Machine Learning for Financial Risk Assessment and Fraud Detection

Abstract

The financial services industry operates within an environment of escalating complexity, where institutions must simultaneously manage diverse risk exposures and defend against increasingly sophisticated fraudulent activities. Traditional approaches to risk assessment and fraud detection, grounded in rule-based systems, expert heuristics, and classical statistical models, are proving insufficient against the volume, velocity, and evolving nature of modern financial threats. Machine learning (ML) has emerged as a transformative technology in this domain, offering data-driven methodologies capable of modeling complex non-linear relationships, adapting to emerging patterns, and processing heterogeneous data at scale. This paper presents a comprehensive survey of recent advances in the application of machine learning to financial risk assessment and fraud detection. We examine the algorithmic foundations including gradient boosting frameworks, deep neural networks, graph neural networks, anomaly detection methods, natural language processing, and reinforcement learning that underpin modern financial ML systems. The paper provides detailed reviews of applications across credit risk modeling, market risk estimation, operational risk assessment, anti-money laundering, payment fraud detection, insurance fraud identification, and insider trading surveillance. We further analyze critical challenges including extreme class imbalance, concept drift and adversarial adaptation, model interpretability and regulatory compliance, data privacy constraints, and the tension between detection accuracy and customer experience. Finally, we outline future research directions including federated financial learning, causal inference for risk modeling, graph-based transaction intelligence, and the integration of large language models into financial compliance workflows.