Development of knowledge model for insurance product decision using the associative classification approach

Individual protection, physically or mentally, is very important for someone living in a risk environment. Insurance is one of the individual protections due to accident, blaze, critical diseases or death. Insurance company plays a critical role in providing competitive product insurance that covers flexible features depend on customer requirements. In order to compete with other competitors and fulfill the customer needs, the company needs a wise and proper business strategy. The insurance company needs extra knowledge on the potential customer whom can give a positive response to the insurance product being offered. In this paper we proposed an associative classification model to develop a knowledge model for determining the best class solution for insurance policy dataset. We enhanced the classification-based association of associative classification by using a heuristic to process two types of decision rules. The decision rules types were the correctly classified rules and the verified uncertain classified rules. The finding showed that the type of products could be proposed in a new insurance policy based on individual profiles.