AI and big data

With more than 2.5 quintillion bytes of data generated every day, only artificial intelligence can process it effectively enough to allow ESG investors to be more objective.

The financial industry is shifting its focus towards sustainability. In his 2021 letter to CEOs, BlackRock CEO Larry Fink announced that the institution will immediately stop investing in companies that “present a high sustainability risk” and that it will make sustainability an integral part of its investment strategy.

Sustainability is also high on the agenda of many commercial banks – such as Barclays, HSBC, Morgan Stanley and Santander, among others – which are increasingly implementing initiatives to align their financing portfolios with the 2050 global climate targets.

However, to fulfil their commitment to sustainability, financial institutions need to address the sustainability data gap. Due to a lack of objective and standardised sustainability information, financial institutions have been facing troubles measuring the environmental, social, and governance (ESG) performance of their portfolios.

Artificial intelligence (AI) might be the way forward. After all, in a world where more than 2.5 quintillion bytes of data are generated every day (a quintillion has 18 zeros), large amounts of ESG information is already there, although often unstructured. AI could analyse all of this unstructured data, allowing investors and banks to analyse ESG data that are more granular and objective.

AI and alternative data

Every day, a tremendous amount of data is generated through social networks, news websites, blogs, and many other sources. Financial institutions can use natural language processing – a field of AI specifically aimed at understanding and deriving meaning from human language – in combination with big data analytics to scan large amounts of web sources in almost real-time.

In doing so, they can discover ESG risks and opportunities at an early stage. For example, the data might show that a borrower is investing in autonomous delivery to reduce its carbon footprint, but they might also show that one of its suppliers has consistently been violating human rights. TruValue Labs, a San Francisco-based company, already uses a combination of natural language processing and big data analytics to provide ESG insights into more than 16,000 listed companies.

Dutch investment firm NN Investment Partners has also started using natural language processing, big data analytics, and machine learning. But instead of analysing written text, the firm analyses speech in company conference calls. This allows them to better capture a manager’s attitude towards ESG.

Using AI to analyse content to capture the beliefs and sentiments towards a firm’s ESG performance… has major potential for applications in sustainable financing

This process of using AI to analyse content to capture the beliefs and sentiments towards a firm’s ESG performance is called ‘sentiment analysis’, and it has major potential for applications in sustainable financing.

But the use of AI in sustainable financing goes much further. The variety of data sources financial institutions can leverage through AI is huge.

UK start-up Cervest, for example, combines satellite imagery and other environmental observational data with AI and machine learning to model climate risk. Investors and banks can use this to effectively incorporate climate risks in making their financing decision. Satellite imagery can also be used to model an organisation’s direct environmental impact by evaluating deforestation and river pollution.

A firm’s societal impact, on the other hand, can be determined using telecommunications and demographic data. Using data aggregated from telecommunication providers, Distilled Analytics, a Boston-based company, was able to show the increase in local gross domestic product associated with firm investment. 


Small and medium-sized enterprises (SMEs) account for more than 95% of all businesses worldwide and, consequently, for the majority of social and environmental impacts. Since many SMEs are private and rely on banks and private equity investors for financing, banks and private equity investors can have a considerable impact by holding their portfolio companies accountable.

However, the data quality problem is even worse for private firms, as they are typically smaller and have fewer resources to conduct sustainability audits. AI models now provide a way for banks with large SME portfolios and private equity investors to also measure the sustainability performance of their portfolio companies. After all, private firms get media coverage too, and satellites do not discriminate between listed and non-listed firms.

Overall, AI and big data analytics are powerful tools that can aid financial institutions in making their financing decisions. They are fast, provide an almost real-time analysis and help elicit more objective information by combining multiple data sources. Using AI, therefore, decreases the uncertainty inherent to sustainable finance.

By buying a minority stake in Clarity AI, a tech platform that uses big data and machine learning to provide sustainability insights, BlackRock recognises the importance of AI in helping it deliver on its promise of becoming more sustainable. As a leader in the industry, BlackRock is setting an important example and driving the transition towards AI for sustainability. Both the technology and the data are already there, and it’s likely that many other financial institutions will quickly follow.

Bjarne Brié and Kristof Stouthuysen are members of the Centre for Financial Leadership and Digital Transformation at Vlerick Business School in Belgium.


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