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 C5i

Leveraging an insurance customer retrieval solution to reactivate lapsed accounts

C5i

Technology Category	Analytics & Modeling - Predictive Analytics
Applicable Industries	Finance & Insurance
Applicable Functions	Sales & Marketing
Use Cases	Predictive Replenishment
Services	Data Science Services
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Predictive Analytics

Applicable Industries

Finance & Insurance

Applicable Functions

Sales & Marketing

Use Cases

Predictive Replenishment

Services

Data Science Services

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 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.

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.

Impact #1	Comprehensive analysis of lapsed policies
Impact #2	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
Impact #3	Development of a strategy for reactivating lapsed policies

Impact #1

Comprehensive analysis of lapsed policies

Impact #2

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

Impact #3

Development of a strategy for reactivating lapsed policies

Benefit #1	Approximately 11,000 policies targeted from a portfolio of 50,000
Benefit #2	8,000 policies successfully repossessed

Benefit #1

Approximately 11,000 policies targeted from a portfolio of 50,000

Benefit #2

8,000 policies successfully repossessed

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Overview

Leveraging an insurance customer retrieval solution to reactivate lapsed accounts

Operational Impact

Quantitative Benefit