Applications of Data Mining Techniques in Life Insurance

Abstract

Knowledge discovery in financial organization have been built and operated mainly to support decision making using knowledge as strategic factor. In this paper, we investigate the use of various data mining techniques for knowledge discovery in insurance business. Existing software are inefficient in showing such data characteristics. We introduce different exhibits for discovering knowledge in the form of association rules, clustering, classification and correlation suitable for data characteristics. Proposed data mining techniques, the decision-maker can define the expansion of insurance activities to empower the different forces in existing life insurance sector.