Advanced fraud detection using machine learning models: enhancing financial transaction security

TL;DR

This paper develops a comprehensive machine learning framework that combines feature engineering, anomaly detection models, and clustering techniques to improve fraud detection in digital financial transactions.

Contribution

It introduces an integrated approach using multiple models and feature sets for enhanced fraud detection in real-world credit card transaction data.

Findings

Unsupervised models effectively identify anomalies with top 1% reconstruction errors.

Feature engineering captures behavioral and temporal patterns for fraud detection.

Clustering techniques help isolate suspicious transaction regions.

Abstract

The rise of digital payments has accelerated the need for intelligent and scalable systems to detect fraud. This research presents an end-to-end, feature-rich machine learning framework for detecting credit card transaction anomalies and fraud using real-world data. The study begins by merging transactional, cardholder, merchant, and merchant category datasets from a relational database to create a unified analytical view. Through the feature engineering process, we extract behavioural signals such as average spending, deviation from historical patterns, transaction timing irregularities, and category frequency metrics. These features are enriched with temporal markers such as hour, day of week, and weekend indicators to expose all latent patterns that indicate fraudulent behaviours. Exploratory data analysis reveals contextual transaction trends across all the dataset features. Using the transactional data, we train and evaluate a range of unsupervised models: Isolation Forest, One Class SVM, and a deep autoencoder trained to reconstruct normal behavior. These models flag the top 1% of reconstruction errors as outliers. PCA visualizations illustrate each models ability to separate anomalies into a two-dimensional latent space. We further segment the transaction landscape using K-Means clustering and DBSCAN to identify dense clusters of normal activity and isolate sparse, suspicious regions.