Machine Learning for Credit and Transaction Risk Scoring Mitigation in Financial Frauds: A Review

Abstract

Abstract—Financial fraud remains a pervasive challenge in
the global financial market, causing substantial economic losses
and undermining trust in financial institutions. In addition to
outlining several fraud categories, such as credit card fraud,
financial statement fraud, insurance fraud, mortgage fraud, and
money laundering, this paper offers a thorough review of
financial fraud with an emphasis on credit and transaction risk.
It examines the fundamental ideas of credit and transaction
risks, such as counterparty, concentration, nation, default, and
settlement risks, and highlights how important machine learning
methods are to improving risk assessment and fraud detection.
The study looks at state-of-the-art methods for effectively
identifying and reducing fraudulent behavior, including deep
learning architectures, neural networks, decision trees, support
vector machines, and supervised and unsupervised learning.
Furthermore, it discusses integrated fraud detection frameworks
that leverage multimodal data fusion, unified risk assessment,
and real-time adaptive scoring to improve detection accuracy
and operational efficiency. The study concludes by identifying
emerging trends and future research directions to strengthen
financial fraud prevention and risk management in an
increasingly digital financial ecosystem.