AI-Powered Fraud Detection in Online Banking Transactions

Abstract

The trend of online payment fraud has become a significant challenge for financial institutions, resulting in enormous financial losses and security violations. Traditional fraud detection methods have a tendency to overlook the evolving patterns of fraud, resulting in excessive false positives and failure to identify actual fraud cases. In order to overcome this challenge, we propose a real-time Online Payment Fraud Detection Model based on machine learning methods. The model is able to process big data with transaction attributes like transaction value, user behavior, and geographic location. The model leverages Support Vector Machine (SVM), Random Forest, and XGBoost algorithms to label a transaction as authentic or malicious using past records. The implementation of the model is carried out in Python in Google Colab, which provides scalability and training efficiency. The model enhances detection rates and reduces false positives using feature engineering and preprocessing of data. The system provides instant fraud notifications, thus facilitating proactive intervention by financial institutions. The utilization of cloud-based infrastructure provides the proposed model with high performance, flexibility, and improved security in online payment systems.