Prevent Policy Lapse Proactively

Prevent Policy Lapse Proactively

Persistency is a key driver for successful insurance business. You just cannot let your existing customers churn; proactive alerts and actions are necessary to address policy lapse.

GrayMatter’s Center of Excellence for Data Science (GMCoE-DS) is focused on predictive insights driven value for businesses. Insurance lapse prediction is one such typical use case which the CoE has expertise in handling. How do the experts do this?

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The typical solution approach is to devise a logistic regression model to predict the likelihood of lapse of policies. There are several data points that go in as inputs to this model like the following:

  • Customer Demographics – Gender, Age, Race, Income, Nationality, Marital Status
  • Customer Interaction mode and frequency with company – Email, Phone, others (fax, letters)
  • Number and type of insurance products customers have bought from the company
  • Policy details – Agent, Sum Insured, Premium, Term
  • Each event for the policy – Inception, Lapse, Claim, Reinstatement, Cancel, Surrender, Mature

The model output helps in predicting whether a certain customer profile is likely to lapse or not. It also provides indicators on the significant factors impacting lapse, for example, Age, income, channel of customer interaction etc that can help you take focused actions.

So go ahead and address your policy lapse issues now!

2018-02-21T09:21:49+00:00

About the Author:

Anupam Dasgupta