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A comprehensive survey of methods for overcoming the class imbalance problem in fraud detection

Fraud is a hugely costly criminal activity which occurs in a number of domains. Data mining has been applied to fraud detection in both a supervised and non-supervised manner. When a supervised data mining approach is used, one of the biggest problems that is encountered, is the problem of class imbalance. The class imbalance problem is not unique to the domain of fraud, but also occurs in fields as diverse as medical diagnosis and quality control. There are two basic means of overcoming the class imbalance problem, these are data methods and algorithmic methods. Data methods generally involve an under sampling, over sampling or hybrid over/under sampling approach. Other data method investigated include SMOTE, which uses a K-NN learner to artificially synthesize minority class samples. Algorithmic methods investigated include using either a mis-classification cost in the case of the Metacost procedure or metacost thresholds. Other algorithmic methods include the use of learners which are not sensitive to the class imbalance problem. The different methods for overcoming the class imbalance problem are implemented using open-source software. 3 datasets are used to investigate the usefulness of the different methods. 2 of the datasets are from the domain of fraud, while the third is from the domain of medical diagnosis.