ESG ratings.
You can read the full article here:
<a href="https://www.sustainableviews.com/sustainable-companies-are-more-expensive-says-study/">https://www.sustainableviews.com/sustainable-companies-are-more-expensive-says-study/</a>

sustainable-companies-are-more-expensive-says-study

Sustainable companies are more expensive, says study

How can you tell if a company is not only talking the talk but also walking the walk when it comes to ESG? in this article, our ESG Research Director Diana Rose sets out how Artificial Intelligence can be the key in figuring who isn&#8217;t keeping their promises.
You can read the full Computer Weekly article here:
<a href="https://www.computerweekly.com/blog/Green-Tech/Using-AI-to-discern-if-enterprises-are-walking-the-talk-on-ESG">https://www.computerweekly.com/blog/Green-Tech/Using-AI-to-discern-if-enterprises-are-walking-the-talk-on-ESG</a>
&nbsp;

using-ai-to-discern-if-enterprises-are-walking-the-talk-on-esg

Using AI to discern if enterprises are walking the talk on ESG

Between 2015 and 2020, the amount of ESG disclosures quadrupled, but we are by no means at a peak. 
You can read our ESG Research Director Diana Rose&#8217;s thoughts on what the future of ESG disclosure might look like in the latest Pensions Expert blog using the link below.
You can read the article in full here:
<a href="https://www.pensions-expert.com/Law-Regulation/What-does-the-future-of-ESG-disclosure-look-like?ct=true">https://www.pensions-expert.com/Law-Regulation/What-does-the-future-of-ESG-disclosure-look-like?ct=true</a>
&nbsp;

what-does-esg-disclosure-look-like

What does the future of ESG disclosure look like?

The &#8216;G&#8217; in ESG is widely known as governance, but what exactly is governance? And how can it be used to ensure better long-term performance in a company?
You can listen to our ESG Research Director <a title="https://www.linkedin.com/in/ACoAAAEN8ukB24Qgb2FV7o0onAJyWf9meMTRnpw" href="https://www.linkedin.com/in/ACoAAAEN8ukB24Qgb2FV7o0onAJyWf9meMTRnpw">Diana Rose</a> discuss governance on the latest <a title="https://www.linkedin.com/company/financial-times/" href="https://www.linkedin.com/company/financial-times/">Financial Times</a> Adviser Podcast using the link below:
<a href="https://podcasts.apple.com/gb/podcast/exploring-governance-the-g-in-esg/id1437151302?i=1000556289364">https://podcasts.apple.com/gb/podcast/exploring-governance-the-g-in-esg/id1437151302?i=1000556289364</a>

what-exactly-is-governance

What exactly is governance?

<h2>TCFD: Task Force on Climate-related Financial Disclosure</h2>
One could argue that this four-letter acronym is the start of the green revolution for the corporate reporting world. With TCFD reporting now mandatory from 6 April 2022 for over 1,300 large corporations with &gt;500 employees and &gt;£500m in annual turnover, the UK is the first of the G20 to make it law, with others set to follow.
One could also argue that the regulatory burden of disclosure and reporting is going to cause a huge amount of pain. Not just for the companies required to report, but every investor required to analyse the mountain of disclosure documents coming their way, in order to substantiate their investment decisions.
The TCFD was established in 2015 and finalised in 2017 to drive the reporting of climate-related financial information in publicly available Annual Reports. Widely applicable across all sectors and jurisdictions, disclosures are around 4 core pillars that demand oversight of climate risk at every level of business: governance, strategy, risk management, and metrics and targets.
There is no question that the merits of TCFD are going to change the way companies operate and report, and will certainly change the way investors evaluate the companies that they buy into. The near-term pain and overhead of the reporting requirement will also hopefully accelerate corporate awareness on their impact on climate and, over time, we all start to see and feel the benefits of a recovering environment.
With the 6th of April looming, the big question is: how many of the major FTSE companies are prepared for the disclosure task ahead of them or risk falling foul of regulation? What’s telling is to see how long a company has been talking about TCFD, and where they publish their disclosures. The point here is that TCFD requires significant lead-in time to implement and requires that climate risk is addressed in the fundamentals of the Annual Report.
<h2>What Does the Data Say?</h2>
Using the Insig.AI database of corporate ESG and financial documents, the goal was to identify trends around TCFD disclosure over time and across report types.
The approach was to use a keyword and phrase search across the entire database on: “TCFD”, “climate related”* and “climate risk”.
COMPANY, TIMEFRAME AND DOCUMENT UNIVERSE 
Companies: FTSE 100 (plus a few other UK names) 
Timeframe: 2015-2021 
No. of documents queried: 3,345 
Document Types: Annual Report, Integrated Report, Sustainability/ESG Report, Climate Change Report, Climate Change Policy, Financial Document, ESG Data
*Searching “climate related” also accurately surfaces “climate-related” which is the TCFD naming convention.
<h2>Summary Observations</h2>
<h3>Who was first to start referencing TCFD?</h3>
Aviva, Barclays, BHP and LSEG were the first FTSE companies to make reference to TCFD back in 2016, before the TCFD recommendations were even finalised.  This indicates foresight and these companies have continued to be leaders in TCFD disclosure over the past 5 years.
<img decoding="async" class="wp-image-2540 size-full aligncenter" src="https://insigai.io/wp-content/uploads/2022/03/esg_tcfd_1.png" alt="" width="904" height="346" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_1.png?resize=200%2C77&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_1.png?resize=300%2C115&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_1.png?resize=400%2C153&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_1.png?resize=600%2C230&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_1.png?resize=768%2C294&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_1.png?resize=800%2C306&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_1.png?fit=904%2C346&amp;ssl=1 904w" sizes="(max-width: 904px) 100vw, 904px" />
<h3>References Over Time</h3>
FTSE companies have been actively referencing “climate related” since 2017 and the trend has continued to grow year on year. Reference to “climate risk” only started to gain traction in 2020. TCFD references have doubled year on year since 2017.
&nbsp;
<img decoding="async" class="wp-image-2541 size-full aligncenter" src="https://insigai.io/wp-content/uploads/2022/03/esg_tcfd_2.png" alt="" width="932" height="592" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_2.png?resize=200%2C127&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_2.png?resize=300%2C191&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_2.png?resize=320%2C202&amp;ssl=1 320w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_2.png?resize=400%2C254&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_2.png?resize=600%2C381&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_2.png?resize=768%2C488&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_2.png?resize=800%2C508&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_2.png?fit=932%2C592&amp;ssl=1 932w" sizes="(max-width: 932px) 100vw, 932px" />
<h3>Leaders and Laggards</h3>
Analysing combined TCFD relevant disclosures over this same period, there are clear and consistent leaders. At the other end of the spectrum, there is a stark difference in level of disclosure.  It raises some questions about the level of commitment and readiness for TCFD reporting come April. Whilst the ‘materiality’ of climate change risk and impact does vary between sectors, which means some industries would start with more established reporting, a principle of the TCFD is that climate change is a risk to all businesses.
 <img decoding="async" class="size-full wp-image-2542 aligncenter" src="https://insigai.io/wp-content/uploads/2022/03/esg_tcfd_3.png" alt="" width="928" height="574" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_3.png?resize=200%2C124&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_3.png?resize=300%2C186&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_3.png?resize=400%2C247&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_3.png?resize=600%2C371&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_3.png?resize=768%2C475&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_3.png?resize=800%2C495&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_3.png?fit=928%2C574&amp;ssl=1 928w" sizes="(max-width: 928px) 100vw, 928px" />
<h3>Disclosure by Document Type</h3>
Taking 2020 as a snapshot of where TCFD disclosures are found across reports, Annual and Climate Change Reports make up just over a third each.  22% are found in Sustainability and ESG Reports.  Companies not yet reporting to the TCFD in their Annual Reports will be required to start in the 2022-23 reporting cycle.
<img decoding="async" class="aligncenter wp-image-2543" src="https://insigai.io/wp-content/uploads/2022/03/esg_tcfd_4-800x720.png" alt="" width="600" height="540" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_4.png?resize=200%2C180&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_4.png?resize=300%2C270&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_4.png?resize=400%2C360&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_4.png?resize=600%2C540&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_4.png?resize=768%2C691&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_4.png?resize=800%2C720&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2022/03/esg_tcfd_4.png?fit=1011%2C910&amp;ssl=1 1011w" sizes="(max-width: 600px) 100vw, 600px" />
&nbsp;
&nbsp;

