Teferi (PhD), DerejeMulugeta, Mirchaye2018-11-112023-11-182018-11-112023-11-182015-06-03http://etd.aau.edu.et/handle/12345678/14153Insurance fraud is an act that can be seen in different insurance types including medical insurance. Fraud in the case of medical insurance is done by misrepresenting facts to get unauthorized benefit from the expenses covered under medical insurance. Globally companies are spending high amount of claim costs due to insurance fraud. It is a concern for companies to have a system that could differentiate frauds from incoming claims. Data mining tools and techniques can be applied in different fields one of which is fraud detection. This research is conducted for the purpose of testing the applicability of data mining techniques in detecting fraud suspected medical insurance claims in the case of Ethiopian Insurance Corporation. A six_step hybrid process model is used to guide the entire knowledge discovery process. J48 decision tree and Naïve Bayes classification algorithms are used to build predictive model. Several experiments are conducted and the resulting models show that the J48 decision tree is found to work well in detecting fraud with 84.01% classification accuracy. A prototype is developed based on the rules extracted from the J48 decision tree model. Finally recommendations and future research directions are forwarded based on the results achieved.enThe Case Of Ethiopian Insurance CorporationThesis