Underwriting Decision Support System for a Commercial Lines Insurer
Commercial lines insurer, Midwest US
01 / Challenge
Commercial underwriters were spending 65% of their time on data gathering, spreading financial statements, and researching industry risk factors — leaving only 35% for actual underwriting judgment. Loss ratios in three book segments were trending above target, and the underwriting team suspected data quality issues in their risk selection, but had no systematic way to identify them.
02 / Approach
We built an underwriting decision support system that automates data aggregation from 12 internal and external sources, generates structured risk profiles with loss ratio trend analysis by segment and underwriter, and surfaces comparable account loss experience to inform pricing. The system tracks each underwriter's book performance to identify risk selection patterns.
03 / Outcome
Underwriter data gathering time fell from 65% to 18% of working hours. Three loss-ratio-challenged segments were identified and repriced, producing a 6.2 combined ratio point improvement within two underwriting cycles. Premium volume per underwriter increased 34% with no additional headcount.
Representative case study illustrating common agentic-AI deployment patterns in Insurance; not a specific QuettaMinds client engagement.
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