tcfd-which-ftse-companies-are-ready

TCFD: Which FTSE companies are ready for the 6th of April…and which are not?

what-is-so-hard-about-pdf-text-extraction

What is so hard about PDF text extraction

If you ask any data scientist what they spend most of their time doing, they won’t tell you that it is solving the world&#8217;s hardest problems, or fine tuning an incredible new life changing algorithm, or even predicting the next bitcoin bounce. No- what they would say is, collecting and cleaning data. Period. Once they have done this, they still have to transform it into a format that is machine readable. The reality is that these seemingly mundane tasks are 80% of their day job. In the asset management industry, there is a constant drive to find a trading edge or to find a new signal in a dataset that informs an investment decision. You only need to look at firms like Renaissance, TwoSigma and Citadel to find evidence of advanced data science driven strategies. They have invested millions of dollars into curating their own datasets and building teams to find value in their data. Anyone new to the game, however, has to start from scratch. This is why getting access to transformed, structured, labelled/tagged and machine readable data is so valuable.
<h3 class="MuiTypography-root MuiTypography-h3 css-1inx43p-MuiTypography-root">Initial success</h3>
In late 2020, we were developing a new machine learning model for an asset management client who was looking to identify the strategic direction of a company based on earnings call transcripts and annual reports. We started with the S&amp;P 500 and very quickly were able to get machine readable transcripts from Capital IQ along with companies&#8217; annual reports from the SEC Edgar database. The data was well formatted, tagged, easy to download and there was a lot of history &#8211; a data scientist’s dream! We used a combination of NLP to analyse what was being said in the text, and predictive machine learning algorithms to analyse the company financials. This approach resulted in an ecstatic client and an expanded mandate.
<h3 class="MuiTypography-root MuiTypography-h3 css-1inx43p-MuiTypography-root">The challenge</h3>
Filled with confidence (perhaps even a hint of arrogance) following our recent success, we set out to repeat the exercise on European companies. The earnings call transcripts were again, readily available and these were ingested and processed quickly. Next, we set out to add the European annual reports and filings, expecting to find the same clean dataset, beautifully structured as was held in the Edgar database. Instead, we found ourselves in a data-science nightmare. European companies do not have to report in a uniform, consumable standard, nor is there a central database to access files. They are free to do whatever they want and can publish the content anywhere, whether this be the investor page on their website, to LinkedIn. Put simply, there is no consistency of format, or filing. Given we had promised our client a quick turnaround (based on our first experience), we had a problem.
<h3 class="MuiTypography-root MuiTypography-h3 css-1inx43p-MuiTypography-root">The solution</h3>
In a scramble to find a reliable source of data that would help us avoid having to find and download thousands of PDF documents that we would have to manually clean and tag, we stumbled across an article by a small company called FDB Filings. They had apparently come across the same problem and built a business around it!
&#8211; Comprehensive coverage of European annual, interim &amp; press release reports
&#8211; PDF documents converted into a machine readable JSON format
&#8211; Link back to every original source document
&#8211; Point-in-time entity mapping
&#8211; Clear and well-defined tags on revenue guidance, executive speaker, business risks &amp; more
&#8211; 10 years of filing history
&#8211; Data accessible via an AWS S3 bucket
We set up a trial to test the data, and to our collective delight, the quality of their technology, data structuring and team was outstanding! We were able to ingest and score 10+ years of annual reports for 1,000 European companies, almost immediately and deliver back the results our client was expecting. The team at FDB had set out to solve a very hard data structuring problem and succeeded so much so that In November 2021 Insig AI acquired FDB.
<h3 class="MuiTypography-root MuiTypography-h3 css-1inx43p-MuiTypography-root">What we learned</h3>
Starting from scratch doesn’t necessarily require billions. This process demonstrates that a combination of thoughtful data structuring, good timing, and robust infrastructure, is fertile ground to create an AI-powered analytical platform, that delivers unbiased, uniform, value-add insights to an entire sector, across different markets, in an accessible, and easily digestible format. Financial Institutions are entering an entirely new paradigm, one where stakeholders and shareholders alike are squarely focused on sustainability and accountability. This is bringing a significant layer of complexity and cost to a financial institution. Technology and data companies are emerging with cost effective and transparent data scientific propositions that enhance the due diligence and risk management capabilities of an institution, while informing and improving investment decisions. For the heads of risk and sustainability at financial institutions, the choice of data provider is yours, but the time for acting, is now!

european-machine-readable-company-filings-a-data-scientist's-dream

European machine readable company filings: a data scientist's dream

Read the latest Pensions Expert article to hear the thoughts of our ESG Research Director Diana Rose on whether its time for pension schemes to consider natural capital.
You can read the article in full using the link below:
<a href="https://www.pensions-expert.com/ESG/Is-now-the-time-for-schemes-to-consider-natural-capital?ct=true">https://www.pensions-expert.com/ESG/Is-now-the-time-for-schemes-to-consider-natural-capital?ct=true</a>

is-now-the-time-for-schemes-to-consider-natural-capital

Is now the time for schemes to consider natural capital?

