Using statistical models and case-based reasoning in claims prediction: experience from a real-world problem

Case-based reasoning (CBR) has been widely used in many real-world applicat ions. In general, CBR systems propose their answers based on solutions atta ched with the most similar cases retrieved from their case bases. However, in our vehicle insurance domain where the dataset contains a large amount o f inconsistencies, proposing solutions based only on the most similar cases results in unacceptable answers. In this article, we propose a hybrid-reas oning algorithm which employs a number of statistical models derived from a nalysis of the entire dataset as an alternative reasoning method. Results o f our experiments have shown that the use of these models enable our experi mental system to propose better solutions than answers proposed based only on the closest matched cases. (C) 1999 Elsevier Science B.V. All rights res erved.