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Predicting Customer Attrition: Churn Prevention for a Leading P&C Insurance Company .

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Overview.

A leading property and casualty (P&C) insurance company aimed to predict customer attrition, both mid-term cancellations and end-term non-renewals, to increase its persistency ratio. Coforge implemented advanced machine learning techniques, including feature engineering, survival analysis, and ensemble learning models, to accurately identify high-risk customers and improve retention rates.

Challenges.

The client faced significant challenges in predicting customer attrition: 

  • Customer Attrition Prediction: Needed to predict both mid-term cancellations and end-term non-renewals. 
  • Increasing Persistency Ratio: Aimed to increase the persistency ratio by identifying and retaining high-risk customers. 

Solution.

Coforge implemented a comprehensive churn prevention solution: 

  • Feature Engineering: Combined variables from socio-demographic data, customer-company interactions, product and servicing details, claims, and competing product offerings. 
  • Feature Selection: Used the Boruta algorithm for effective feature selection. 
  • Survival Analysis: Applied survival analysis techniques to address attrition probability and timing. 
  • Separate Modeling: Modeled cancellations and non-renewals separately for more accurate predictions. 
  • Ensemble Learning Model: Developed an ensemble learning model using various techniques, including Random Forest, AdaBoost, CatBoost, XGBoost, and LightGBM. 

Key Highlights: 

Coforge's solution delivered significant value to the client's operations: 

  • Early Flagging: Identified high-probability and high-value customers early. 
  • High Model Accuracy: Achieved a high model accuracy of approximately 92%. 

The Impact.

Metric 

Result 

Customer Identification 

Early flagging of high-risk customers 

Model Accuracy 

~92% accuracy 

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