As part of International Women&#8217;s Day 2022, our ESG Research Director Diana Rose talks about the development of her role and her experiences in IFA Magazine #IWD2022
You can read the full article, including Diana&#8217;s top tips for Women starting out in the industry using the link below:
<a href="https://ifamagazine.com/article/iwd2022-women-in-finance-experts-reflect-on-their-experiences/">https://ifamagazine.com/article/iwd2022-women-in-finance-experts-reflect-on-their-experiences/</a>

women-in-finance-experts-reflect-on-their-experiences

Women in finance - experts reflect on their experiences

Our CPO Steven Cracknell spoke to Funds Europe about how there has been a shift away from the AI black box system, but not completely. Stating the difference today is that #AI and machine learning are a lot more accessible.
The full article can be found using the link below:
<a href="https://www.funds-europe.com/december-2021/technology-for-explainable-ai-look-in-the-white-box">https://www.funds-europe.com/december-2021/technology-for-explainable-ai-look-in-the-white-box</a>

technology-for-explainable-ai-look-in-the-white-box

Technology: For explainable AI, look in the white box

We spend a large part of our time working with text data and as a result we often find ourselves implementing some kind of NLP tool either as part of our product or on behalf of customers. One of those tools is Named Entity Recognition (NER) – a process for identifying entities, e.g. people and places, in text. There are a wide range of possible use cases for NER but ours are usually focused on investing where we have used it to augment search tools, automatically connect individuals and companies, and screen huge volumes of text for known bad actors. While we work with NER regularly, and with text data every day, we don’t consider ourselves to be experts in NER specifically – our interest is practical in nature and as such our experience is in implementing other people’s models rather than in developing new models from scratch.
This short piece contains some of the lessons we’ve learned: it’s primarily aimed at the potential NER user without much experience of the technology but we hope there will be something here too for the algorithm developers and vendors who may find it useful to see the experiences of a more narrowly commercially focused organisation.
<h2 class="MuiTypography-root MuiTypography-h2 css-ur62r9-MuiTypography-root">What is NER?</h2>
Before we go any further, for those who aren’t familiar with NER it is roughly speaking the process of going from this:
Elon Musk invites Vladimir Putin to connect on the Clubhouse app.
to this:
(Elon Musk)[PERSON] invites (Vladimir Putin)[PERSON] to connect on the (Clubhouse)[ORGANISATION] app.
As you can see there are basically two components to NER: (1) identifying the strings (i.e. words) which are entities, and (2) identifying the type of entity. The main benefit of this technology is that because it is automated you can process volumes of text that would be completely unfeasible for a human with the result that several tools and applications which were previously impossible become possible.
<h2 class="MuiTypography-root MuiTypography-h2 css-ur62r9-MuiTypography-root">One NER algorithm to rule them all?</h2>
One of the most immediately striking things when you attempt to implement NER in real use cases is that the available solutions are very generic and may not fit that well for your specific use case. One way in which this appears is in the labels used to categorise entities.
The overwhelming majority of solution developers and vendors use 4 categories of entity: people, places, organisations, and miscellaneous. This is mostly for the historical reason that these were the categories used in the CoNLL-2003 shared task which has acted as a touchstone for the industry ever since. Nor are the categories bad or unreasonable – given a task to group entities into four intuitive groups it is likely many people would arrive at something similar.
The problem is that reasonable though those categories may be they don’t necessarily make sense in every use case. In our use cases, for example, we typically don’t care about organisations as such but about companies. The difference being that ‘organisation’ is a broader category which includes things like the U.N. and the FBI.
The practical implication of this is that many off-the-shelf NER models may simply not be that useful for your application and may require some additional work or post-processing steps to deliver what you want. This situation seems to be improving as some more targeted vendors/developers are beginning to offer a wider range of commercially useful categories but given the number of possible entity categories we might want to target we are probably some way off having readily available models for all of them and it may not even be possible. We are also seeing more platforms which offer some ‘train your own’ capabilities and this may be a promising path.
What this touches on is that, practically speaking, it is a little misleading to think of NER as a generic problem with a generic solution. Perhaps one day there will be one algorithm to rule them all but today the best solution for identifying drugs in research papers is unlikely to be the same one as for identifying companies in regulatory filings. It may be more helpful for both consumers and producers to think of NER as a family of solutions to a range of different problems which share some formal characteristics. From a commercial perspective this is no bad thing because it is usually far easier to solve problems in a domain or application specific way than in a general manner as we will see below.
<h2 class="MuiTypography-root MuiTypography-h2 css-ur62r9-MuiTypography-root">What does good look like?</h2>
One of the surprising facts about implementing NER solutions in the wild is not just that it is tricky to work out which solution is best, but that it is also much harder than you might think to establish some objective and general measure of quality against which you can measure them.
If you wanted to know which NER solution was best for you, a reasonable starting place might be the publicly available benchmarking data. Unfortunately, our experience is that these benchmarks have not been especially useful. Any benchmark is only useful if it matches your intended use case in the relevant ways and we have found that the absolute and relative performance of NER algorithms on benchmarks was quite different to the performance on our specific data (mainly company filings and regulatory releases). Further, just as with a CPU or database benchmark, implementation details matter and can contribute significantly to the outcome. Even when the implementation details in a benchmark have been honestly chosen they may not be applicable to your circumstances and again result in the benchmark being unrepresentative.
These issues are really true of all benchmarking processes to a greater or lesser extent, but there are some features of NER that make it difficult even in theory to establish a general and objective measure of quality. Consider an algorithm which returns us the following:
Philip (Seymour Hoffman)[PERSON] was a prolific actor.
It correctly identifies “Seymour Hoffman” as a person, but misses the full name. Should this receive a full point, half point or no point? And what about cases in which the correct string is identified but the wrong label is applied? There just isn’t a right answer here which doesn’t depend on the context and use case – even among the relatively homogenous set of use cases we have encountered it has sometimes made sense to use very strict scoring criteria and at other times to weight it differently.
What this means is that if commercial circumstances allow it is far preferable to have (1) an independent benchmark which reflects the data you will be running the models on and (2) a scoring model which reflects the value of different kinds of partial scores in your use case. This might seem intimidating or impractical but we have found it more achievable than you might expect – we have built test datasets of &gt;90,000 tokens which have been hand labelled multiple times (with additional human reviews) in under a week with a small team. In general for situations where you are considering investing substantial resources into a solution or where you are sensitive to performance differences we have found the ROI to be compelling.
