Introducing the TDI

Advisory services combining sustainability and financial expertise with technology solutions to manage data better. | /solutions/esg-consultancy

Sustainability Consultancy

Leverage our unique dataset of machine-readable financial and sustainability reports for your LLMs or research.| /solutions/esg-data

AI-ready ESG Data

ESG research platform which measures how well a company reports corporate and sustainability information.| https://tdi-demo.insg.ai

Transparency & Disclosure Index

Put your information at the centre of your organisation to increase efficiency, reduce risk, and identify opportunities. | /solutions/data-engineering

Data Engineering Solutions

With this year’s exponential advancements in open-source AI, regulators and investors are now armed with tools for benchmarking and scrutiny that means companies can no longer fly below the radar on transparency, disclosure, accountability and greenwashing.  
Sustainability reporting has been around for over a decade and is finally being regulated; are large companies ready?  
If not, why not, and what risk does that pose to them and their investors?
<h3>Announcing the TDI Global</h3>
Insig AI has released the Global version of its Transparency and Disclosure Index (TDI).  Covering over 2,300 of the world’s largest listed companies, it takes stock of which documents companies are publishing and benchmarks their levels of governance and sustainability disclosure.
Agnostic to ‘green’ vs ‘dirty’ sectors, companies are recognised for making certain information available for accountability and trust and showing readiness for emerging regulation.
The platform uses dynamic statistical benchmarking against the global universe and sector peers.  The index is live and responsive; as soon as a company is added, the ranking updates.
2,300 companies are ranked dynamically against the global universe and their sector benchmark:
<img decoding="async" class="wp-image-3468 aligncenter" src="https://insigai.io/wp-content/uploads/2024/06/TDI-blog-Picture-1.png" alt="" width="717" height="467" />
The TDI’s transparent scoring highlights strengths and gaps in reporting, with granular and actionable insights.  It signposts to best practice examples and links directly to the evidence-base of Insig AI’s database of c200,000 machine-readable corporate reports.
This benchmarking ability, extensive database and machine-learning and AI-assisted research tools make the TDI Global a powerful and highly flexible asset for consultants and analysts.
<h3>Large, listed…lagging?</h3>
Scored out of 100, a TDI result above 70 indicates a company is close to meeting stakeholders’ corporate reporting expectations. Anything below 50 is evidence of significant and multiple gaps in disclosure, lagging against expectations driven by reporting standards and the benchmark.
Distribution of TDI results for the world’s largest companies, with 59% failing to meet expectations and 21% lagging significantly:
<img decoding="async" class="aligncenter wp-image-3475" src="https://insigai.io/wp-content/uploads/2024/06/Blog-picture-2.png" alt="" width="804" height="421" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-2.png?resize=200%2C105&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-2.png?resize=300%2C157&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-2.png?resize=400%2C210&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-2.png?resize=600%2C315&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-2.png?resize=768%2C403&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-2.png?resize=800%2C419&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-2.png?resize=1024%2C537&amp;ssl=1 1024w, https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-2.png?resize=1200%2C629&amp;ssl=1 1200w, https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-2.png?w=1362&amp;ssl=1 1362w" sizes="(max-width: 804px) 100vw, 804px" />
The results reveal that 59% of mega and large-cap companies fall below the 70 mark and 21% are lagging significantly.  These are companies with over $200 billion and $10 billion market cap respectively.
<h3>This raises key questions</h3>
1 &#8211; Fundamentally, these are the world’s richest, most investable and regulated companies: Where is their information?
2 &#8211; Sustainability aside, expectations on governance, risk and accountability are not about to go away: How can we make well-informed decisions?
3 &#8211; More stringent regulation is emerging to level the playing field: Does the data explain the push-back? 
4 &#8211; Environment, Social and Governance (ESG) ratings agencies have been filling a gap, but when companies publish so little: What are these ratings based on?
<h3>Back to basics </h3>
The TDI is scored on six criteria, one of which is whether certain key documents have been published on the website.  These include reports, policies or statements on pay gap/diversity, modern slavery/human rights and business ethics/conduct.
The TDI’s stocktake shows that more than one in five of the overall TDI universe have failed to publish a single one of these documents within the last three years.
Breakdown of overall TDI Global results by 6 scoring components.  22% of companies have failed to publish key documents on pay gap/diversity, human rights and business ethics/ conduct within the last three years:
