From Models to Data at the Center: A Paradigm Shift in Flood Modeling for Insurers
Climate and weather extremes are intensifying. In 2024 alone, the United States witnessed 24 weather and climate disasters exceeding one billion dollars in damages, with floods and water-driven catastrophes (e.g., hurricanes) making up a large share of these losses. According to the National Oceanic and Atmospheric Administration (NOAA), both the frequency and the financial toll of flood events have sharply increased over the past few decades.?
Much Greater Risk—Much Greater Insights
Yet, alongside rising risks, our capacity to analyze these threats has surged as well. Computing power has grown exponentially, thanks to advancements in parallel processing, cloud computing, and artificial intelligence (AI). Meanwhile, remote sensing via high-resolution satellites and geospatial innovations (think: free-to-use mapping services and real-time data feeds) has opened entirely new avenues for understanding flood drivers. The question is: How does all this potential translate into better flood-risk insights for insurers??
The question is: How does all this potential translate into better flood-risk insights for insurers??
Breaking Away from Traditional Models?
Historically, flood predictions often relied on process-based simulations—often CPU-intensive, especially when integrating complex sewer or river systems across large regions. While these methods hold value, a paradigm shift is underway: increasingly, experts advocate a data-centric rather than a model-centric approach (Zolghadr-Asli et al., 2024).?
In simpler terms, the new thinking is that high-quality, rich data can outperform or at least significantly enhance the predictive power of overly intricate models. AI and machine learning (ML) algorithms excel at detecting patterns in large datasets—even if the explicit, hand-coded processes of older models become secondary. However, many insurers remain anchored in traditional methods, which often overlook the abundant, high-resolution geospatial data now available. This reliance on outdated approaches risks missing out on more precise risk insights.?
Why Data-Centric Flood Modeling Matters for Insurers?
The shift is important to insurers namely in the property space with these factors being chief drivers:
Sharper Risk Assessment; a data-focused perspective integrates historical claims, real-time sensor data, and satellite imagery to yield more accurate underwriting and pricing strategies. By spotlighting where and how floods are likely to occur, insurers can set premiums that better reflect actual risks.?
Enhanced Predictive Analytics; data-centricity emphasizes data quality and relevance, boosting predictive models. Insurers benefit from better forecasting of claim frequency and severity, reducing the risk of unexpected losses—and helping ensure adequate reserves.?
Climate Change Readiness; Flood events are intensifying. Traditional models may struggle to keep pace with rapidly evolving climate patterns. With a data-centric strategy, ML and AI tools can be continuously updated as soon as new information (e.g., precipitation shifts, altered land use) becomes available, keeping flood models more reliable over time.?
A Gradual, Yet Accelerating, Transformation?
Although data-centric approaches offer clear advantages, the transition has been building slowly. Many insurers historically saw flood risk as marginal or handled it using simplistic models. However, in other areas—such as claims automation or fraud detection—insurers have long embraced data analytics. That is beginning to spill over into flood modeling. A simplified timeline:?
Increasing evidence suggests insurers who stick with basic, model-centric flood tools risk second-best outcomes. By contrast, those who integrate fresh, comprehensive datasets—from property-level claims to satellite-based terrain analyses—can dramatically improve how they detect, price, and hedge flood risks.
Positioning for the Future
For P&C insurers and reinsurers, the message is clear: harnessing data-centric flood modeling translates into
This paradigm shift in hydroinformatics aligns perfectly with the leaps in computational power and geospatial data—meaning that if ever there was a time to switch from purely model-centric to truly data-centric flood analyses, it is now. By making that leap, insurers can place themselves at the forefront of risk intelligence, ready to meet the rising challenges of water-related disasters head-on.
Sources: Zolghadr-Asli B, Ferdowsi A, Savi? D. A call for a fundamental shift from model-centric to data-centric approaches in hydroinformatics. Cambridge Prisms: Water. 2024;2:e7. doi:10.1017/wat.2024.5