Insurance dynamics - A data mining approach for customer retention in health care insurance industry

Abstract

Extraction of customer behavioral patterns is a complex task and widely studied for various industrial applications under different heading viz., customer retention management, business intelligence and data mining. In this paper, authors experimented to extract the behavioral patterns for customer retention in Health care insurance. Initially, the customers are classified into three general categories - stable, unstable and oscillatory. To extract the patterns the concept of Novel index tree (a variant of K-d tree) clubbed with K-Nearest Neighbor algorithm is proposed for efficient classification of data, as well as outliers and the concept of insurance dynamics is proposed for analyzing customer behavioral patterns.