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Credit card fraud is a major problem in today’s financial world. It induces severe damage to financial institutions and individuals. There has been an exponential increase in the losses due to fraud in recent years. Hence, effectively detecting fraudulent behavior is of vital importance for either financial institutions or individuals. Since credit fraud events account for a small proportion of all transaction events in real life, the datasets about credit fraud are usually imbalanced. Some common classifiers, such as decision tree and naïve Bayes, are unable to detect fraud. Furthermore, in some cases, traditional strategies for dealing with an imbalanced problem, such as the synthetic minority oversampling technique (SMOTE), are not effective for the fraud detection datasets. To accurately detect fraud behavior, this study uses anomaly detection on imbalanced data, as well as Isolation Forest (IForest) with kernel principal component analysis. A one-class support vector machine (OCSVM) with AdaBoost is used as two models to detect outliers which significantly improves detection accuracy and efficiency. The model achieved 96% accuracy, 100% precision, 96% recall, and 98%  F 1 score, respectively. The proposed model is expected to become a helpful tool for finding credit card fraud detection, and the analysis presented in this study will provides useful insights into credit card fraud detection mechanisms.

The Optimized Anomaly Detection Models Based on an Approach of Dealing with Imbalanced Dataset for Credit Card Fraud Detection