<h2 class="MuiTypography-root MuiTypography-h2 css-ur62r9-MuiTypography-root">Algos in action: YMMV</h2>
You could be forgiven for assuming that all NER solutions would perform about the same – after all they are all using roughly similar methodologies, designed by people with similar skills and experience, and tend to be in the same ballpark on claimed performance. This has not been our experience at all and we have seen very wide differences in performance on our independent benchmarks. For example below is a table showing the F1 scores (this is a value between 0 and 1) of different providers (OS = Open Source, COM = Commercial) in one of our benchmarks and you can see that in a sense the best performing model was almost twice as good as the worst.
<table style="border-collapse: collapse; width: 100%;">
<tbody>
<tr>
<td style="width: 10%;">Provider</td>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 10%;" scope="col">COM1</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 10%;" scope="col">OS1</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 10%;" scope="col">OS2</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 10%;" scope="col">OS3</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 10%;" scope="col">OS4</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 10%;" scope="col">COM2</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 10%;" scope="col">COM3</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 10%;" scope="col">OS6</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 10%;" scope="col">COM4</th>
</tr>
<tr>
<td style="width: 10%;">F1-Score</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 10%;">0.36</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 10%;">0.42</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 10%;">0.44</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 10%;">0.46</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 10%;">0.47</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 10%;">0.54</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 10%;">0.56</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 10%;">0.63</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 10%;">0.68</td>
</tr>
</tbody>
</table>
There are of course other dimensions on which one algorithm can be superior to another including speed (latency and/or throughput), ease-of-use, cost etc. NER algorithms are highly parallelizable so for self hosted solutions the limiting factor is usually going to be the size of the compute grid you have access to and the cost of running it. Here again the differences can be enormous and a range of 100x is not uncommon which, depending on the size of your dataset, may make some options unfeasible.
Another important and often overlooked consideration is that with a vendor API you do not typically have visibility into which model they are using or what changes are being deployed. There are ways to monitor the stability of a vendor algorithm in production but in practice these are cumbersome and not widely used – functionally these are black boxes. Again the exact use case matters, for use cases that are highly sensitive to changes or refinements in the underlying model you may need to self-host, seek guarantees from your vendor, and/or put a monitoring system in place.
A side note on performance is that we have seen some success using post processing steps on the outputs of the models. Because we tend to care about specific data types (in our case usually company filings) we can often achieve meaningful accuracy improvements with simple heuristics which exploit domain specific formal or content based features of our datasets. Below is the same table as above showing F1 scores after some very simple post processing steps were applied. We can’t promise that these results will be replicable in every case but for commercial applications this is certainly worth considering because the datasets being used will often have features that lend themselves to this kind of exploitation – for example, formal/structural characteristics or a tight domain focus.
<table style="border-collapse: collapse; width: 100%;">
<tbody>
<tr class="MuiTableRow-root MuiTableRow-head css-1hsm0p4-MuiTableRow-root">
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 16.7257%;" scope="col">Provider</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 10.708%;" scope="col">COM1</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 7.61062%;" scope="col">OS1</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 7.78761%;" scope="col">OS2</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 7.87611%;" scope="col">OS3</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 7.87611%;" scope="col">OS4</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 11.1504%;" scope="col">COM2</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 11.2389%;" scope="col">COM3</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 7.9646%;" scope="col">OS6</th>
<th class="MuiTableCell-root MuiTableCell-head MuiTableCell-sizeMedium css-g07xji-MuiTableCell-root" style="width: 11.2389%;" scope="col">COM4</th>
</tr>
<tr class="MuiTableRow-root css-1hsm0p4-MuiTableRow-root">
<th class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 16.7257%;">F1-Score</th>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 10.708%;">0.36</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 7.61062%;">0.47</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 7.78761%;">0.49</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 7.87611%;">0.51</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 7.87611%;">0.52</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 11.1504%;">0.61</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 11.2389%;">0.61</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 7.9646%;">0.67</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 11.2389%;">0.75</td>
</tr>
<tr class="MuiTableRow-root css-1hsm0p4-MuiTableRow-root">
<th class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 16.7257%;">Uplift (%)</th>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 10.708%;">0.2</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 7.61062%;">11.2</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 7.78761%;">11.4</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 7.87611%;">11.2</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 7.87611%;">11.3</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 11.1504%;">12.2</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 11.2389%;">9.7</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 7.9646%;">5.2</td>
<td class="MuiTableCell-root MuiTableCell-body MuiTableCell-sizeMedium css-6inj7x-MuiTableCell-root" style="width: 11.2389%;">10.0</td>
</tr>
</tbody>
</table>
<h2 class="MuiTypography-root MuiTypography-h2 css-ur62r9-MuiTypography-root">Takeaways</h2>
As people that work with text data everyday we are excited about the potential role NER technologies can play in a wide range of applications as they improve and evolve. To recap our observations for potential users and developers are:
Many solutions currently available are very generic meaning a little more effort may be required to deliver what you want. A definition of ‘good’ is less obvious than it might seem and benchmarking against your definition is both advisable and surprisingly achievable. In our experience performance varies greatly across different providers and it may be possible to improve results with some post processing steps.

some-observations-from-implementing-named-entity-recognition-algorithms-in-the-real-world

Some observations from implementing Named Entity Recognition algorithms in the real world

why-is-impactful-data-hard-to-find-and-what-it-means-for-the-future

Why is impactful data hard to find and what it means for the future?

