STM Journals Peer-reviewed research from Consortium e-Learning Network

E-Commerce for Future & Trends

Customer Churn Prediction in Life Insurance

  1. Ninad Rao
  2. Shalaka Waghamale
  3. V. Krishnasubramaniam
  4. Shanta Sondur

Abstract

In order to forecast customer churn and keep consumers, life insurance firms frequently struggle to use their data efficiently. New opportunities have emerged for tackling this problem due to advancements in categorization models within the field of machine learning. In this study, we suggest an innovative method that uses machine learning classification models to forecast client churn and boost customer retention rates in the life insurance sector. Our proposed system utilizes multiple data sources, such as LIS, to generate valuable insights and identify specific markets for particular products. We want to build an accurate and efficient customer churn prediction system that can improve business outcomes and customer experiences by integrating machine learning algorithms with the extensive data that life insurance firms have access to.

Keywords

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