Diana Rose, ESG Research Director, Insig ESG, explains how artificial intelligence can expose evidence of empty promises and hold companies to account.
To be able to meet the opportunity that lies in the transition to net zero, investors need ESG information that allows them to meaningfully appraise a firm’s progress against targets and be able to hold them to account. Artificial intelligence is emerging as a powerful tool in the evolution of meaningful ESG scores.
The ESG scoring dilemma
ESG scoring has huge potential to unlock a significant amount of information on the management and resilience of companies when pursuing long-term value creation. It can also represent an important market mechanism to help investors better align their portfolios with environmental and social criteria that align with sustainable development.
However, currently the investment community is faced with the dilemma of ESG ratings varying strongly depending on the provider chosen. This happens for a number of reasons that are often obscured in the methodology and aggregation of data, such as different frameworks, key indicators and metrics, data quality, qualitative judgement, and weighting.
These differences make it nearly impossible for investors to have confidence in the way they are managing the material ESG risks within their investment mandates and can lead to missed opportunities as well as nasty surprises.
AI supports the thoughtful analyst
One of the focus areas of COP26 was around strengthening the impact of ESG investment, calling for greater efforts toward transparency, consistency, and comparability, alongside the announcement of a convergence of reporting standards. Attention was placed on corporate disclosures as the real temperature check, both for businesses and the investors that finance them.
So, if ‘ESG scores on a plate’ won’t do any more, what does good practice ESG assessment look like? Gathering evidence from corporate disclosures will have to be a combination of quantitative science and qualitative art, which puts a lot of pressure on the analyst.
In parallel to this raising of the bar in ESG assessment, artificial intelligence has been fast developing into a versatile partner for the thoughtful analyst. Machine learning, a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention, is also becoming more democratic.
The advent of cheaper off-the-shelf algorithms, combined with advances in computational power, mean investment managers and analysts have new superpowers.
Proprietary machine-learning models can process and visualise huge amounts of messy qualitative data, taking a lot of the grunt-work out and enabling more transparent, robust and timely analysis. When directed thoughtfully, the insights that AI offers can enable investors to make more informed investment decisions and reduce the dependence on a high-level ESG score.
Directions of travel
One of the more insightful ways to consider ESG data is to analyse change over time through a company’s disclosure history – this can reveal gaps that may flag up greenwashing, as well as meaningful improvements compared to a peer group. A search for trends like this is just one example of analysis that’s painful to tackle manually, but lends itself beautifully to technology.
To illustrate this, let’s take a look at a handful of household retail names in the FTSE 100. Looking at key climate change disclosures across all reports published since 2015, some patterns immediately come to light. There’s a significant gap between the leaders and laggards, and different trajectories in direction of travel between those which have been reporting consistently and those whose disclosures have recently accelerated.
Fig 1. Count of climate-related keywords mentioned each year in Annual Reports, Sustainability Reports, ESG documents and Earnings Call Transcripts; ‘Carbon Emission’, ‘Carbon Footprint’, ‘Carbon Neutral’, ‘CDP’, ‘Climate Change’, ‘Net Zero’, ‘TCFD’. Source: InsigAI
Given the high-profile supply chain issues among retail giants over the past few years, viewing disclosures with a social lens on workforce issues is also insightful. It reveals a steady upwards trend across most of the peer group, with boohoo Group lagging behind the curve in both timing and volume of disclosures.
Fig 2. Count of workforce-related keywords mentioned each year in Annual Reports, Sustainability Reports, ESG documents and Earnings Call Transcripts; ‘Diversity’, ‘Gender’, ‘Human Rights’, ‘Modern Slavery’, ‘Pay Gap’. Source: InsigAI
Nowhere to hide
COP26 made it clear that the spotlight is firmly on company disclosures regarding net zero, with the idea that those driving the transition are rewarded by the market. This requires investor scrutiny. Science-based metrics are essential but so is the qualitative assessment of the context and direction of travel contained in disclosures.
Stock-pickers are pouring more and more into companies making most progress regarding ESG, so the potential for greenwashing will increase. Last year, the UK’s Competition and Markets Authority found that 40% of environmental information on corporate websites was deemed to be misleading.
By using machine learning algorithms and natural language processing, investors now have tools at their fingertips to surface and expose evidence of empty promises and hold companies to account. In the evolution of the ESG space, the benefits of access to powerful technology to decision-makers is playing its part. Now it’s time to read between the lines to find who’s really going to move the needle.