In the past few years we have worked with many fundamental investors to help them get value from new data and over that period we’ve seen first-hand the problems funds routinely run into and the frustration that can arise as a result of them. Over time we’ve learnt that many of the problems stem from the same small group of mistakes and that these mistakes can be fixed. In this short article our goal is to describe and to codify two of the most common problems, to explain why they tend to occur, and to suggest some actionable remedies.
<h2 class="MuiTypography-root MuiTypography-h2 css-ur62r9-MuiTypography-root">The incompatibility problem</h2>
<h3 class="MuiTypography-root MuiTypography-h3 css-1inx43p-MuiTypography-root">Problem</h3>
One of the most common problems funds hit when bringing on new data or new data methods is difficulty getting the analysts/PMs to engage. A common pattern is that a data vendor or internal data researcher is pitching something they think should be interesting but find the analyst/PM is not engaging in the way they hoped. To the data person this can feel like outright irrationality and evidence that fundamental investors are not long for this world; to the analyst/PM it can seem like a waste of resources on hype driven but irrelevant work. In practice something like this situation often happens with sentiment scores: the data team works hard to produce scores which capture the positive/negative sentiment in a quantitative way and those scores may perform very well in a predictive capacity but when introduced to the fundamental investors after a brief spell of curiosity they fall flat and end up being ignored.
<h3 class="MuiTypography-root MuiTypography-h3 css-1inx43p-MuiTypography-root">Explanation</h3>
What has gone wrong in these situations is that the investor has been presented with data which is incompatible with their process. You can think of fundamental investors as machines for converting data into investment decisions, and like any kind of machine they need the right kind of input to generate the desired results – if you put diesel into your petrol powered car you won’t get very far. While no two fundamental investors will have identical processes, as a group they are similar enough for us to make some generalisations: the kind of inputs investors thrive on are things like company filings, earnings calls, broker research, industry analysis, and news. Those are datasets – and they are datasets, even if that is not how we usually think of them – which are compatible with the investment process.
What then makes one dataset compatible and another incompatible? Ultimately it comes down to understanding. For data to be compatible with an investor’s process they need to understand where to place it in their analysis, how to weigh it against other inputs, when to trust it, and when to ignore it. Sentiment scores typically fail on this test: it’s hard to know how to weigh a negative sentiment score against your higher than consensus earnings forecast, or positive M&amp;A expectations, or long term bullishness on demographic driven sales growth; and it’s hard to know when a sentiment score pointing in one direction should trump a thesis pointing in the opposite direction. Compatibility is not a fixed concept but relative to an investor: different investors may be able to work with different datasets and the same investor may, over time, find that what is compatible for them changes as their skills and experience change.
The roots of this problem lie in a common but mistaken – in the context of fundamental investors – approach to data and extracting insights from it. Many investors don’t encounter this problem until they start working with new data, so there can be a tendency to think that the problem is the data, but really the problem is what is being done to the data. You can see this by thinking about sentiment scores again: they turn something investors are very comfortable working with (earnings calls, say) into something very alien. The reason new datasets are especially subject to this is that many of them are not consumable by investors in their original or raw form, meaning there is an extra step required. This extra step introduces data science and data scientists into the mix (not necessarily in your fund, they may be on the vendor side) and this creates fertile ground for problems to appear.
A key part of the problem is that most data scientists are trained to think about data science as an independent activity instead of one which fits into a bigger pipeline. As a result they define ‘good’ in terms of certain objective measures like model performance and tend to optimize for improving these measures. This makes perfect sense in an environment where data science is the entire process – like in a quant fund – but works very badly when data science is occupying an intermediary role – like in a fundamental fund. In our experience funds do well when instead of focusing on objective measures of correctness instead they focus on compatibility, i.e. ensuring investors understand and are comfortable working with the data they receive. Data scientists in this environment need to understand that their role is not to do the best data science in a global sense, but the most useful data science.
<h3 class="MuiTypography-root MuiTypography-h3 css-1inx43p-MuiTypography-root">Solution</h3>
We have two general rules you can apply to help mitigate this problem. The first is to make the output of any data analysis, model, or signal directly, logically, and obviously impact or predict some company KPI or accounting line item. It follows from this that data scientists should avoid focusing on share prices or entirely abstract things like sentiment. This rule doesn’t prevent the data from being bad but it does guarantee that it is recognisably about something investors understand and will be compatible with their process. It also importantly gives the investor and the data scientist a common language to communicate in which allows for iteration and improvement as both parties become more familiar with the working practices and preferences of the other: &#8220;this just isn’t relevant to me&#8221; becomes &#8220;I don’t care about KPI X for company Y for these reasons…&#8221;.
The second rule is to do as little data science as is necessary, or in other words to give investors the rawest data they can handle. For obvious human reasons this can be difficult for data scientists to follow and to some it may even sound wilfully perverse. But there is a good reason, namely that every extra step in processing or each additional element of complexity adds more distance in understanding between the investor and the data.
Importantly we find reducing complexity gives the best overall results even when more complexity and processing is objectively better in terms of accuracy and performance. Organisationally this means bringing investors into the data process early and making sure they understand all of the important steps in the process even when those are technical. Once again this is a dynamic state of affairs: different investors have different degrees of understanding and any one investor can learn more over time and thereby increase the range of data that is compatible with them. It also means that investors who want to get more value from data must engage with their data teams and vendors and should not shy away from digging into the nitty-gritty of collection and processing.
<h2 class="MuiTypography-root MuiTypography-h2 css-ur62r9-MuiTypography-root">The actionability problem</h2>
<h3 class="MuiTypography-root MuiTypography-h3 css-1inx43p-MuiTypography-root">Problem</h3>
Another common way in which data can fail to deliver for the investor is that the insights from the data may not be actionable or may be uneconomical if the cost to produce them outweighs their value. As an example you might be able to track social media engagement for a consumer brand which demonstrates that it has meaningfully better engagement than its main competitors – but that is unlikely to support an investment decision in the absence of other views on the company. The problem here is that while you have an insight it is a weak insight, meaning that on a standalone basis it is not enough to motivate an investment decision. Weak insights are not necessarily a problem, in fact most insights from data are weak in this narrow sense, but are only useful if you can ensure that you have pre-existing investment theses to plug them into or if not to ensure the cost to produce them is low enough to economically use them in screening and background research.
<h3 class="MuiTypography-root MuiTypography-h3 css-1inx43p-MuiTypography-root">Explanation</h3>
This problem occurs because (1) it is hard for most fundamental investors to trade small insights in isolation and (2) because the process of mining insights from data is very costly. The first issue is really less about being fundamental as such, and more a byproduct of most fundamental investors being quite concentrated: investors who run concentrated portfolios have a higher bar for inclusion that an idea must meet than a more diversified manager with an otherwise identical strategy. In our experience investors running portfolios with &lt;100 names will often find that the insights they get from the data alone are insufficient to meet that bar (we should note that this is only generally true, in practice there are exceptions). This is one of the reasons why quantitative investors can often structurally pay more for certain data because it is easier for them to trade on all of the insights in a given dataset. The second issue comes down to the contingent fact that in 2021 data and the people who work with it are very expensive, which in turn means that the cost of insights from data are also very high. These are the two factors weighing on the ROI of your data strategy and keeping them in check requires a structured process and good discipline.
<h3 class="MuiTypography-root MuiTypography-h3 css-1inx43p-MuiTypography-root">Solution</h3>
We find one of the best solutions to this problem is to work ‘from the idea to the data’. When you decide to bring in new data to your investment process there are broadly two ways you can do it: you can begin with the data and mine it for insights (going from the data to the idea), or you can begin with fully or partially formed investment ideas and look for data which would support or weaken them (going from the idea to the data). Most fundamental investors, most of the time, will find that they achieve significantly better ROI following the second approach. In practice this means reviewing your positions/ideas and asking what data in an ideal world would help you to confirm or disprove an investment thesis, and then going to look for that data. When stated like this most fundamental investors find this intuitive but the difficulty comes in maintaining discipline when confronted with seemingly infinite datasets and institutional pressure to do more with data.
This amounts to running a kind of ‘actionability’ filter ahead of time and ensures resources are only spent looking at datasets where successful insight mining will have an investment impact. It has two additional benefits: first that it gives you from the outset clear metrics for success which helps cut through vendor marketing and technical blather, and second that it allows you to estimate (necessarily very roughly) ahead of time the value of a given insight and to budget accordingly. Initially you may want to run a review process over all of your holdings and ideas in a strict fashion, but over time this is likely to become less burdensome as you only really need to go through this exercise on new names and ideas. You will also likely find some data sources demonstrate their evergreen usefulness which you keep on even if holdings change whereas others are tactical, picked up and discarded as needed. As part of the review process you will need to think about the stability and longevity of your interests in companies and sectors, particularly as you consider situations where data cannot be collected after the fact, and therefore need to decide whether you are willing to invest today to answer investment questions in the future.

