Machine Learning-Based Financial Transaction Fraud Detection Using Autoencoders

Abstract

Financial fraud has become a significant challenge due to the rapid growth of digital transactions. Traditional fraud detection systems rely on rule-based approaches, which are often ineffective against evolving fraud patterns. This project proposes a machine learning-based fraud detection system using Autoencoders, a type of unsupervised neural network, to identify anomalies in transaction data. The model is trained on normal transaction data and learns to reconstruct it with minimal error. When fraudulent transactions are introduced, the reconstruction error increases significantly, allowing the system to detect anomalies effectively.The system processes transaction data through preprocessing, feature scaling, and anomaly detection stages. It enables real-time monitoring and alerts for suspicious activities. The use of Autoencoders enhances detection accuracy while reducing false positives. The proposed system is scalable, efficient, and adaptable to changing fraud patterns.Overall, this approach improves financial security by providing an intelligent and automated solution for fraud detection, helping organizations minimize financial losses and enhance trust in digital transactions.
Keywords: Fraud Detection, Financial Transactions, Machine Learning, Autoencoders, Anomaly Detection, Neural Networks, Data Preprocessing, Feature Scaling, Real-Time Monitoring, Cybersecurity