AI and Flood Prediction: Why Financial Firms are Hesitant

While artificial intelligence offers compelling solutions for flood prediction, implementing such systems in regulated financial environments reveals significant practical limitations. Financial institutions making investment decisions about climate resilience require transparent, explainable, and robust models enveloped by model governance and a secure risk supply framework.? The rush to marry AI with flood and climate risk assessment lacks these essential characteristics, explaining financial firms' hesitancy in adopting such solutions.

?Research papers showcase AI's potential to improve flood prediction through pattern recognition in vast datasets from satellites, weather stations, and river gauges. However, these theoretical improvements face fundamental challenges when confronted with the rigorous requirements of financial model governance. The "black box" nature of many AI models is not merely an inconvenience—it's a fundamental barrier to adoption in regulated financial environments where complete transparency and audibility are essential.

?The fundamental challenge with AI approaches lies in data heterogeneity and inconsistent collection and curation. Current AI models treat all data sources equally - an approach that needs to acknowledge vast data quality and reliability differences across regions and measurement systems.

Consider satellite data's varying resolution from 10 to 100 meters. AI models treating these different resolutions as equivalent need revision. Google's laudable attempt at flood prediction in Kenya, based on a single river gauge, perfectly demonstrates this challenge. The credibility of any forecasting system is stretched regardless of AI sophistication.

The heterogeneity of data sources - satellite imagery, weather stations, river gauges, terrain models - requires understanding each source's limitations. Traditional hydrological models account for these differences through physical understanding. In contrast, AI approaches attempting to learn these relationships directly from data can produce impressive results in data-rich environments but fail when data quality vanishes in remote regions.

?The gap between theoretical AI models and practical financial sector implementation reveals a crucial misunderstanding about value creation in risk modelling. While academic literature focuses on novel algorithms, financial institutions have long recognised that their intellectual property lies not in theoretical models but in robust implementation.

?Financial institutions that implement pricing and risk models reliably and securely outperform laggards. The same principle must apply to flood prediction models if they're to be incorporated into financial risk assessment frameworks.

?Financial institutions have developed sophisticated data validation and quality assessment processes, refined over decades and subject to regulatory scrutiny. These aren't merely technical procedures; they represent accumulated knowledge about how data quality affects decision-making. Any new flood prediction system must meet these same stringent standards.

?Successfully integrating flood prediction models into financial risk assessment requires the same rigorous implementation approach banks have developed for their existing models. The intellectual property lies not in the theoretical models but in implementing a risk supply framework that delivers robust data handling, calibration, integration, and governance processes.

?Until AI researchers in academia and NGOs recognise this fundamental truth about financial sector implementation, their models will likely remain largely theoretical rather than practical tools for financial decision-making.