Client
Leading South East Asia-based insurance provider catering to small and medium businesses.
OPPORTUNITY
The client wanted to develop a model to predict lapses at the policy level. The aim of the model was creating cost-effective interventions to target high-value customers with high likelihood of lapse.
SOLUTION
SG Analytics decided to design a classification model to predict the probability of lapse, using the following approach:
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We created a data mart that combined demographic data with policy characteristics.
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Our data scientists developed a logistic model to predict the probability of a customer lapse on premium payments using variables such as age, gender, marital status, income, household size, mode of payment, payment frequency, timeliness of payment, etc.
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The team also designed other models based on Kernel-based SVM Regression and Tree-based Regression techniques.
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Finally, we calculated the lifetime value index for all the customers. Based on the lifetime value, we identified the customers that should be targeted for business growth.
VALUE DELIVERED
1
Achieved 85% accuracy on unseen data, with an MCC score of 71.
2
Reduced probability of lapsing from 16% to 7%.
