Xerago helped one of India's growing life insurance companies build a model to identify potential early claimers in advance, allowing for more investigation prior to offer of insurance.

Xerago helped the client in building a model to identify potential early customers

The client is one of India's growing life insurance companies. It is a three way joint-venture of an Indian development and commercial bank; one of India’s leading private sector banks and, a multinational insurance giant based out of Europe.

The client was looking for a robust automated solution to identify potential early claimers in advance, so that more investigation could be carried out before providing them insurance. In addition to this they also wanted to build a robust automated solution to identify potential non-persistent customers in advance, so that targeted campaigns could help retain them

Challenges

  • Client had never used predictive modelling earlier and hence lot of efforts were required to make business units understand the modelling workflow
  • Data was maintained in different formats and locations by different business units
  • The proportion of early claimers was minuscule compared to non-early claimers

Solution

  • Xerago identified and integrated all the required data sources to create a single master database
  • Cleaning up and transformation of data was done to suite the requirements Key Performance Indicators (KPI) were derived from the available variables
  • A batch processing analytics model was built and deployed to identify customers with high propensity of non-persistence Balancing was performed to boost the proportion of early claim class and predictive model was built

Results

By targeting potential non-persistent customers through campaigns/call-center actions and by promoting NACH Registrations (NACH was identified as an important factor for persistency during analysis), 13th month persistency of the client increased by 5% in regular premiums. 84% of the non-persistent and 89% of the persistent customers were correctly identified by the model for 13th month persistency.

During the project period, out of the 13 predicted customers as high risk early claimers, 9 were confirmed by further investigation as potential scammers, thereby the company has saved Rupees 16.8 million of sums assured as claims amount.

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