Abstract

Machine learning (ML) has become a cornerstone technology in combating financial fraud and performing risk assessment in modern financial systems. The increasing volume and sophistication of fraudulent activities—ranging from credit card fraud to money laundering—demand adaptive, scalable, and accurate detection mechanisms. Unlike traditional rule‑based systems that rely on static thresholds and human‑defined patterns, ML systems learn from historical and real‑time transactional data to detect anomalous behavior and evolving threat patterns. This paper explores the design and implementation of ML techniques for fraud detection and risk assessment, covering supervised, unsupervised, and hybrid learning approaches. We review key algorithms such as logistic regression, decision trees, ensemble methods, support vector machines, and deep neural networks, and discuss how they handle class imbalance, feature engineering, and real‑time scoring. The integration of ML with big data platforms and real‑time streaming frameworks enables financial institutions to process large volumes of transactions efficiently while maintaining low latency. The research also examines model interpretability, regulatory compliance, and operational challenges including false positives, data privacy, and concept drift. Through a combination of literature review, methodological synthesis, and case analysis, we highlight best practices, performance trade‑offs, and future directions for leveraging ML in securing financial ecosystems.