a combined approach of the supervised autoencoder and xgboost method for credit card fraud detection

Losses related to fraudulent transactions are increasing, so building a fraud detection system is essential. previous studies have employed a variety of data mining and machine learning techniques to construct fraud detection systems. this study presents a new hybrid method based on the supervised autoencoder and the extreme gradient boosting (xgboost) method. this combined method uses the power of a supervised autoencoder to generate an expressive representation of the data. it employs the xgboost method as a robust classifier to detect fraudulent transactions. the hyperparameters of the proposed method are fine-tuned using the bayesian optimization algorithm. the experiments on a public dataset containing 280 thousand records demonstrated that the proposed method achieves better results than the baseline method considering all the performance criteria, including recall, precision, and f1 measure.