Policyholder Behaviour Analysis

What Do Customers Want?

What do policyholders want? And why do insurers care so deeply about this question?

In FY 2021-2022, Indian life insurers collectively earned a total premium of approximately 7 lakh crore rupees and earned an income of approximately 14,000 crore rupees. Can better policy design and feature fine-tuning help an insurer win an extra 1% market share (~ Rs. 140 crores) of this income pool? We think that they absolutely can!

Six life insurers recorded premium growth exceeding 20% yoy (three insurers exceed 30%). And while the pie is growing, the competition is heating up too. Policyholder Behavior Analysis is one of the highest value-generating data analytics projects that insurers can undertake. 

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? 

We have listed below the main drivers of policyholder behavior. These drivers, if studied properly, will enable the insurer to build a near-complete picture of their policyholders’ behavior. 

• Effect of surrender charge on lapse (surrender) rate 
• Level of shock lapse under various conditions. 
• Minimum interest guarantees
• Interest Rates and moneyness
• Effects of benefits and their utilization on surrender rate.
  • Income guarantees 
  • Partial Withdrawals availability and utilization
  • Interest gained
  • Renewal rates
  • Loan Utilization
• Fund elections and fund transfers
• Effect of Sales or Distribution channel on persistence.
• Effect of Age and Wealth of policyholders on withdrawals, surrender, and benefit elections.
• Rate of Annuitization and its correlations
• Rate of Deferral of payout
• Free Looks and their correlations
• Death benefits and their correlations
• Flexibility in premium payment mode
• Flexibility in payout
• Variations by policy cohort (year, tax regime, interest rate/macro environment)

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. 

Coming back to the question that we asked at the beginning – is 1% possible?