A Machine-Learning-Based Business Analytical System for Insurance Customer Relationship Management and Cross-Selling

Abstract

Effective cross-selling practices are integral to maintaining strong customer relationships and optimizing business processes within the insurance industry. This study presents a novel three-stage Machine Learning-Based System (MLBS) designed to enhance the identification of potential insurance customers and improve customer relationship management. This study proposes combining under sampling strategies and an ensemble approach to improve prediction performance. The proposed MLBS method involves selecting the best training sample using artificial neural networks and employing the stacking ensemble approach. It yields superior prediction results, exhibiting the highest recall, precision, and Area Under the Curve (AUC). These advancements substantially bolster the efficiency of cross-selling strategies. This research pioneers the application of stacking ensemble learning within the cross-selling domain, representing a novel contribution to the business field. The outcomes underscore the superiority of the MLBS system over baseline models across multiple performance metrics, thereby significantly enhancing support for cross-selling campaigns in various businesses.