Smart finance solutions: Leveraging AI for climate finance and risk management

Artificial intelligence (AI) can contribute to finding innovative solutions for climate finance, 

climate risk management, and insurance

Artificial intelligence (AI) can contribute to finding innovative solutions for climate finance, climate risk management, and insurance and enable the development of transformative financial instruments and tools. As the technology progresses, there are emerging use cases for AI to enhance climate finance in terms of speed, efficiency, transparency, and equity. However, the potential role of AI for finance and risk management is complex and not without challenges, which need to be carefully considered before any large-scale implementation or the introduction of innovative applications.

Data management and analytics

AI-driven solutions present significant opportunities to enhance processes and instruments related to climate finance, risk finance, and insurance. For example, AI can significantly improve the accuracy of climate change projections and climate risk assessments by analysing vast datasets on weather patterns, recorded impacts, and other relevant factors. By improving the precision of risk modelling, AI can inform financial instruments, ensure that they reflect ground realities, facilitate informed investment, and enhance the evidence-based design of relevant triggers and clauses in insurance or loan agreements.

However, it is important to note that an effective application of AI for data management and analytics is heavily dependent on the quality and availability of data and methodologies. In many cases, especially in developing countries, there is a lack of reliable, comprehensive, and/or centrally available data on climate risks, financial flows, and disaggregated household characteristics. Gaps in this regard might hinder the ability of AI to generate accurate predictions and assessments, potentially limiting its utility in these contexts.

Innovative financial tools and products

Beyond analysing and managing complex data patterns, AI can also directly aid in the development of innovative financial tools and products. For example, this could be customised insurance solutions that are tailored to the specific needs of individual households or small businesses with minimal transaction costs, bundled with advisory or risk management functions that not only provide a financial safety net but also help to minimise existing risks. Another potential solution could revolve around pricing and structuring green bonds or climate resilience bonds, which fund projects that have positive environmental outcomes or enhance climate resilience.

Personalised banking services; financial advisory; automated claims processing; dynamic pricing models; precision agriculture financing platforms; AI-guided ecosystem restoration funds; enhanced customer support; more accurate underwriting processes; smart microfinance products; or regulatory compliance monitoring are further examples of tools and products that can benefit from the ability of AI to customise user experiences, utilise a wider range of data points and formats, and process data quickly and efficiently with full-time availability.

Local-level access to finance

With the upcoming operationalisation of the Loss and Damage Fund under the UNFCCC and other global funds—such as the Green Climate Fund, the Global Environmental Facility, or the Adaptation Fund—, it is important to look at local-level access to finance and how global funds can directly connect to vulnerable communities on the ground. In this context, AI has the potential to enhance local-level access to financial resources for climate action by automating and streamlining the process of identifying, assessing, and funding projects. AI could reduce the barriers to entry for smaller projects and communities that traditionally lack access to finance, including those led by indigenous communities or small-scale farmers, and match them with appropriate funding sources based on their specific needs and impacts. To achieve this, it is essential to incorporate principles of equity and climate justice into AI-driven climate finance mechanisms. This involves designing AI systems that explicitly aim to address the disproportionate impacts of climate change on marginalised communities and ensure that these communities have equitable access to finance. AI can aid in identifying the most vulnerable populations and the most effective interventions to support them, but this requires the integration of social and environmental justice considerations into AI models, as well as equitable access modalities that are usable by local-level stakeholders and available in local languages. Moreover, design and governance of AI for climate finance should be inclusive and based on multi-stakeholder engagement to ensure that the benefits of AI are distributed fairly and do not exacerbate existing inequalities.

Moving towards the future

Fully or partially AI-based solutions offer opportunities as well as challenges for the areas of climate finance, risk management, and insurance. It is important to facilitate an open conversation on aspects such as data availability, accessibility, interpretability, bias, transparency, equity, inclusivity, and climate justice as the field continues to evolve. Navigating the practical and ethical complexities of AI for climate finance will likely be a vital part of policymaking, planning, and research and development processes in the near future to ensure that the technology is used to its full potential and contributes towards achieving a sustainable, equitable, and climate-resilient future.

(The writer works as Director: Research and Knowledge Management at SLYCAN Trust, a non-profit think tank based in Sri Lanka. His work focuses on climate change, adaptation, resilience, ecosystem conservation, just transition, human mobility, and a range of related issues. He holds a Master’s degree in Education from the University of Cologne, Germany and is a regular contributor to several international and local media outlets.)