Title: Artificial Intelligence: Theory, Tools and Real-World Applications
Editors: Prof. Dr. A. Shameem and Dr. J. Rengamani
ISBN: 978-81-69857-67-3
Chapter: 6
DOI: https://doi.org/10.59646/819/18
Author: Ms. J. Francis Julee Rajam
Abstract
The rapid growth of digital financial transactions has significantly increased the risk of financial fraud, posing major challenges for financial institutions, businesses, and regulatory agencies. Traditional rule-based fraud detection systems often struggle to identify sophisticated and evolving fraudulent activities in real time. Machine Learning (ML) has emerged as a powerful solution for enhancing fraud detection and risk assessment by analyzing large volumes of transactional data, identifying hidden patterns, and adapting to new fraud techniques. This study examines the role of machine learning algorithms in detecting financial fraud and assessing financial risks across banking, insurance, credit card transactions, and online payment systems. Various supervised and unsupervised learning techniques, including Decision Trees, Random Forests, Support Vector Machines, Neural Networks, and Anomaly Detection models, are explored for their effectiveness in identifying suspicious activities. The research highlights how ML models improve detection accuracy, reduce false positives, and enable real-time monitoring of financial transactions. Furthermore, the study discusses challenges such as data imbalance, privacy concerns, model interpretability, and regulatory compliance. The findings indicate that machine learning significantly enhances fraud prevention strategies and supports proactive risk management, contributing to greater financial security and operational efficiency. As financial systems continue to evolve, the integration of advanced machine learning techniques is expected to play a critical role in strengthening fraud detection frameworks and improving risk assessment capabilities.
Keywords: Machine Learning, Financial Fraud Detection, Risk Assessment, Artificial Intelligence, Anomaly Detection, Predictive Analytics, Credit Card Fraud, Financial Security, Data Mining, Banking Analytics.