how-to-get-more-value-from-data-actionable-solutions-to-common-problems

How to get more value from data: actionable solutions to common problems

Like most people when possible we prefer to get our data from the official source, we also like it when companies are compelled to provide the data under pain of legal or regulatory penalties. One example of that is the National Storage Mechanism, the official document storage for companies listed in the UK – now maintained by the FCA, but for a long time outsourced to Morningstar. According to Listing Rule 9.6.1. companies are supposed to forward their annual reports to the FCA’s document viewing facility (i.e. the NSM) and issue a notification via an RIS (i.e. the RNS). Many people assume that when the source is official and the data collection is governed by regulations that the underlying data will be reliable – but here we show you a couple of examples demonstrating this is not always the case.
So if we look at Tesco we see the system working like it should, on 13th May 2020 they issue an RNS notifying the market that they have published the Annual Report and have submitted it to the NSM.
<img decoding="async" class="alignnone wp-image-2314 size-full" src="https://insigai.io/wp-content/uploads/insights/bad-data-image-0.png" alt="bad-data-image-0" width="822" height="400" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-0.png?resize=200%2C97&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-0.png?resize=300%2C146&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-0.png?resize=400%2C195&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-0.png?resize=600%2C292&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-0.png?resize=768%2C374&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-0.png?resize=800%2C389&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-0.png?fit=822%2C400&amp;ssl=1 822w" sizes="(max-width: 822px) 100vw, 822px" />
If we check the NSM we find that annual report with the correct date:
<img decoding="async" class="alignnone wp-image-2310 size-full" src="https://insigai.io/wp-content/uploads/insights/bad-data-image-1.png" alt="bad-data-image-1" width="829" height="400" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-1.png?resize=200%2C97&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-1.png?resize=300%2C145&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-1.png?resize=400%2C193&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-1.png?resize=600%2C290&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-1.png?resize=768%2C371&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-1.png?resize=800%2C386&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-1.png?fit=829%2C400&amp;ssl=1 829w" sizes="(max-width: 829px) 100vw, 829px" />
However, the system doesn’t always work the way it should. Particularly in the Morningstar era we routinely come across examples like this:
<img decoding="async" class="alignnone wp-image-2311 size-full" src="https://insigai.io/wp-content/uploads/insights/bad-data-image-2.png" alt="bad-data-image-2" width="900" height="164" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-2.png?resize=200%2C36&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-2.png?resize=300%2C55&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-2.png?resize=400%2C73&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-2.png?resize=600%2C109&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-2.png?resize=768%2C140&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-2.png?resize=800%2C146&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-2.png?fit=900%2C164&amp;ssl=1 900w" sizes="(max-width: 900px) 100vw, 900px" />
This date is clearly incorrect: the 31st of December is the financial year end not the publication date (which is usually months after the financial year end). The same thing happens with Sage, where we can see the correct date from this RNS:
<img decoding="async" class="alignnone wp-image-2312 size-full" src="https://insigai.io/wp-content/uploads/insights/bad-data-image-3.png" alt="bad-data-image-3" width="900" height="119" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-3.png?resize=200%2C26&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-3.png?resize=300%2C40&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-3.png?resize=400%2C53&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-3.png?resize=600%2C79&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-3.png?resize=768%2C102&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-3.png?resize=800%2C106&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-3.png?fit=900%2C119&amp;ssl=1 900w" sizes="(max-width: 900px) 100vw, 900px" />
But again on the NSM the ‘publication date’ is in fact the financial year end:
<img decoding="async" class="alignnone wp-image-2313 size-full" src="https://insigai.io/wp-content/uploads/insights/bad-data-image-4.png" alt="bad-data-image-4" width="900" height="74" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-4.png?resize=200%2C16&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-4.png?resize=300%2C25&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-4.png?resize=400%2C33&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-4.png?resize=600%2C49&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-4.png?resize=768%2C63&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-4.png?resize=800%2C66&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/insights/bad-data-image-4.png?fit=900%2C74&amp;ssl=1 900w" sizes="(max-width: 900px) 100vw, 900px" />
The NSM is not the only party to blame, sometimes the company fails to carry out its obligations under the Listing Rules properly. If we look at Sage again, for 2018 and 2017 they did not publish an RNS when they released the annual report meaning there is now no extant public record of when that document was published. Some other FTSE 100 and FTSE 250 companies that have similarly failed at least once include Wood Group, Inmarsat, Arm, Sports Direct, Amlin, Polyus Gold, Imagination Technologies, International Power, and Aberdeen Asset Management.
This should serve as a reminder that (1) official sources are by no means immune from mistakes and (2) that something is a legal or regulatory requirement does not guarantee companies will follow it. On a more positive note the FCA’s recent decision to bring the NSM back in-house appears to have made a significant difference to the quality meaning we may have less of these issues in future.

