Reduce Policy Lapse

Reduce Policy Lapse

The Challenge

There are many different reasons why insurance policies lapse, and it is very difficult for insurers to know which policyholders are likely to lapse and to determine the actual cause of lapse. To a reasonable extent Insurance companies try to determine the cause of lapse to reveal more obvious reasons such as under-insurance or over-insurance, but AI driven intelligence can predict which policyholders are likely to lapse and what could be the specific cause of lapse at individual customer level. Insurers have started capturing granular data but struggle due to complexity of different data sources and ability to leverage it to drive business decisions.

About the Company

The Solution

With iTuring, you can build extremely accurate lapse prediction models to identify which policyholders are likely to lapse and proactively identify potential policy lapsation. iTuring will not just help you predict risky policyholder but also provide causes of lapsation with detailed information and help you build an appropriate strategy to prevent revenue leakage.

For a leading General Insurance company in India, CyborgIntell used iTuring to build “Lapse Prediction for Fire Insurance” to prevent revenue leakage and develop a customer centric strategy for retention and improved customer experience. Assist the renewal team in identifying potentially risky customers with driving factors leading to a lapse and support their treatment strategy to retain them. Business retained 16% policyholders who were likely to lapse in the coming year, analysing just top 20% policyholders with high probability to lapse.

To improve loyalty, Insurer started using iTuring developed policyholder level risk causation metrics, to understand actual cause of lapsation for all risky policyholders. Developed treatment strategy to not just retain them but also provide dedicated representatives for superior customer experience. Insurer developed property level contact strategy and saved 9% erosion in their risky policy with 93% model accuracy.

Highlights

Impact

Revenue Leakage

0 %↓

Time to Deployment

0 Weeks

Customer Retention

0 % ↑

Why CyborgIntell

Not only does iTuring provide highly accurate predictions of which policyholders are likely to lapse, but also provides early warning indicator on policyholders who are likely to be risky and provide reason of risk at policyholder level.

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