When a major property and casualty insurance carrier experienced significant deterioration in one of its books of business, an investigation revealed that antiquated actuarial approaches were causing slow response times and ongoing performance issues. KPMG was asked to help rectify the problem with advanced analytics and technologies.
We conducted interviews with key underwriters, actuaries, product managers, claims handlers, litigation practitioners, and various executive stakeholders. Acting on this information, we built a machine learning model using claims and policy-level data to analyze the carrier’s loss of performance at a granular level. This enhanced view helped the client accurately analyze its profitability by class, state, and other dimensions not previously available for study. The increased transparency and enhanced output improved trust in the actuarial model and resulted in potential annual savings of tens of millions of dollars.
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