Detecting Auto Insurance Fraud by Data Mining Techniques
The paper presents fraud detection method to predict and analyze fraud patterns from data. To generate classifiers, the authors apply the Na?ve Bayesian Classification, and Decision Tree-Based algorithms. A brief description of the algorithm is provided along with its application in detecting fraud. The same data is used for both the techniques. They analyze and interpret the classifier predictions. The model prediction is supported by Bayesian Na?ve Visualization, Decision Tree visualization, and Rule-Based Classification. They evaluate techniques to solve fraud detection in automobile insurance.