Case Study Leveraging an insurance customer retrieval solution to reactivate lapsed accounts
Client: Leading insurance company Industry: Financial Services Business Impact: • Comprehensive analysis • Account restoration • Improved ROI
Business Challenge The client faced an immediate challenge of accessing lapsed insurance policies with a potential of repayment within a specific time bracket. By identifying these accounts, the company can focus on their reactivation, which will result in an additional flow of revenue. The client was witnessing revenue loss and hence wanted to reactivate the lapsed insurance policies. However, while doing this, they wanted to ensure that the strategy is resulting in minimum wastage of money and effort.
Solution Blueocean Market Intelligence customized the insurance customer retrieval solution to address this particular problem. The two policies namely, Traditional and ULIP, were in two states - Inforce and Lapsed. • As a first step, the lapsed policies, having the potential of repayment by invoking inforce (customers with a payment profile > three years) attributes on the defaulted policies, were accessed • A binary logistic regression was utilised on lapsed and inforce datasets and a KS cutoff was decided based on the model results after which confusion matrix was built consisting of all the four wells
• The error pertaining to two wells in the matrix told about the reinforcement of attributes of inforce on lapsed policies and vice-versa
Traditional Policies Result Predicted
ULIP Policies Result Predicted Lapsed Actual
Factors like premium to be paid, income of the policy holder, occupation and the total sum assured at the end of maturity were found to be greatly affecting the model results. The confusion matrix shows a pictorial representation of the hypothesis used, where in 2827 and 8435 policies can be possibly retained in traditional and ULIP plans respectively. The above table also gives us the percentage correctness of the model, 74.9% and 64.7% for Traditional and ULIP policies respectively.
Outcome Factors that affected the predictive model were identified as: • • •
Premium to be paid Income of the policy holder Occupation and the total sum assured at the end of maturity
The study suggested it is always good to approach the lapsed policies within a specified time bracket after which the policies may get permanently lapsed. Based on our analysis, approximately 11,000 policies have been targeted from a portfolio of 50,000 and 8,000 have been successfully repossessed.
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