Abstract & Overview
Fraudulent internet transactions have caused considerable harm and losses for both people and organizations over time. The growth of cutting-edge technology and worldwide connectivity has exacerbated the rise in online fraud instances. To offset these losses, robust fraud detection systems must be developed. ML and statistical approaches are critical components in properly recognizing fraudulent transactions. However, implementing fraud detection models presents challenges such as limited data availability, data sensitivity, and imbalanced class distributions. The confidentiality of records adds complexity to drawing inferences and constructing improved models in this domain. This research explores multiple algorithms suitable for classifying transactions as either genuine or fraudulent using the Credit Card Fraud dataset. Given the extremely unbalanced nature of the dataset, the SMOTE approach was used for oversampling to alleviate the class distribution imbalance. In addition, feature selection was carried out, and the dataset was divided into training and test data. The experiments utilized NB, RF, and MLP algorithms, all of which demonstrated high accuracy in detecting credit card fraud. MLP method achieved 99.95% accuracy as compared to other methods