<img decoding="async" class="aligncenter wp-image-3470" src="https://insigai.io/wp-content/uploads/2024/06/Blog-picture-3.png" alt="" width="731" height="351" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-3.png?resize=200%2C96&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-3.png?resize=300%2C144&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-3.png?resize=400%2C192&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-3.png?resize=600%2C288&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-3.png?resize=768%2C369&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-3.png?resize=800%2C384&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-3.png?resize=1024%2C491&amp;ssl=1 1024w, https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-3.png?resize=1200%2C576&amp;ssl=1 1200w, https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-3.png?w=1442&amp;ssl=1 1442w" sizes="(max-width: 731px) 100vw, 731px" />
<h3>Good news despite the weather </h3>
Trends in the data reveal clear regional patterns.
The EU and UK are on different tracks for implementing reporting requirements for companies and funds.  The EU has taken the lead first with SFDR and CSRD for investors and companies respectively, while the UK has just released SDR and is expected to align with ISSB*.
There is a race on to be leaders in sustainable investment while also cracking down on greenwashing.  The TDI will be a strong indicator of who’s winning this race over the next couple of years.
Score distribution by % of mega and large-cap listed companies in four regions (T1=lowest 3rd of scores, T3=highest 3rd of scores):
<img decoding="async" class="aligncenter wp-image-3471 " src="https://insigai.io/wp-content/uploads/2024/06/Blog-picture-4-e1719410537480.png" alt="" width="756" height="386" srcset="https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-4-e1719410537480.png?resize=200%2C102&amp;ssl=1 200w, https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-4-e1719410537480.png?resize=300%2C153&amp;ssl=1 300w, https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-4-e1719410537480.png?resize=400%2C204&amp;ssl=1 400w, https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-4-e1719410537480.png?resize=600%2C306&amp;ssl=1 600w, https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-4-e1719410537480.png?resize=768%2C391&amp;ssl=1 768w, https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-4-e1719410537480.png?resize=800%2C408&amp;ssl=1 800w, https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-4-e1719410537480.png?resize=1024%2C522&amp;ssl=1 1024w, https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-4-e1719410537480.png?resize=1200%2C612&amp;ssl=1 1200w, https://i0.wp.com/insigai.io/wp-content/uploads/2024/06/Blog-picture-4-e1719410537480.png?w=1440&amp;ssl=1 1440w" sizes="(max-width: 756px) 100vw, 756px" />
Meanwhile in the US, in March the Securities and Exchange Commission (SEC) adopted its final rules to require large companies to disclose certain climate-related information in annual reports.  Coming into effect next year and more prescriptive than Taskforce for Climate-Related Financial Disclosure (TCFD), the US will have some catching up to do.
<h3>Nowhere to hide</h3>
2024 marks a global transition, from leaders and laggards getting lost amid the noise, to companies being held accountable on transparency and disclosure.  With the advancements in benchmarking and scrutiny, regulators and investors are adding new powers to their radar that mean companies can no longer fly below it.
The TDI Global is designed to identify gaps between what’s expected and what’s being done now, and to help direct companies to close these gaps one by one.  Not to tick boxes, but as a constructive feedback signal to make fundamental information accessible to meet expectations.
As we move beyond ‘ESG’ as we’ve known it, this is the time to be closing these gaps and walking the talk.  The principles of transparency, accountability, clarity and communication are more important than ever to build trust in sustainable investment.
* Sustainable Finance Disclosure Regulation (SFDR); Corporate Sustainability Reporting Directive (CSRD); Sustainability Disclosure Requirements (SDR); International Sustainability Standards Board (ISSB).

TDI-global-is-announced-and-reveals-trends

TDI Global reveals 59% of world’s largest companies are failing to meet basic reporting expectations, while the UK takes a clear lead

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" 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?w=1883&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?w=754&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?w=754&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?w=1754&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?w=892&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?w=1420&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?w=840&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?w=745&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?w=1430&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?w=1063&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?w=852&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?w=836&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?w=903&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?w=797&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?w=663&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?w=1379&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?w=1379&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?w=1233&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?w=903&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?w=624&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>

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