Designing of Knowledge-based System for Fraud Detection in Insurance Companies: Fuzzy Approach

As in other sectors of the economy, the insurance industry has experienced many changes in information technology over the years. Advances in hardware, software, and networks have offered benefits, such as reduced costs and time of data processing and increased potential for profit, as well as new challenges particularly in the area of increased competition. Technological innovations, such as data mining and data warehousing, have greatly reduced the cost of storing, accessing, and processing data. Data mining can be defined as the process of selecting, exploring, and modelling large amounts of data to uncover previously unknown patterns. In the insurance industry, data mining can help firms gain business advantage. For example, by applying data mining techniques, companies can fully exploit data about customer's buying patterns and behavior and achieve a greater understanding of customer motivations to help reduce fraud, anticipate resource demand, increase acquisition, and curb customer attrition. This paper designs expert system for intelligent fraud detection in e-insurance by fuzzy set theory with data mining approach.