ESG & Generative AI – Clash of the brave new worlds (Part 3 of 3)

Part 3 in our thought leadership series exploring the opportunity for ESG and AI. In this third article, we consider how AI can be leveraged to automate ESG reporting

Here we are in the third and final leg of our mini-series exploring the collaborative possibilities of ESG and Generative AI in the sustainable finance space.

In part 2, we delved into strategic decision making, considering models such as double materiality and triple bottom line. We also highlighted some uses cases where generative AI is being leveraged to assist with data aggregation, analysis and decision making in the sustainable finance space.

In this final article, we consider how AI can be leveraged to automate ESG reporting – an emerging and increasingly relevant requirement in the financial services industry given mounting disclosure requirements. Whether it’s CSRD, ESRS, SDR, GAR or any other number of acronyms in the space, the bottom line is that ESG-related regulations are intensifying and it’s always better to be ahead of the game.

Automating ESG reporting

Let’s start by contextualizing why the disclosure and reporting requirements are so critical to the sustainable finance space.

There is a well-publicized need for private sector financing to meet the transition to net zero – and banks around the world are expected to be key facilitators of this. In order to promote capital inflows and investment towards the transition to a low-carbon economy, regulators and supervisory bodies around the world have established market guidelines to ensure transparency and reduce ‘greenwashing’ risks. In Europe, the European Commission's CSRD rules exemplify this: “The new rules will ensure that investors and other stakeholders have access to the information they need to assess the impact of companies on people and the environment, and for investors to assess financial risks and opportunities arising from climate change and other sustainability issues.”

In our first article in this 3-part series, we discussed leveraging unstructured data and how banks will need to draw upon a combination of existing and new data points to fulfil these requirements. Incubity, part of AI-based product development and consulting firm, Ambilio, explores how Generative AI and large language models (LLMs) can automate sustainability reporting: “By parsing through vast datasets from disparate sources, these technologies extract pertinent information, analyze trends, and generate comprehensive reports. This automation not only saves time but also ensures accuracy and completeness in compliance with ESG standards.”

In the market

Automating ESG reporting presents significant challenges, yet within these challenges lie valuable opportunities, and there are many players in the market now on hand to enable this. KPMG assert that one of the key challenges in ESG reporting is the “convergence of financial and non-financial reporting” where the employment of an appropriate technology stack is essential to feed both data sets into a data lakehouse to enable standardized reporting.

Let’s take a look at one of the leading players in the market – Envizi ESG Suite by IBM. The software pledges to “remove the challenges and complexity of ESG data collection, analysis and reporting so you can harness the power of data to fast-track your success” and emphasizes the benefit of automation – with one company having reduced their time spent on ESG disclosure by 50% in one year. The solution is able to collate over 1,000 quantitative and qualitative data points which, for financial services users, facilitates adherence with the major international reporting frameworks and disclosure requirements.

From a Finastra perspective, the end-to-end sustainable finance journey is a top priority, with our ESG Service automating and streamlining the lifecycle management of sustainability-linked loans and bonds. This SaaS solution enables our clients to manage all of the complexities associated with sustainable debt, including KPI and performance management, pricing adjustments and testing cycles. Ongoing and historic data captured within the Finastra ESG Service can be fed into data lakes and warehouses, which, as this space grows and evolves, will become an even more vital information source for banks’ reporting requirements and disclosures.

Joining the dots

There is no doubt that meeting ESG reporting requirements is a complex and onerous task, especially for large organizations operating in multiple jurisdictions. And the case for automating ESG reporting and disclosures is clear. Harnessing AI and large language models to automate ESG reporting promises to significantly speed up the processes of data collection, aggregation and facilitate the generation of disclosures and reporting. It is also clear, however, that these technologies cannot be applied in isolation – applying human guidance and contextualization is essential.

As we draw this mini-series to a close, we’ve seen that there is vast potential for the use of AI in the ESG space as well as in financial services more broadly. The question for market players now is how, when and where to deploy the technology both effectively and ethically.

Thank you for joining us on this exploration of these two hot topics. Continue to watch this space for more to come.

We would be delighted to discuss your ESG and sustainable finance strategies further with you; please feel free to reach out to Jamie directly or alternatively contact us below.