Improving Credit Card Fraud Detection Through Quantum Computing

Credit card fraud is a pervasive and evolving challenge in the financial sector, necessitating innovative solutions for accurate and efficient detection. Traditional fraud detection methods, while effective in many cases, often struggle with scalability and the complexity of modern fraud patterns. Recent advancements in quantum computing have opened new avenues for tackling these problems. This paper introduces Quantum Transaction Flow Analysis (QTFA) , a novel аnd innovative quantum-based methodology for enhancing credit card fraud detection. QTFA leverages the principles of quantum mechanics, such as superposition, entanglement, and quantum optimization, to model and analyze transaction flows within a quantum network. By representing transactions as quantum states and their relationships as entanglements, QTFA enables precise anomaly detection through quantum measurement. Experimental results demonstrate that QTFA outperforms classical machine learning methods, such as Random Forests and Support Vector Machines (SVMs), achieving a 98 % accuracy rate, a 10 % reduction in false positive rates, and improved recall. This paper also explores the integration of QTFA into real-world systems, highlighting its potential to revolutionize fraud detection while identifying current limitations and directions for future research.