Improve Revenue Projections by Predicting Premium Payment Suspension

Case Study

Improve Revenue Projections by Predicting Premium Payment Suspension

Business Objective

Our client is one of the largest life insurance companies in the United States. The client was interested in improving short and long-term revenue projections by determining how likely a policyholder was to have his or her policy suspended due to unpaid premiums.

The specific objectives were:

– Predict the probability of policy suspension ascertained from 24 months of unpaid premiums

– Determine factors that drive policyholders’ decisions to suspend premium payments

Challenges

  • Multiple data sources (including external data, longitudinal data)
  • Data used had limited metadata, sparse data dictionaries, and ambiguous business contexts for certain elements

Solution Methodology 

  • Developed hypotheses about relationships between data and policy suspension, using
    • Macroeconomic data
    • Characteristics and behavior of financial professionals
    • Policyholder demographics and socio-economic data
    • Policy data such as issue dates, coverage amounts, loans, and riders
    • Transaction data such as counts of premiums, premium values, and dates paid
    • Customer relationship interaction data from the client’s call center
  • Experimented with various under-sampling/over-sampling techniques to account for unbalanced response variable with only 6% of policies suspended during study period
  • Experimented with logistic regression, random forests, and other ensemble methods before settling on random forests for the final models
  • Gained substantial performance improvements from hyperparameter tuning
  • Achieved an overall accuracy of 98% – precision and recall both above 0.8 two quarters out and above 0.65 six quarters out

Business Impact

  • The final models provided additional information for actuarial models used for revenue projections
  • Nearly 85% of premium suspensions were identified correctly by the model
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