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

• URL: http://arxiv.org/abs/2506.10842v1
• Date: Thu, 12 Jun 2025 15:59:25 GMT
• Title: Advanced fraud detection using machine learning models: enhancing financial transaction security
• Authors: Nudrat Fariha, Md Nazmuddin Moin Khan, Md Iqbal Hossain, Syed Ali Reza, Joy Chakra Bortty, Kazi Sharmin Sultana, Md Shadidur Islam Jawad, Saniah Safat, Md Abdul Ahad, Maksuda Begum,
• Abstract summary: This research presents an end-to-end, feature-rich machine learning framework for detecting credit card transaction anomalies and fraud using real-world data.
• Score: 0.3370543514515051
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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