SafeSwipe: A Cloud-Enabled XGBoost Framework for Credit Card Fraud Detection
DOI:
https://doi.org/10.5281/zenodo.18974723Keywords:
Credit Card Fraud Prevention, Adaptive Machine Learning, Amazon SageMaker Platform, XGBoost Classifier, Dynamic Fraud Detection, Behavioral Pattern Analysis, Secure Transactions.Abstract
The abuse of the credit card systems is a significant cause of financial risks that are subjecting both the customers and the banking institutions to a significant level of risk. Conventional fraud detection systems, which are generally based on a preconfigured set of rules, are unable to adapt fast enough to changing fraud techniques. Consequently, they often produce a significant number of false alarms and decrease the effectiveness of the entire prevention procedure. To overcome these limitations, this paper presents a machine learning-based framework, which combines the use of Amazon SageMaker and XGBoost. With the help of historical transaction data classified as legitimate or malicious, the model is taught the ability to identify the slight changes of behavior that can lead to malice. The proposed approach will enable quick, adaptive evaluation unlike fixed rule engines and can dynamically adapt to new fraud patterns. Such models can be created, modified and stored in Amazon SageMaker, using limited infrastructure, in an easy managed manner. Gradient boosting implementations, such as XGBoost provide greater accuracy and lower false positive detection rates. As a result, this combined usage will strengthen payment security and improve the reliability and convenience of transaction processing for all financial organizations and customers.
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