Abstract
Credit Card fraud is a tough reality that continues to constrain the financial sector and its detrimental effects are felt across the entire financial market. Criminals are continuously on the lookout for ingenious methods for such fraudulent activities and are a real threat to security. Therefore, there is a need for early detection of fraudulent activity to preserve customer trust and safeguard their business. A major challenge faced in designing fraud detection systems is dealing with the class imbalance issue in the data since genuine transactions outnumber the fraudulent transactions typically account less than 1% of the total transactions. This is an important area of study as the positive case (fraudulent case) is hard to distinguish and becomes even harder with the inflow of data where the representation of such cases even decreases further. This study trained four predictive models, Artificial Neural Network (ANN), Gradient Boosting Machine (GBM) and Random Forest (RF) on different sampling methods. Random Under Sampling (RUS), Synthetic Minority Over-sampling Technique (SMOTE), Density-Based Synthetic Minority Over-Sampling Technique (DBSMOTE) and SMOTE combined with Edited Nearest Neighbour (SMOTEENN) was used for all models. The findings of this study indicate promising results with SMOTE based sampling techniques. The best recall score obtained was with SMOTE sampling strategy by DRF classifier at 0.81. The precision score for this classifier was observed to be 0.86. Stacked Ensemble was trained for all the sampled datasets and found to have the best average performance at 0.78. The Stacked Ensemble model has shown promise in the detection of fraudulent transactions across most of the sampling strategies.
Highlights
Transactions using credit cards have become an important aspect of our daily lives
Detection of credit card fraud is classified as a costsensitive problem, where there is an associated cost incurred for incorrectly classifying a genuine transaction as fraudulent and incorrectly classifying fraudulent transaction as genuine
Detection of credit card fraud is classified as a cost-sensitive problem, where there is an associated cost incurred for incorrectly classifying a genuine transaction as fraudulent and incorrectly classifying fraudulent transaction as genuine [9]
Summary
Transactions using credit cards have become an important aspect of our daily lives. Purchase of goods and services are no longer a chore that requires physical activity, rather it is initiated with a touch of a button on our smartphone or personal computers. The authorization of transactions is rigorous and secure such conveniences are brought about by compromising the proof of identity checks which require personal identification documents, authorized signature and physical presence. The basis of the identity proof in such transactions is the information on the card along with digital identification tied to the cardholder. The conveniences brought about by digital transactions makes it a target for fraudsters who employ elegant tactics for theft and illicit use. Credit card fraud is generally an unauthorized movement by an individual who is not authorized
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More From: International Journal of Advanced Computer Science and Applications
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