Predict policy level lapses for insurance provider


 Leading South East Asia-based insurance provider catering to small and medium businesses


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.


SGA decided to design a classification model to predict probability of lapse, using the following approach:

  • SGA created a data mart that combined demographic data with policy characteristics
  • SGA developed a logistic model to predict the probability of a customer lapse on premium payments using predictors such as age, gender, marital status, income, household size, mode of payment, frequency of payment, timeliness of payment, etc.
  • The team also designed other models based on Kernel based SVM Regression and Tree based Regression techniques
  • Finally, the SGA team calculated the lifetime value index for all the customers. Based on the lifetime value, SGAidentified the customers that should be targeted for business growth.


  • Achieved 85% accuracy on unseen data, with an MCC score of 71
  • Reduced probability of lapsing from 16% to 7%


Achieved 85% accuracy on unseen data, with an MCC score of 71.
Reduced probability of lapsing from 16% to 7%.