AI-Powered Fraud Detection and Risk Management in FinTech: Safeguarding Transactions with Machine Learning

Preethi Ravisankar

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025, vol. 11, issue 1, 2304-2310

Abstract: 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.

Keywords: Fraud Detection Systems; Machine Learning; Risk Management; Behavioral Analytics; Cross-Border Security (search for similar items in EconPapers)
Date: 2025
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT251112241
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrcseit.com/home/article/view/CSEIT251112241 Article URL (text/html)
https://ijsrcseit.com/home/article/download/CSEIT251112241/CSEIT251112241 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v11:y2025:i1:id:899

DOI: 10.32628/CSEIT251112241

Access Statistics for this article

More articles in International Journal of Scientific Research in Computer Science, Engineering and Information Technology from International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Bibliographic data for series maintained by Pankaj Sharma (USA) ().