A Review of Anomaly Identification in Finance Frauds using Machine Learning System

The growing prevalence of digital financial payments has caused fraud in financial services to significantly increase globally. Artificial learning-based abnormality to identifying anomalies must be used because traditional fraud detection methods are not very adaptable to contemporary dishonest methods. This review investigates a variety of machine learning methodologies, such as deep learning, and the techniques used to detect fraud in banking, insurance, stock market processes, and digital payment transactions. The methods used include autonomous, freestanding, and semi-supervised learning. The study highlights challenges associated with imbalanced data distributions and adversarial attacks, which impact detection performance and interpretability. Furthermore, the study investigates current advances in the integration of transparent artificial intelligence with graph-based anomaly identification technologies to enhance the transparency and credibility of fraud detection systems. The investigation's constraints are reviewed to lead the construction of modern counterfeiting detection platforms that use numerous machine learning approaches for better accuracy, real-time processing, and privacy preservation. The findings provide insights into designing robust fraud detection systems aligned with banking institutions' requirements, ensuring enhanced financial security and compliance.