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

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 (EDA) 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 model’s 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. Finally, we propose a composite risk score by aggregating binary flags ‎from all anomaly detectors, unexpected spend indicators, rapid‐use events, and high‐frequency “spending sprees”. This score highlights ‎the riskiest cardholders and merchants, enabling prioritized investigation. Our framework detects approximately 1–2% of transactions as ‎anomalies and effectively surfaces high‐risk entities, demonstrating the power of unsupervised analytics for real-time fraud surveillance ‎in dynamic financial ecosystems‎.