From detection to prevention: A review of end-to-end machine learning pipelines for fraud management in credit union operations

Machine learning has emerged as a transformative force in financial security, offering institutions the ability to anticipate and neutralize threats before they materialize. Against this backdrop, this study reviews the transition from traditional fraud detection to prevention-oriented strategies in credit union operations through the use of end-to-end machine learning pipelines. It reviews literature on the main stages of fraud management pipelines, including data collection, preprocessing, feature engineering, model training, validation, deployment, and continuous monitoring. Key machine learning techniques for fraud detection, including supervised, unsupervised, ensemble, hybrid, and anomaly detection models, are also covered in the paper, along with an assessment of their relevance to credit union operations. The findings show that machine learning can improve fraud detection accuracy, support real-time decision-making, and strengthen proactive risk management, but its effectiveness depends on data quality, scalable infrastructure, governance, and explainability. Key problems with false positives, model drift, privacy, and regulatory compliance are further highlighted by the study. Overall, the paper makes the case that effective fraud management in credit unions necessitates not just sophisticated analytical tools but also rigorous ethical protections, institutional preparedness, and ongoing adaptability to evolving fraud behaviors.