Artificial Intelligence in Financial Risk Assessment and Fraud Detection

Marti Hearst

Artificial Intelligence in Financial Risk Assessment and Fraud Detection

Author(s)	Marti Hearst
Country	United States
Abstract	The rapid evolution of financial technologies has transformed the global financial sector by introducing advanced analytical methods for risk management, fraud prevention, and decision-making. Traditional financial risk assessment and fraud detection systems often depend on rule-based approaches, historical analysis, and manual investigations, which may struggle to identify sophisticated and rapidly changing financial threats. Artificial Intelligence (AI) has emerged as a powerful technology capable of analysing massive financial datasets, detecting complex patterns, predicting risks, and improving the accuracy and efficiency of financial security systems. This study examines the role of Artificial Intelligence in financial risk assessment and fraud detection through a comprehensive review of AI-driven methodologies, applications, benefits, challenges, and future directions. The research adopts a qualitative and analytical methodology based on secondary data collected from academic publications, financial technology reports, regulatory frameworks, and industry case studies. The study explores Machine Learning, Deep Learning, Natural Language Processing, anomaly detection, predictive analytics, and intelligent automation techniques used in modern financial systems. The findings demonstrate that AI significantly enhances financial risk management by enabling real-time transaction monitoring, credit risk prediction, market risk analysis, fraud pattern recognition, and automated compliance monitoring. Machine Learning algorithms can identify abnormal transaction behaviours, detect fraudulent activities, and adapt to emerging financial threats more effectively than traditional approaches. AI-powered systems also improve customer verification, anti-money laundering (AML) processes, and cybersecurity protection. However, challenges related to data privacy, algorithmic transparency, cybersecurity threats, regulatory compliance, and bias in AI decision-making remain significant concerns. Effective implementation requires explainable AI frameworks, ethical data governance, human oversight, and collaboration between financial institutions, technology providers, and regulators. The study concludes that Artificial Intelligence is becoming a critical component of modern financial security infrastructure. Future financial ecosystems will increasingly rely on intelligent, adaptive, and transparent AI systems to strengthen risk management, improve fraud prevention, and support sustainable financial innovation.
Keywords	Artificial Intelligence, Financial Risk Assessment, Fraud Detection, Machine Learning, FinTech, Predictive Analytics, Banking Security, Anomaly Detection.
Field	Engineering
Published In	Volume 1, Issue 3, May-June 2019
Published On	2019-05-20

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American Journal of Advanced Multidisciplinary Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Artificial Intelligence in Financial Risk Assessment and Fraud Detection

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