Data and AI, key ingredients for Sustainable Finance

At a time when ESG data is the new gold, artificial intelligence (AI) has a key role to play in analysing and assessing data.

Planet Earth is on red alert, recent studies show. To fight climate change, companies are at different stages of their transition to net-zero. Regulations are being put in place in various jurisdictions, also at different paces. Sustainable finance is a key lever in decarbonising the economy and Environmental, Social, and Governance (ESG) data is a gold mine to best understand where clients, partners, suppliers and prospects stand in their net-zero journey.

The data curse

Sustainable Finance used to lack data. Now, we seem to have too much data, and the lack of standardisation makes it difficult to compare, so therefore less reliable and less valuable. The role of data is increasing but sourcing good data has been (and remains) a major challenge since 2017; the good news is solutions are now emerging, the recent BNP Paribas Global ESG Survey shows.

“[Putting] data at the heart of its sustainable finance strategy and [devoting] substantial resources to developing its data collection and processing capabilities, [as well as committing] to making it open source and publicly available” explains why Euromoney named BNP Paribas world’s best bank for ESG data and technology.

Collecting and storing data

To assess the maturity of companies in terms of sustainability, the first step is to collect ESG data: either with open-source data (e.g. published online like annual reports), by collecting it with the clients’ or prospects’ files, or by buying ESG data from external providers.

Once data is collected, it needs to be stored, preferably on a platform that gives a quick and easy access to each piece of data. This is where artificial intelligence (AI) comes in enabling to match data collected from different channels with one given client.

Ultimate goal: to display all ESG data available on a client or a prospect with a simple search on a dedicated platform. Such platform is key to build a complete database to share information between different entities accessing and using the platform. This allows a mutualisation of information and favours collaboration to provide one-bank advice and services for clients, and it helps determine if the bank has enough data on the client or if there is a need to buy data from an external provider.

Analysing data at a glance

Data accessibility is a good first step, but it is just the beginning. AI can process the data collected and assist relationship managers in getting a snapshot of the client profile: it highlights relevant information on the page and generates an ESG scoring for the client.

Relationship managers and risk analysts are able to process information more rapidly and to be more exhaustive in the analysis they make. For example, they can use this data in the credit committees to help the decision-making.

What’s next?

It is possible to imagine that this centralised, platform approach to sourcing and managing ESG data will be more than useful with the new regulations such as EU taxonomy, in which reporting plays a key role.

Analysing and processing ESG data is still very new and the context continues to evolve. There is a strong need to be agile and be able to adapt to this moving context. Along with growing regulations, open source offers a good solution to create standards so that companies share the same language when talking about sustainability.

Going further, we can imagine that artificial intelligence can be one of the keys to build the sustainable finance of tomorrow.

BNP Paribas and OS-Climate

On March 2021, BNP Paribas joined The Linux Foundation on OS-Climate as first founding European bank member and first European Premier member. This agreement aims at building a breakthrough Open Source Data and modelling solution for climate-aligned investing, finance and regulation. 

The project’s mission is to accelerate climate-aligned finance and investment by providing access to high quality, actionable data regarding climate-related risk and opportunity.