Reduce Policy Lapse

– Predicted policyholders likely to lapse at an individual level – Identified specific drivers of lapsation – Provided early warning indicators for risky policies For this general insurance company, iTuring built a Lapse Prediction Model for Fire Insurance, supporting a more customer-centric retention strategy. The renewal team used model outputs to: – Identify high-risk policyholders with clear reasons for lapsation – Design targeted treatment strategies – Focus on only the top 20% most at-risk policyholders This approach helped retain 16% of policyholders who were likely to lapse in the coming year. To further improve loyalty, the insurer used policyholder-level risk causation metrics to: – Understand the true cause of lapsation – Provide dedicated representatives for high-risk policyholders – Develop property-level contact strategies As a result, the business reduced erosion in risky policies by 9% with 93% model accuracy.