Policyholders make decisions regarding the exercise of benefits and guarantees within their contracts. Insurers can gather all such data, in addition to choices policyholders make related to the purchase and utilization of their policies. Insurers can also obtain data from their sales, marketing, and distribution channels to understand the trends underlying the sale of their policies. Analysis of this data to identify underlying patterns and factors that drive changes in those patterns is called policyholder behavior analysis. So, where should the insurer begin and what can the insurer achieve by studying their policyholders’ behavior.

Minimize Lapse Rates

Minimizing surrenders is one of many goals of policyholder behavior analysis. Most annuity issuers pay significant sales commissions upfront and hence need the annuity policy to persist for at least a few years for it to become profitable. Lapse rates, higher than expected, can also dramatically change the profit outcome of a reinsured book of business, especially when the reinsurer has reimbursed acquisition costs (via ceding allowances) and has little control over credited rates. 

While there are many generally observed patterns (listed below), gleaned from industry data, patterns themselves are not sufficient. What product managers need is actionable insights. For instance, an insight such as ‘X % incentive will reduce surrenders by Y % and increase ROE by Z %’. 

What correlates with lower lapse rates?

  • Higher credited rates relative to competitor rates
  • Large account values
  • GLWB Rider
  • GLWB Rider Utilization (Consistent and limited, penalty-free withdrawals)
  • High participation rates linked to Smart Beta indices

Translating ‘obvious’ correlation into causality requires extreme caution. Moreover, translating causal relationships or correlations into actual product enhancements and price incentives requires high-precision measurement of the company’s own experience data. 

Other drivers of policyholder behavior include a) Sales / Distribution channels especially with respect to free-looks, b) Rider availability and utilization, c) Age and wealth of policyholders. d) Availability and election of index, e) Flexibility in transfers , etc. 

By understanding these factors, insurers can optimize the core policy parameters that include design, pricing, promotions, and feature set of their policies. These policy parameters can be optimized to better fit the needs of their customers, so that insurers gain market share, and improve customer satisfaction and retention. Policy parameters can also be optimized to adjust the risk exposure of the insurer, such as by targeting the policy towards certain demographic profiles, or by tilting the risk exposure towards certain types of guarantees and away from others. 

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