ANALYTICS, COP26, DISCLOSURE, ESG, NET ZERO INVESTING, NON-FINANCIAL REPORTING, RATINGS, RISK MANAGEMENT, SCORES, SOCIAL, TECHNOLOGY
Diana Rose, ESG Research Director, Insig ESG, explains how artificial intelligence can expose evidence of empty promises and hold companies to account.
To be able to meet the opportunity that lies in the transition to net zero, investors need ESG information that allows them to meaningfully appraise a firm’s progress against targets and be able to hold them to account. Artificial intelligence is emerging as a powerful tool in the evolution of meaningful ESG scores.
The ESG scoring dilemma
ESG scoring has huge potential to unlock a significant amount of information on the management and resilience of companies when pursuing long-term value creation. It can also represent an important market mechanism to help investors better align their portfolios with environmental and social criteria that align with sustainable development.
However, currently the investment community is faced with the dilemma of ESG ratings varying strongly depending on the provider chosen. This happens for a number of reasons that are often obscured in the methodology and aggregation of data, such as different frameworks, key indicators and metrics, data quality, qualitative judgement, and weighting.
These differences make it nearly impossible for investors to have confidence in the way they are managing the material ESG risks within their investment mandates and can lead to missed opportunities as well as nasty surprises.
AI supports the thoughtful analyst
One of the focus areas of COP26 was around strengthening the impact of ESG investment, calling for greater efforts toward transparency, consistency, and comparability, alongside the announcement of a convergence of reporting standards. Attention was placed on corporate disclosures as the real temperature check, both for businesses and the investors that finance them.
So, if ‘ESG scores on a plate’ won’t do any more, what does good practice ESG assessment look like? Gathering evidence from corporate disclosures will have to be a combination of quantitative science and qualitative art, which puts a lot of pressure on the analyst.
In parallel to this raising of the bar in ESG assessment, artificial intelligence has been fast developing into a versatile partner for the thoughtful analyst. Machine learning, a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention, is also becoming more democratic.
The advent of cheaper off-the-shelf algorithms, combined with advances in computational power, mean investment managers and analysts have new superpowers.
Proprietary machine-learning models can process and visualise huge amounts of messy qualitative data, taking a lot of the grunt-work out and enabling more transparent, robust and timely analysis. When directed thoughtfully, the insights that AI offers can enable investors to make more informed investment decisions and reduce the dependence on a high-level ESG score.
Directions of travel
One of the more insightful ways to consider ESG data is to analyse change over time through a company’s disclosure history – this can reveal gaps that may flag up greenwashing, as well as meaningful improvements compared to a peer group. A search for trends like this is just one example of analysis that’s painful to tackle manually, but lends itself beautifully to technology.
To illustrate this, let’s take a look at a handful of household retail names in the FTSE 100. Looking at key climate change disclosures across all reports published since 2015, some patterns immediately come to light. There’s a significant gap between the leaders and laggards, and different trajectories in direction of travel between those which have been reporting consistently and those whose disclosures have recently accelerated.
Fig 1. Count of climate-related keywords mentioned each year in Annual Reports, Sustainability Reports, ESG documents and Earnings Call Transcripts; ‘Carbon Emission’, ‘Carbon Footprint’, ‘Carbon Neutral’, ‘CDP’, ‘Climate Change’, ‘Net Zero’, ‘TCFD’. Source: InsigAI
Given the high-profile supply chain issues among retail giants over the past few years, viewing disclosures with a social lens on workforce issues is also insightful. It reveals a steady upwards trend across most of the peer group, with boohoo Group lagging behind the curve in both timing and volume of disclosures.
Fig 2. Count of workforce-related keywords mentioned each year in Annual Reports, Sustainability Reports, ESG documents and Earnings Call Transcripts; ‘Diversity’, ‘Gender’, ‘Human Rights’, ‘Modern Slavery’, ‘Pay Gap’. Source: InsigAI
Nowhere to hide
COP26 made it clear that the spotlight is firmly on company disclosures regarding net zero, with the idea that those driving the transition are rewarded by the market. This requires investor scrutiny. Science-based metrics are essential but so is the qualitative assessment of the context and direction of travel contained in disclosures.
Stock-pickers are pouring more and more into companies making most progress regarding ESG, so the potential for greenwashing will increase. Last year, the UK’s Competition and Markets Authority found that 40% of environmental information on corporate websites was deemed to be misleading.
By using machine learning algorithms and natural language processing, investors now have tools at their fingertips to surface and expose evidence of empty promises and hold companies to account. In the evolution of the ESG space, the benefits of access to powerful technology to decision-makers is playing its part. Now it’s time to read between the lines to find who’s really going to move the needle.
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