Anomaly Detection in Financial Transactions

A Comparative Study of Machine Learning Approaches for Fraud Detection

Authors:

Prof. Mohammad Asif, Vrajkumar Patel, Prof. Dr. Vikram Kumar, Hemil Prajapati

Assistant Professor, Department of Computer Science and Engineering, Parul Institute of Technology, Parul University, Gujarat, India Students of Computer Science and Engineering, Parul Institute of Engineering and Technology, Parul University, Gujarat, India

Abstract:

Financial fraud is an ever-growing threat in the global banking and e-commerce landscape, costing organizations billions of dollars each year. Anomaly detection — the process of identifying patterns in data that deviate significantly from expected behavior — has emerged as a cornerstone of modern fraud prevention systems. This research paper presents a comprehensive review and comparative analysis of machine learning techniques applied to anomaly detection in financial transactions, with a focus on their effectiveness, scalability, and real-world applicability.

We examine supervised methods such as Logistic Regression, Random Forest, and XGBoost; unsupervised approaches including Isolation Forest, Local Outlier Factor (LOF), and Autoencoders; and hybrid ensemble frameworks. Special attention is given to the challenge of highly imbalanced datasets — a persistent issue in fraud detection — and techniques such as SMOTE (Synthetic Minority Oversampling Technique) and cost-sensitive learning are evaluated as remedies.

Experimental results on the publicly available IEEE-CIS Fraud Detection and PaySim datasets demonstrate that ensemble methods and deep learning autoencoders yield the highest AUC-ROC scores, reaching up to

0.98. The paper further discusses real-world implementation challenges, ethical considerations, regulatory compliance (including GDPR), and future directions such as federated learning and explainable AI (XAI) in fraud detection.

Keywords: Anomaly Detection, Financial Fraud, Machine Learning, Isolation Forest, Autoencoder, Imbalanced Data, XGBoost, Deep Learning, SMOTE, Federated Learning