We’re continuing our discussion on emerging “green” finance offerings in this four-part blog series, with an emphasis on their proliferation and importance. In part one, we took a look at green finance and green bonds, and, in part two, we examined sustainability-linked loans (SLL) and sustainable supply chain financing (SSCF).
In this third blog, we’ll explore the relationship between green finance, ESG ratings, and artificial intelligence.
Leveraging the power of artificial intelligence
Finance is a powerful lever for motivating businesses to become more environmentally sustainable. Banks and other financial institutions are the backbones of SSCF, and they play a key role in catalyzing awareness and driving the uptake of their newest green programs and offerings.
Banks are working across their departments and business segments to improve the integration of environmental, social and governance (ESG) priorities into their core products and due diligence processes, including know your customer (KYC) processes. They’re also developing new green corporate scoring models, which blend both ESG and KYC data.
The increasing deployment of artificial intelligence (AI) is helping banks with these efforts by improving ESG data analytics. The key to success is unlocking insights from data to inform sustainable investment strategies and ESG performance ratings, both in the present and over the long term.
Rating a company’s ESG performance involves risk because ESG rating providers use different frameworks, measurements, metrics, data, and qualitative analysis. Despite their increasing use of ESG ratings over the past year, banks often lack the necessary tools to inform their ESG rating decision-making in an efficient and appropriate way.
In addition, a large portion of ESG data analysis covers non-financial elements of a company’s actions related to sustainability. Research also reveals concerns about the reliability of ESG and KYC data. Frequently there are gaps in data availability, data inaccuracies, and a lack of comparability across ESG providers. As a result, ESG rating analysis and consistency is a difficult task for banks, indeed.
AI can enable more informed ESG rating decision-making by limiting the subjectivity and cognitive bias that may stem from traditional analysis, reducing the noise in ESG data and making use of unstructured data. Specifically, natural language processing can be used to analyze massive amounts of unstructured datasets—think geo-localization and social media—to perform sentiment analysis and identify patterns and relationships in data.
The results of such analysis can be used to assign quantitative values to qualitative data for sustainability parameters. This measurable clarity represents the power of AI at its best.
Alternative uses of AI
In recent years, alternative ESG ratings providers have emerged, offering ratings based on AI with a view to providing a more objective, outside-in perspective of a company’s sustainability performance. Some experts predict that these alternative ESG ratings won’t replace traditional ESG ratings, but instead be used to complement existing knowledge about the buyer, supplier and customer.
In fact, the use of AI could be used by rated corporations themselves to further inform their ESG decision-making and strategic direction. Additionally, using AI to generate ESG ratings could help minimize “greenwashing” by corporations by uncovering less readily available information about their sustainability practices. This might be another context in which the power of AI is particularly valuable.
In our next and final blog in this series, we’ll discuss the future of green finance and how you can make the shift towards sustainability. In the meantime, if you’d like further information on this or other related topics, reach out to either of us (email@example.com; firstname.lastname@example.org).