AI-Powered Fraud Detection Quantum Model in Embedded Payments Products

Abstract

This paper explored how to integrate an AI-based fraud detection quantum model in embedded payment products which 
will aid in securing the system and limiting the exposure of the owners to financial risks. The analysis of transaction 
patterns and their anomalies in real time was done via the application of a hybrid approach by combining machine 
learning algorithms with quantum computing techniques. The literature review illustrates the exceptional abilities that 
AI technology has gained in the fight against fraud and recognises the role of deep learning and the integration of 
blockchain technology in it. Graphs and charts showed how emerging fraud trends could be data visualised and how 
efficiency could be gained from quantum computing. The results of the case study analysis of some of the leading firms 
in PayPal and Visa showed that the detected fraud accuracy has improved, false positives reduced and the system is 
scalable. The results indicate that placing AI-driven quantum models in the payment ecosystems is crucial to enhancing 
the fraud mitigation strategy. For optimal fraud prevention in embedded payment systems, the study suggests that a 
multi-layered AI security framework, continued model training, and compliance with regulations be aligned. These 
insights help in understanding the development of more resilient intelligent financial security mechanisms.