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Minimizing Policy Attrition

3AI August 16, 2020

Leading Life Insurance Company In Japan

Problem Statement

  • A high attrition in the customer base due to competition and miss-selling (45% is overall lapse rate)
  • Expectation was to build an approach to retain policies and to set up an early warning system to identify a potential attrition cases to prevent further revenue leakage

Analytics Led Approach

  • Attrition trends in the data over the years were analyzed and an approach was designed to provide a solution to contain further revenue leakage
  • Predictive attrition model was built to predict the propensity of policies to attrite
  • Given are high level process steps which were followed :
    • Data Load Exploration
    • Model variable Creation
    • Analytical Model Development
    • Business rules

Business Impact

  • Raw data was mined, and a methodology was built for developing a predictive attrition model to provide a probability score to influence the decisions to follow the case by the customer.
  • Model output was segmented to optimize the approach to follow the case and provided insights to treat each policy based on the segmented matrix Re-evaluate;Retain;Grow;Protect– for example, Retain is the quadrant high value and high risky policy, retaining such policies will optimize the effort as well as cost
  • Primary users, retention team were provided with insights for customer retention..

Critical Success Factors

  • 20% of the in-force portfolio has been identified with 69% of the premium to be at risk of attrition (Approx. $ 1.3 BN premium at risk)
  • Formulated a growth strategy to retain existing customers as well as grow their business
  • Integrated attrition prediction with every customer touch point system through a web service

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