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C5i > Case Studies > Leveraging an insurance customer retrieval solution to reactivate lapsed accounts
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Leveraging an insurance customer retrieval solution to reactivate lapsed accounts

Technology Category
  • Analytics & Modeling - Predictive Analytics
Applicable Industries
  • Finance & Insurance
Applicable Functions
  • Sales & Marketing
Use Cases
  • Predictive Replenishment
Services
  • Data Science Services
The Challenge
The client, a leading insurance company, was facing a challenge of accessing lapsed insurance policies with a potential of repayment within a specific time bracket. The company was witnessing revenue loss and wanted to reactivate these lapsed policies. However, they wanted to ensure that the strategy resulted in minimum wastage of money and effort.
About The Customer
The customer in this case study is a leading insurance company operating in the financial services industry. The company was facing a challenge with lapsed insurance policies and was seeking a solution to reactivate these accounts. The company was experiencing revenue loss due to these lapsed policies and was looking for a strategy that would result in minimum wastage of money and effort. The company's goal was to identify these lapsed accounts and focus on their reactivation, which would result in an additional flow of revenue.
The Solution
Blueocean Market Intelligence customized their insurance customer retrieval solution to address this problem. They accessed the lapsed policies that had the potential of repayment by invoking inforce attributes on the defaulted policies. A binary logistic regression was utilized on lapsed and inforce datasets and a KS cutoff was decided based on the model results. A confusion matrix was built consisting of all the four wells. Factors like premium to be paid, income of the policy holder, occupation and the total sum assured at the end of maturity were found to be greatly affecting the model results.
Operational Impact
  • Comprehensive analysis of lapsed policies
  • Identification of factors affecting the predictive model such as premium to be paid, income of the policy holder, occupation and the total sum assured at the end of maturity
  • Development of a strategy for reactivating lapsed policies
Quantitative Benefit
  • Approximately 11,000 policies targeted from a portfolio of 50,000
  • 8,000 policies successfully repossessed

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