bad-date-in-official-places-the-uks-national-storage-mechanism

Bad data in official places: the UK’s National Storage Mechanism

Our ESG Research Director Diana Rose recently spoke with Pensions Expert on how how machine learning and AI can help investors assess ESG standards.
To read her thoughts in full, use the link below:
<a href="https://www.pensions-expert.com/ESG/Machine-learning-can-help-investors-tackle-fast-fashion-ESG-issues?ct=true">https://www.pensions-expert.com/ESG/Machine-learning-can-help-investors-tackle-fast-fashion-ESG-issues?ct=true</a>

machine-learning-can-help-investors-tacklet-fast-fashing-esg-issues

Machine learning can help investors tackle fast fashion ESG issues

In the latest commentary piece, featured in ESG Investor, Diana Rose explains how artificial intelligence can expose evidence of empty promises and hold companies to account.
You can read her piece in full using the link below
<div class="video-shortcode">
<blockquote class="wp-embedded-content" data-secret="ZBQqWovgRs"><a href="https://www.esginvestor.net/esg-scoring-systems-are-giving-way-to-intelligent-analysis-of-disclosures/">ESG Scoring Systems are Giving Way to ‘Intelligent’ Analysis</a></blockquote>
<iframe class="wp-embedded-content" sandbox="allow-scripts" security="restricted" style="position: absolute; clip: rect(1px, 1px, 1px, 1px);" title="&#8220;ESG Scoring Systems are Giving Way to ‘Intelligent’ Analysis&#8221; &#8212; ESG Investor" src="https://www.esginvestor.net/esg-scoring-systems-are-giving-way-to-intelligent-analysis-of-disclosures/embed/#?secret=oK7zPxjmmQ#?secret=ZBQqWovgRs" data-secret="ZBQqWovgRs" width="600" height="338" frameborder="0" marginwidth="0" marginheight="0" scrolling="no"></iframe></div>

esg-scoring-systems-are-giving-way-to-intelligent-analysis-of-disclosures

ESG scoring systems are giving way to intelligent analysis of disclosures

When I co-founded Insig AI three years ago with my colleague Warren Pearson, we did so with a purpose to transform the asset management industry through AI and machine learning. This mission was based on multiple conversations we had with industry leaders where it became apparent to us that while many wanted to get the benefits of machine learning and AI, they simply did not know how to go about it.
Through companies like Citadel and BlackRock, we have seen just how successful machine learning and AI can be. Few in the industry, however, have their resources – to spend big and build teams who can create bespoke in-house solutions – and the challenge for them becomes how to keep up. This is where Insig AI’s suite of “out of the box” products come in. Our solutions allow investment professionals to see, interact, and experience their data in a way they never have before.
A core way in which we do this is through data visualisation. When we take a client’s data and apply our machine learning and AI tools to it, we are giving them the ability to interpret it through multiple lenses in a fraction of the time traditional tools would take. The success our clients are enjoying shows just how beneficial these solutions are. One client’s fund has so far outperformed by 30 percentage points, while another has experienced a 25% reduction in operational costs.
It’s important to note that these results are not reached by machine learning and AI alone, but rather augmenting the portfolio managers’ decision-making ability and judgement. The tools generate insights that may otherwise have gone unseen. Ultimately, however, investment decisions continue to remain with the portfolio manager. One of the major benefits AI and machine learning products gives to asset managers is speed in processing and investigating data. When once testing a hypothesis would once have taken five days to investigate can now be done in a matter of minutes.
As many asset managers know, their first instincts on an investment can often be far from the mark. Machine learning and AI products expedite this discovery process, so when they are wrong – they know it fast. This allows the user to move on to their next hypothesis rapidly. The success here lies in being able to investigate multiple theories at once, in order to find and validate the right path forward. During the discovery process, we also know that when weighing a decision, it is equally important to know the data and decision-making behind it. Therefore transparency is a core pillar of our company and our products are not “black boxes” – you can drill down every step of the way.
As new trends and industry sentiments emerge the ability to be flexible will be as important as ever. As the global asset management spend on AI is set to reach $8.3bn in the next five years, technology will continue to play an ever-increasing importance.
To this end, we are already developing a product called Insig ESG, set to launch in the third quarter of this year, which will develop our clients ESG strategy and execution. While our future products pipeline will look to continue to enhance the ability to mitigate risk, reduce costs, deliver better returns, and deliver products and services more efficiently. The transformative ability of AI and machine learning on Asset Management is no longer the domain of a few, and Insig AI is the perfect partner to help deliver it, now and in the future.

