We are entering a new era of artificial intelligence-led innovation. In emerging markets, one of the potential benefits of AI is that it can reduce barriers to climate finance by expanding opportunity sets, improving risk-return profiles, lowering transaction costs, and supporting quicker and better climate risk management and governance. But investments and partnerships are needed now to avoid the potential downsides of and actively drive greater inclusion and responsible data management in emerging markets.

AI tools increasingly support, simplify, and in many cases automate decision-making in the private sector, driven by the growing abundance of digital data, as well as expanding and more affordable data processing capabilities. Recent advances in AI suggest that these tools, already present in the economy, will become widespread. Generative AI could support global GDP growth by up to $7 trillion by 2032.

A significant portion of this growth will originate from financial services. In developed economies, investors already apply AI tools to accelerate decision-making, lower operating costs, and improve portfolio and risk management. Bottom-line growth will continue driving adoption: AI tools could unlock up to $1 trillion in additional value to global banking from cost-efficiencies in pipeline building and customer management, as well as from better credit underwriting, portfolio risk management, and fraud prevention, according to McKinsey.

In emerging markets, AI tools may be particularly useful due to their ability to analyze incomplete, small and unstructured datasets. We see the potential for AI tools to reduce investment barriers to climate finance in particular by improving the risk-return profile of investments and expanding the opportunity set, cutting diligence and supervision expenses, speeding climate transactions, and supporting better climate risk management and governance. 

Given their ability to add value in the financial sector, these tools can help reduce barriers to climate finance in several ways:

AI tools support more efficient pipeline building for climate-related investments and more accurate and efficient climate due diligence, finding investment opportunities that others would miss. “Agtech” firms, such as FarmDrive in Kenya, Cropin in India, and Creditas in Brazil,  use AI tools to analyze crop, yields, and farming practices, as well as third-party climate data (including from satellites) on weather patterns, soil moisture, and other climate data to develop more accurate credit scoring models shared with banks. 

AI-driven investment analysis supports better-designed investments to address climate risks and opportunities. In Indonesia, KoinWorks, a peer-to-peer lending platform, connects small businesses with local banks and has catalyzed more than $1 billion in small business financing since 2015.^[4] KoinWorks’s success rests on an AI platform that uses broad data sets, including climate data, to evaluate a borrower’s creditworthiness and then adjusts the loan size and interest rates to borrowers’ climate risk. Borrowers in areas with lower drought or flooding risks may receive larger loan amounts or lower interest rates, while borrowers in higher-risk areas may see loan sizes shrink unless they take action to mitigate climate risks.

AI-driven due diligence tools support faster and more proactive investment monitoring. Credit scoring models also support investment monitoring by helping adjust outstanding “value at risk” and accounting for evolving climate risks, by using data from early warning systems, for example. And they can help segment high-risk investees and preempt potential late payments.

AI tools support quicker responses to evolving climate risks and prompt risk management responses. Climate analytics firms have developed AI-driven services to help investors assess, manage, and reduce their portfolio climate risks by analyzing large amounts of climate-related data, including quantitative data (e.g., weather patterns, temperature trends, and sea level rise projections) and qualitative data (e.g., climate-related news articles and reports). These firms can then assess portfolio resilience to climate scenarios and estimate potential impacts on asset values. 

An added benefit is that AI tools have the potential to support expanded financial inclusion, with a climate lens. Some AI tools can bridge the gap with speakers of minority/Indigenous languages and offer natural language processing for visually impaired people.

Getting ahead of AI risks

To capture the full potential of AI for climate finance it will be important to anticipate and avoid foreseeable downsides and ensure explainability, limited inclusion, and good data management. New investments and partnerships will be needed to speed responsible adoption and develop industry-wide best practices and common approaches.

For starters, AI tools for climate finance in emerging markets need to be explainable and avoid discrimination. In general AI tools’ “limited explainability,” where it is difficult to understand the rationale driving their outputs and recommendations, remains a key challenge in the financial sector, particularly for regulated firms and given increased modeling complexity of climate data in these countries. 

AI tools’ limited explainability can also result in bias or discriminatory investment practices against marginalized groups, minorities, or Indigenous people. 

Financial AI tools will need to incorporate improved data visualization and traceability, with adjustments tailored to less reliable and incomplete data in emerging markets and more complex climate data. To ensure that AI tools are used to be as inclusive as possible, private investors should work with public sector counterparts to frame best practices to jointly address AI tool explainability for climate data and increased financial inclusion.

AI tools also need industry-wide policies that can govern and certify tools and their responsible use of climate data. While there is a growing number of partnerships to help financiers access relevant borrower data, these efforts are often hobbled by limited or unclear regulations and standards in many emerging markets around data ownership, privacy, protection and sharing. The growing range of public and private sources of climate data and their potential uses will compound this challenge. 

Investors should explore partnerships to set industry-wide policies that can govern and certify climate AI tools and their use of data for finance – as currently being proposed for the broader AI community – at least until policymakers develop legal frameworks for oversight. 

Innovation in AI holds the promise of accelerating climate finance in emerging markets, if properly directed. While it is early days, it is possible to fulfill the promise of these AI tools to improve the ability of investors to identify new opportunities, manage risks, and allocate capital more efficiently to ensure a positive climate and social impact.

Manuel Bueno is director of climate finance at Abt Associates. Darius Nassiry is vice president and director at Climate Finance Advisors, a member of WSP. Both authors are writing in their personal capacities.