AI-Powered Fraud Detection and Risk Management in FinTech: Safeguarding Transactions with Machine Learning
DOI:
https://doi.org/10.32628/CSEIT251112241Keywords:
Fraud Detection Systems, Machine Learning, Risk Management, Behavioral Analytics, Cross-Border SecurityAbstract
This comprehensive article examines the evolution and implementation of AI-powered fraud detection and risk management systems in the FinTech sector. The article explores how artificial intelligence and machine learning technologies have revolutionized financial security through advanced detection capabilities, real-time monitoring, and adaptive learning systems. The article explores both supervised and unsupervised learning approaches in fraud detection, analyzing their effectiveness in identifying known patterns and detecting novel fraud schemes. It delves into behavioral analytics and anomaly detection systems that create detailed user profiles and identify suspicious patterns through multi-variable analysis. The article further examines the critical balance between security measures and user experience, highlighting how modern systems adapt authentication requirements based on risk levels while maintaining customer satisfaction. Additionally, the article addresses the complexities of cross-border payment security, discussing specialized measures for international transaction monitoring and regulatory compliance across multiple jurisdictions.
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