Enhancing Fraud Detection Systems in the USA: A Machine Learning Approach to Identifying Anomalous Transactions

Abstract

The landscape of financial fraud in the United States is more advanced today, with fraudsters adopting sophisticated methods that elude traditional detection systems. As digital payments gain popularity, the number of potential fraud cases and their sophistication also increased, causing heavy financial losses to institutions and consumers alike. The primary objective of this research was to design and implement machine learning models that can significantly improve fraud detection systems in their precision. This study was centered specifically on fraud detection in the US financial system, researching artificial intelligence approaches that can be applied to support anomaly detection and risk analysis processes. The dataset employed in the analysis is a high-level transaction dataset that includes a spectrum of financial transaction details. Each transaction entry included primary details such as timestamp, transaction value, and sender-receiver information. The timestamp enabled each transaction to be sorted in chronological order, making it possible to carry out time-series analysis of patterns such as maximum transaction time or seasonality in spending behavior. Three models were predominantly employed: Random Forest Classifier, Logistic Regression, and Support Vector Classifier. The performance of models was measured using a set of metrics that included accuracy, precision, recall, F1-score, and ROC-AUC. The Random Forest model was better in terms of higher accuracy, thanks to its ability to handle non-linear relationships via ensemble learning. The integration of machine learning in fraud detection enhances the capabilities of payment providers and financial institutions tremendously. With sophisticated algorithms, financial institutions can process large volumes of transactional data in real time, enabling them to detect anomalous patterns that speedily indicate fraud. The findings of this study reinforce the effectiveness of machine learning models in identifying anomalous transactions, verifying that advanced approaches such as Random Forest and Support Vector Machines significantly enhance fraud detection compared to legacy approaches. One key to such effectiveness is that feature selection is crucial; carefully chosen features that included user behavior and transactional context played a key role in increasing detection rates and eliminating false positives.