Artificial intelligence (AI) is playing an increasingly significant role in transforming underwriting processes. A recent Capgemini report shows that 62% of executives believe AI enhances underwriting quality and reduces fraud. Furthermore, 43% of underwriters say they trust and regularly use automated recommendations from predictive analytics tools.
ICAT, a company focused on underwriting solutions, is leveraging AI and data science to further evolve property risk assessment. According to Chief Underwriting Officer Robert Klepper, ICAT now prioritizes location-level data across residential and commercial properties. This shift enables more granular and dynamic risk evaluations.
Data Overload and the Challenge of Structure
With increased access to data, the issue isn’t lack of information — but how to manage it. Desk underwriters in particular face difficulties structuring vast data sets in a meaningful way. ICAT addresses this challenge by incorporating both passive and dynamic data streams. Passive data includes satellite and aerial imagery, while dynamic data sources stem from on-site inspections and sensor technologies.
Aerial Imagery and AI Interpretation
Aerial imagery has emerged as a particularly impactful tool. According to Klepper, AI can interpret risk characteristics within this imagery that might not be visible even during a physical inspection. For example, AI models can detect roof conditions, yard storage, or proximity to potential hazards like chemical storage. These insights can be derived without ever stepping on the property.
Another key benefit is the ability to observe conditions over time. Timeline analysis of imagery enables underwriters to detect changes such as new exposures, added equipment, or evolving environmental risks. This longitudinal approach adds a new dimension to risk modeling.
Natural Language Processing and Real-Time Insights
AI applications extend beyond visual analysis. Natural language processing (NLP) helps organize large volumes of textual data, enabling underwriters to prioritize relevant insights more efficiently. This structured approach frees underwriters to focus on strategic decision-making.
Sensor technology further enhances real-time awareness. For example, water or heat sensors can detect anomalies before they result in a claim. Post-event analysis also benefits from rapid access to updated aerial imagery, helping claims teams identify heavily affected areas and respond more efficiently.
Human Brokers Still Essential
Despite advances in automation, ICAT emphasizes that human brokers remain vital to the insurance process. Brokers provide critical context that complements structured data. According to Klepper, a deeper understanding of a client’s specific risks enables better-informed insurance decisions. This alignment between broker and underwriter ensures consistent, accurate pricing based on actual—not assumed—risk conditions.
Complexity in Today’s Risk Landscape
The underwriting environment is becoming more complex, particularly for large commercial accounts. According to Klepper, factors such as inflation, supply chain volatility, commodity prices, and heterogeneous physical site conditions make risk assessment increasingly difficult.
In 2024, U.S. insurers paid approximately $140 billion in claims related to natural disasters, according to Munich Re. The total economic cost reached an estimated $320 billion. Events such as wildfires are revealing new exposures not fully captured by traditional models. While wind and earthquake risks are generally well modeled, flood and wildfire risks demand additional data and more advanced analytics.
Future Outlook
As underwriting continues to intersect with AI, geospatial analysis, and sensor technology, data enrichment and improved model inputs are becoming essential. ICAT’s approach highlights both the challenges and opportunities of integrating advanced technology into underwriting workflows. The ultimate goal is not just automation, but better-informed decisions backed by precise, timely, and comprehensive data.
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