1
Achieved 85% accuracy on unseen data, with an MCC score of 71.
2
Reduced probability of lapsing from 16% to 7%.
Predict policy level lapses for insurance provider
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:
-
We created a data mart that combined demographic data with policy characteristics.
-
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.
-
The team also designed other models based on Kernel-based SVM Regression and Tree-based Regression techniques.
-
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
Related Case Studies
Optimize payments made on third-party insurance claims
