Leveraging emerging technology to enhance ESG and sustainability reporting

According to the latest Forvis Mazars C-Suite Barometer, the prominence of new technology as a global trend is on the rise in financial services, making it one of the most important issues now topping the C-Suite agenda alongside environmental, social, and governance (ESG) expectations from regulators and society.

There is no doubt that emerging technologies such as machine learning (ML) and generative artificial intelligence (AI) can be effective tools in reducing the burden of ESG reporting and improving transparency on sustainability-related issues. However, optimising the use of such tools requires a clear understanding of extremely complex sustainability disclosure requirements that currently lack standardisation at a global level. This complexity is reflected in the Forvis Mazars C-Suite Barometer, where understanding regulation on sustainability reporting is noted as a top challenge for 36% of financial services leaders, along with data capture.

An important first step before implementing IT tools is, therefore, to understand, digest and operationally interpret sustainability regulations so that their impacts on the organisation are clear. What disclosures are required and how they are reported to various stakeholders internally, as well as to clients, regulators and suppliers, can then be factored into IT frameworks and technology transformation plans. This approach will help lay the foundations for optimising emerging technology tools and improve understanding of what such tools can achieve.

Expanding the scope of data

With regulatory requirements and client expectations in sustainability reporting continuously increasing, and with highly heterogeneous data availability and maturity, it is no surprise that data capture is currently a top issue for global financial services leaders. However, alongside speeding up the data retrieval process from annual reports, emerging technologies can help build and improve sustainability databases using a wider variety of information points. This includes analysing newspapers and social media for corporate controversies, identify trends or assess sustainability issues within an industry or sector. So while the regulatory call for sustainability data and clear quantitative commitments is currently much higher than what is available, the capacity of ML and generative AI to widen the data lens will help to improve the range and level of data available.

The emergence of specialised players

While big tech companies have been quick to deploy emerging technology solutions for a range of industries, the arrival of new players is helping to fill the demand for specialised technology and software solutions that are more industry focused and easier to add to existing IT frameworks. Again, the choice of solution goes back to clearly understanding what sustainability disclosures are relevant to your organisation. It could be a specific solution that maps your net zero trajectory or a tool that focuses on measuring your carbon footprint for ESG scoring purposes. However, it should be remembered that along with the benefits of building up a strong sustainability database, there are constraints and costs. Specifically, increasing levels of plug and play software are likely to require organisations to conduct a rationalisation initiative at some point to minimise the risks of IT overload, solution duplication and costs.

Securing data transparency

The urgent need to disclose and report on sustainability issues is currently pushing financial services organisations to fill any gaps by sourcing external data capture solutions. However, the need to have a clear audit trail from ESG scoring to reporting for regulation or assurance purposes going forward will require financial services organisations to build and adapt their own internal data capture capabilities to achieve the level of transparency demanded. Having a high level of data ownership and transparency will strengthen collaborations to develop customised model scenarios using deep learning solutions.

Can emerging technology help mitigate greenwashing?

While emerging technology cannot prevent greenwashing, it can provide a spotlight on how to minimise greenwashing. Looking forward, the use of ML and generative AI will, for example, enable an organisation to analyse trends and patterns that identify the propensity and risk of greenwashing. However, at present the complex nature of sustainability statements and disclosures means human interpretation and intervention is still necessary to avoid the risk of greenwashing. Getting the right balance between emerging technology and human oversight also applies to other aspects of sustainability reporting. In particular social indicators, which are less mature and involve subjective influences that are more difficult for ML and AI alone to understand and develop appropriate models.

Looking ahead

First and foremost, the need to collect good quality data on sustainability issues that can stand up to regulatory challenge and audit assurance is an imperative, particularly as sustainability ratings are set to become as meaningful to investors and stakeholders as financial ratings. Certainly, the use of emerging technologies is helping financial institutions improve their ability to collect and analyse data. The next step will be to ensure model solutions that gather, score, stress test and analyse sustainability information enhance the ability to disclose and report.

What is clear is that complying with ESG targets and sustainability-related disclosures can only be achieved by understanding your reporting obligations and applying emerging technology accordingly.