Leveraging Machine Learning Models for Real-Time Fraud Detection in Financial Transactions
Nathaniel Andrew Davis, Sophia Anne Harris · International Journal of Computer Technology and Science · 2024
This study investigates the effectiveness of machine learning models in identifying fraudulent financial transactions in real-time. Using a large dataset of transactions, we compare the accuracy, precision, and speed of various models, including logistic regression, random forests, and neural networks. Our findings suggest that ensemble methods yield higher detection rates while minimizing false positives, thus providing a promising approach to financial fraud prevention.