machine learning systems in the identification of financial anomalies: a comprehensive review

The global rise in digital payments has been accompanied by a significant increase in financial fraud. conventional detection methods are often inadequate against modern deceptive tactics, necessitating the use of artificial intelligence and machine learning for effective anomaly identification. this review investigates a range of machine learning methodologies—including deep learning, and autonomous, unsupervised, and semi-supervised techniques—as applied to fraud detection in banking, insurance, stock markets, and digital payments. the study addresses key challenges such as imbalanced data and adversarial attacks, which hinder detection performance and model interpretability. it also explores emerging trends, particularly the integration of explainable ai (xai) and graph-based anomaly detection, to enhance the transparency and trustworthiness of fraud detection systems. by evaluating the current limitations of the field, this paper provides a direction for developing next-generation detection platforms that leverage hybrid machine learning models for greater accuracy, real-time processing, and data privacy. the findings offer valuable insights for creating robust fraud detection systems that meet the stringent requirements of financial institutions, thereby improving overall financial security and regulatory compliance.