Securing Transactions: Machine Learning Techniques for Credit Card Anomaly Detection
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Abstract
Credit card fraud is a serious problem when it comes to financial security. When used properly, credit cards have many advantages, but they also put users at risk of various fraudulent actions. These fraudulent activities can be effectively identified by leveraging advanced machine learning algorithms. The Credit Card Anomaly Detection Problem for securing transactions revolves around the development of models using historical credit card transactions to distinguish between legitimate and fraudulent ones. This issue represents a paramount concern that requires the need of various fields such as AI, Machine Learning, and Deep Learning, where automation can promise to deliver robust solutions. The primary goal of this research is to maximize the detection of fraudulent transactions while minimizing the misclassification of legitimate transactions as fraudulent. This detection process constitutes a classification task, which demands meticulous data analysis and pre-processing. It involves the application of multiple anomaly detection algorithms, including Isolation Forest, Local Outlier Factor, and Support Vector Machine. The study employs diverse datasets, covering credit card transactions ranging from 30,000 to 568,630 instances. The analysis integrates crucial performance metrics, such as accuracy, precision, recall, and F1- score. The Isolation Forest algorithm showcases proficiency in accurately categorizing legitimate transactions, achieving its highest accuracy within the datasets
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References
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