An Analysis Of The Comparative Performance Of AI Tools And Techniques In Effective Fraud Detection Across Digital Payment Ecosystems
Dev Sheoran
School of Commerce, Narsee Monjee Institute of Management Studies, Bangalore
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http://doi.org/10.37648/ijps.v17i01.023
Abstract
The rapid proliferation of digital payment systems has increased the potential for fraudulent activities, requiring robust fraud detection mechanisms. Artificial Intelligence has emerged as a pivotal tool in addressing these challenges by leveraging its pattern recognition, anomaly detection, and predictive analytics capabilities. This paper explores the role of AI in fraud detection across payment systems, examining various AI models, their effectiveness, and challenges. The analysis delves into the comparative performance of AI techniques such as machine learning, deep learning, and natural language processing in detecting fraudulent activities, highlighting their strengths and limitations. Furthermore, the study incorporates real-world applications such as credit card transactions, mobile payments, and blockchain systems to underscore AI’s practical utility. This paper critically reviews challenges such as data quality, model interpretability, and the dynamic nature of fraud schemes while highlighting prospects like explainable AI and federated learning. This paper explores how AI continues to transform fraud detection, paving the way for more secure and trustworthy digital payment ecosystems.
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