Using data modeling and predictive analytics, we assisted a life insurer to understand the lapse behaviour of its customer base in its protection business. This work enabled the company to improve its cashflow projections for its existing book and target higher-quality customers in its marketing campaigns.
A life insurer had a higher-than-expected lapse rate and wanted to understand what the key drivers of lapse are in their book.
By applying survival analysis techniques, we predicted expected lapse probabilities for each policy, allowing us to discover the effect of policy characteristics on the lapse rate, and how those effects change over time.
- Visual exploration of policy data
- Thorough data validity and integrity checks
- Identification of key drivers of the policy lapse rate
- Per-policy prediction of expected lapse rate
- Improved quality of business through targetted marketing for new business
- Key Lapse Drivers
- Gender of the primary policyholder
- Smoking status of the primary policyholder
- Coverage type of the policy
- Socio-economic category based on address of the primary policyholder
- Age of policyholder at inception
- Source of business (broker, IFA, etc)
- Key drivers of lapse changed over lifetime of the policy.
- The credit crisis affected policy lapse from 2008 to 2012
- Policies with a large sum assured need separate treatment
KEY CLIENT BENEFITS
- Data quality and integrity rigorously tested
- Improved cash-flow projections for the in-force business
- Improved quality of independent broker business
- Identification of profitable policy cohorts for future marketing
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