How Insurance Companies Use Data Risk Scoring Models
Insurance companies increasingly rely on data risk scoring models to assess client profiles, detect fraud, and manage underwriting risks more accurately. These models analyze large volumes of structured and unstructured data—such as credit history, claim records, geolocation, and behavioral patterns—to assign a risk score. This enables insurers to make smarter premium pricing decisions and reduce potential losses. With cyber threats on the rise, insurers also use these models to evaluate the cybersecurity posture of businesses before offering cyber insurance. As digital transformation accelerates in the insurance sector, mastering data-driven risk scoring has become essential for improving profitability and customer trust.