It’s time for banks to take responsibility for their ESG data

In the wake of COP26 last year, the environmental, social and governance (ESG) regulatory agenda is advancing at pace and organisations are rightfully under pressure to make improvements in this area.

Banks will need to dedicate significant resources to tackle the challenge of ESG data

From 6 April this year, over 1,300 of the largest UK-registered companies and financial institutions will have to disclose climate-related financial information on a mandatory basis – in line with recommendations from the Task Force on Climate-Related Financial Disclosures. This will include many of the UK’s banks and insurers, so it’s important for them to improve how they report their ESG metrics to move the needle now.

However, there are major challenges standing in their way. While regulators are making progress, there is no detailed, globally agreed definition of the term “ESG”. There are multiple standards for reporting elements of ESG performance, but even this doesn’t really tell us what good looks like. And the ESG data landscape is rocky, uneven terrain at best.

These issues are having the following knock-on effects:

1. Most ESG data is self-reported, inconsistent and difficult to compare. Companies are free to disclose (or not disclose) the information they choose based on their understanding of the requirements, meaning comparison or aggregation across different organisations and subsequent benchmarking is next to impossible.
2. ESG data is patchy, particularly for smaller and private corporates who may not have the required resources to improve the quality and quantity of their ESG data.
3. ESG data is compromised by interdependence, whereby data collectors interpret ESG data in line with their own practices, rather than via a standardised means. This causes measurement discrepancies that get worse as that data is combined with other data further up the value chain.
4. Data collectors and providers’ outputs are unverified and inconsistent, meaning separate agencies award organisations completely different ESG ratings – the accuracy of which are also difficult to validate.
5. ESG data is out of date – almost all ESG data is backwards looking and shows up in different commercial sources at different rates.
6. Financial services organisations lack their own ESG data competencies because there aren’t enough data scientists and engineers with domain expertise available to meet their needs. Furthermore, they do not have the tools required to develop an appropriate ESG data analytics solution.
7. Banks cannot source all the ESG data they need through a single provider meaning there is no single source of truth. This requires them to challenge the inconsistencies of the ESG data industry, as well as the technical difficulties of compiling multiple data sources.

Addressing ESG data challenges

To combat these challenges, banks must build a data solution that is dense, explainable, diverse, recent and forward-looking. To achieve this, they need to be more imaginative about the breadth of data that might be useful and relevant. By accessing new data sources, they will have a means by which to build more accurate, timely and verified assessments and measures.

They also need a solution that provides a dataset that incorporates ESG information from all the entities involved in an asset or transaction. This will make it possible to create a standardised way to compare performance on ESG risk and value across an entire network.

Furthermore, by making better use of new tools and technologies to evaluate that data, banks can produce higher quality outputs. Multiple datasets must ultimately be incorporated into these tools (such as satellite imagery, locally sourced reporting and communications data sources) that could provide both additional information and a means with which to verify their ESG reporting standards to regulators, investors and customers.

Organisations must also factor in the implementation of new frameworks for analysis and innovative technologies capable of delivering insight at scale, for example natural language processing (NLP), artificial intelligence (AI) and machine learning (ML). More sophisticated data collaboration and analytics tools will be part of the solution as well. On top of that, banks must be careful to plan for the extensive computing power that, even in the age of cloud computing, will be required to use those techniques in the right way.

The responsibility for delivering those ESG data solutions of the future lies in multiple places. Financial services organisations absolutely have a right to expect more from ESG data providers, but they cannot afford to sit back and wait for change to happen by itself. The sector should take on the responsibility themselves to drive the ESG data agenda forward; they are the ones who could be left accountable when the PRA’s and other regulatory standards are phased in.

It’s not all about reporting

With better use of data, it is not only ESG reporting that will progress, but there are other opportunities that will emerge. The real impact of ESG initiatives will be easier to measure, meaning it will be easier to justify the business case for investing more in underlying ESG activities in the long term.

Also, organisations will have the ability to see what is driving fluctuations in their ESG metrics in real time, so that smart decisions about how the business operates can be made – saving money and resources all while managing ESG impact.

Organisations who have made progress in this area are themselves starting to develop new value propositions and products that truly deliver value to their customers and will help them maintain competitive advantage and drive sustainable revenue growth.

There is no escaping the fact that banks will need to dedicate significant resources to tackle the challenge of ESG data, but the regulatory risks and potential business opportunities make a strong argument for innovation at pace.

About the author:

Paul Henninger is global head of KPMG lighthouse data, AI and emerging technologies at KPMG.

He has also previously served as chief data science officer, senior managing director at FTI Consulting and held senior roles at Fico and NICE Actimize.