Renewal Likelihood Predictor Model for Insurance Products

Renewal Likelihood Predictor Model for Insurance Products


The client is a renowned UK-based Insurance broker & aggregator with access to risk management and employee benefit services. They aim to fill a market gap for a specialist, independent, client-centric, and service-focused insurance adviser.

Business challenges: 

  • Largely inefficient monitoring of high-volume client base and not-so-up-to-date products churned £10M since 2018
  • Needed an early warning indicator of the likelihood of current clients renewing their existing products
  • Efficient monitoring of high volume (small to mid) client base lacked

Benefits achieved:

ITC Infotech delivered a 1% improvement in churn, representing an increase in revenue of ~ £720k. We provided the predictions (churn likelihood) using XGB, Random Forest, Logistic Regression, and Decision tree algorithms that helped achieve 82% accuracy with 0.62 ROC-AUC. Moreover, end-to-end MLOPs integration made tracking, training, deploying, and monitoring the models easy.


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