India's largest platform and marketplace for GCC & AI leaders and professionals

Sign in

India's largest platform and marketplace for GCC & AI leaders and professionals

3AI Digital Library

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

    3AI Trending Articles

  • AI Operations: Think Software Development, not Data Science

    Featured Article: Author: Kuntal Hansaria, Associate Partner – AI, Analytics & Digital, IBM AI Governance includes aspects of Explainability (explaining how a model is working) & AI/ML Operations (scaling model development, management & deployment). While Explainability gets lot of attention, aspects of AI/ML Operations are often ignored. However, without AI Operations, an organization can never […]

  • How Augmented Analytics is Transforming the Analytics Ecosystem

    Author:  Sidharth Sivasailam, Vice President – Products, Course5 Intelligence | LinkedIn – https://www.linkedin.com/in/sidharthsiva/ The world of Business Analytics is at an inflection point. Trillions of bytes of data are being generated every day; however, companies continue to struggle with harmonizing this data, analyzing the data of various shapes and sizes they are storing, determining what’s most […]

  • Transforming Customer Experience through Unified Business Functions

    Author: Souvik Das, AVP – Platforms & Offerings, Genpact Digital and Pramit Dasgupta,VP – Platforms & Offerings, Genpact Digital The recent pandemic has changed customer expectations and interactions in many ways. Forever. As organizations evolve through the new normal and gear up to satisfy their customers, there is a need to rethink and redesign how […]

  • Top four focus areas to help you shape a data-driven enterprise

    Author: Praveen Reddy, Vice President (Digital – data, analytics, and cloud) | Genpact As we navigate the world of data, some significant trends are manifesting with respect to data ownership and utilization. There are multiple triggers for these trends – ever-rising complexity of data, lack of proper intelligence concerning data assets, need for accelerated digital transformation, […]