Credit Card Fraud Detection Based on Combination of Sparse Autoencoder and Support Vector Machine

Credit card fraud has become a serious issue for banks and businesses as more and more transactions take place online. A number of machine learning methods have been developed to learn patterns in credit card frauds. These methods usually depend on sophisticated feature engineering because gaining representative features can improve their performances. However, manual feature engineering is impossible due to the ever-increasing amount of data and complicated relationships between transactions. In this work, a Sparse Autoencoder-Support Vector Machine (SAE-SVM) model is proposed to solve the issue of feature engineering. The SAE is an unsupervised machine learning method that learns representative features from the raw data. The SVM model later uses these features to predict whether the transaction is fraudulent. This SAE-SVM method achieves 0.80 F2 score on the Credit Card Fraud Detection dataset on Kaggle, compared to only 0.71 F2 score by the SVM method alone. In addition, the SAE-SVM model outperforms other autoencoder-based models regarding the F2 value. It is shown that the SAE can extract useful and low-dimensional features without much loss of information. The deployment of the SAE may relax supervised machine learning from complicated feature engineering. Furthermore, the SAE model is robust toward the concept drift issue, making it preferable over manual feature engineering.