ai-offers-unparalled-insights-to-the-asset-management-industry

AI offers unparalleled insights to the asset management industry

As the demand for sustainable investing continues its rapid growth, so too does the importance of reliable ESG credentials and analysis. The uncomfortable truth is that not all ethical investments are what they appear to be, and it’s easy to lose sight of key ESG issues in decision-making. The challenge for investment managers, therefore, becomes how to truly see the wood for the trees.
At its heart, the rise of ESG is a response to demand for companies to do what’s right. If this seems hard to pin down, it is because it is – what’s right for one corporate now may not be right for others in the future. To find consensus and gain perspective on the big picture, while also gathering the detail that really matters, are real challenges that threaten ESG’s credibility. They are also where AI and machine learning can play a crucial role.
Traditionally, ESG credentials are based on the findings from a human analyst, meaning they can be skewed by bias and assumptions. These findings are then prone to being highly processed by obscure methodologies to deal with the challenge of aggregating disparate data. Meanwhile, to add a further challenge, the goalposts of what matters to whom are constantly shifting.
Furthermore, investment analysts are facing an ever-increasing amount of complex data and information relating to ESG. Gathering reliable data points, unpicking methodologies and reading between the lines of Sustainability Reports can seem like a monumental task. Going deep is one thing, but after all that hard work, data can still end up meaningless unless seen in the wider context of a decent benchmark.
This is where the ratings agencies such as MSCI and Sustainalytics stepped in to do a lot of the hard work of research, input, benchmarking and scoring. Asset Managers breathed a sigh of relief. But it soon emerged that a disparity in approaches was resulting in a lack of consensus and transparency in the outputs.
This was recently highlighted by analysis by State Street Global Advisors, as part of an 18-month due diligence process which looked at 30 ESG data providers. It found that ratings aligned on only half of the companies covered. As SSGA noted; “in choosing a particular provider, investors are, in effect, aligning themselves with that company’s ESG investment philosophy.” One thing has become clear: a simple ESG rating is no longer enough for asset managers to hang their hats on; proprietary analysis is still crucial in the quest for evidence and transparency to underpin their confidence in ESG investment decisions.
This is where AI and machine learning can add real value to the ESG analyst’s task as well as the investment manager’s confidence. When it comes to drilling deep into a company’s ESG disclosures, for example, machine learning can do in minutes what would take a team of people days. And as for going wide, AI is perfectly equipped to filter, organise and benchmark huge volumes of diverse data in ways an analyst can then digest and explore. AI can do a lot of the hard work, without introducing the same human bias and assumptions that can creep into aggregation methodologies.
Natural Language Processing (NLP) is a great example of AI’s ability to go wide, by harnessing the content of hundreds of thousands of sources for relevant data. NLP can find and contextualise text that is not in a structured format and make sense of it. The result is that relevant insights from live, ‘real world’ sources can be organised, ready for analysis alongside more formally reported information. This provides a new level of context and richness to the picture of an ESG investment opportunity. NLP can also act like an ‘ear to the ground’ in a shifting landscape of ESG materiality and risk, identifying and highlighting emerging trends that may affect a company – beyond what they’ve reported or are even aware of.
While we are still in the early stages of AI’s application to ESG, some key factors are set to rapidly accelerate its use. On climate change, for instance, it’s no huge surprise that some companies have been getting away with greenwashing – for example talking about ‘Net Zero’ without any strategy to get there. AI has the ability to help bring this to light, and public, political and regulatory scrutiny is on the rise; as Raba Foroohar recently pointed out in the FT “individual companies are being held explicitly responsible for the risks of global warming”.
These increasing standards and scrutiny are both overdue and necessary, making it an exciting time in the world of ESG. But the speed at which things are moving presents challenges for both companies and investors, and there’s a risk that transparency, evidence and ultimately credibility get compromised. As a partner to human decision-making in a complex and at times perplexing world, the power of AI is starting to be harnessed for ESG; if done right, it will add both quality and that all-important sense of perspective.

seeing-the-wood-for-the-trees-with-the-help-of-ai

Seeing the wood for the trees with the help of AI

The recent high-profile acquisition by Philip Morris International (PMI) of Vectura, a health tech company that produces inhalers, has been dominating headlines. Heated debate has been spurred as to whether such an apparent conflict of interest should be allowed, or whether it’s a sign that ESG momentum is biting and business is being forced to pivot for the better.
On one side, there is a sense of moral outrage that a company should profit from solutions to problems it contributed towards. There are also serious concerns expressed by health academics who believe the acquisition will allow Big Tobacco to wield undue influence on UK health policy. This response seems to boil down to a question of whether businesses with damaging legacies deserve to thrive now our eyes are open to their impact – are they beyond becoming sustainable.
On the other side of the coin is the more pragmatic view that these influential companies have a role to play in the transition away from harmful practices. This side argues that reactive divestment in bad news assets is not the answer; washing your hands of poor ESG stocks merely allows them to be snapped up by the less discerning. Ultimately, this leads to our environment and society being no better off.
So while PMI seems to have pulled off a highly strategic move that’s probably key to its survival and the whole thing makes us deeply uncomfortable, it begs the question: Does the motivation really matter if the overall outcome is positive? Sustainability is fundamentally about the long term view – how will we look back at this moment in its evolution?
It’s impossible to say, but critically, the first part of answering this question is to ensure that the outcome is positive and to do that, companies need to be held to account in fulfilling their ESG promises. It is the same logic when it comes to greenwashing. If a company sets long-term targets for emissions, ethics and equality but nobody checks in on whether they’re delivered upon, the company enjoys all the benefits of sounding virtuous without ever having to deliver or face the consequences.
Part of the problem is that littering reports with generic wording, vague long-term targets and catch-all terms has become dangerously easy. So much so that the EU has introduced new anti-greenwash rules forcing the asset management industry to categorise and qualify their ESG claims. And the EU is not alone, with the UK Competition and Markets Authority (CMA) recently putting business “on notice”. In a release published just last month, the CMA warned businesses they have until the New Year to make their environmental claims comply with the law. Further, it stated it would be carrying out a full review of misleading green claims early next year and “stands ready to take action against offending firms”.
Some see the greenwashing trend as threatening the whole ESG movement; others see it as teething problems that won’t hold back the wave of change. Again, what’s key to getting it right is to enforce accountability and responsibility. Consumer pressure, regulation and convergence of standards clearly have a role to play, but it’s still a tricky space for discerning investors. Assessing credibility and tracking delivery to targets is made challenging by the sheer volume of reporting resources published, and genuine disclosures can get obscured in confusion.
This is where we are starting to see the role technology can play in holding companies to account to having the impact they intend. AI and machine learning are emerging as practical tools that investment professionals can use to identify if corporate promises are being acted upon. Thoughtfully trained algorithms can do a lot of the heavy lifting of organising and processing vast volumes of corporate disclosures so that a researcher can more effectively and efficiently home in on metrics, read between the lines where things aren’t what they seem, and determine from the evidence whether words and actions match where it matters.
We are still in the early days of ESG and, as the PMI dilemma highlights, there are conflicts, trade-offs and strong arguments for principles and pragmatism that can pull in different directions. However, as the complexity we need to grapple with increases, so to do the tools and solutions at our disposal. It is my belief that ESG will create positive change over the long term, and AI and machine learning will play a valuable role in supporting us on the journey.

Insights

Using AI to help financial regulators detect greenwashing. Presentation with ImpactScope at World AI Cannes Festival 2024