Quantum Machine Learning and Fraud Detection

Belghachi Mohammed

Vikas Balasaheb Maral, Aryan Dongare, Vidya Gandhale +2 more

Tanay Kamlesh Patel, Todd M.W. Hodges, Glen S. Uehara +4 more

Subodh Nath Pushpak

This paper demonstrates a novel use of quantum machine learning (QML) algorithms for detecting fraudulent activities in the home insurance sector. Utilizing actual data and IBM Quantum processors through the Qiskit software stack, the study introduces an innovative method for selecting quantum features that are specifically designed to accommodate the limitations of Near Intermediate Scale Quantum (NISQ) technology by using the Quantum Support Vector Machine (QSVM) in conjunction with traditional machine learning techniques. A comprehensive comparison was conducted to evaluate their effectiveness in detecting fraud. The indicators such as accuracy, recall, and false positive rate are carefully analyzed. Despite the constraints of current quantum technology, QSVM shows excellent accuracy, especially on limited datasets, indicating its potential to enhance insurance fraud detection. The research emphasizes the crucial feature selection in optimizing QML algorithms for fraud detection tasks. It investigates the capacities of hybrid quantum/classical machine learning ensembles. Future research directions include expanding this study to actual hardware implementations to verify its practical feasibility. The work enhances financial security in the insurance business by using quantum computing technology in fraud detection approaches. It establishes the feasibility and efficacy of using quantum resources to solve difficult real-world issues, setting a foundation for or the broader application of QML in fraud detection and other fields.

Priyanka Jammwal

Journal of Theoretical and Applied Information Technology

With the increasing prevalence of online transactions, the risk of online fraud has become a major concern for individuals, businesses, and financial institutions. Traditional methods of fraud detection often fall short in addressing the dynamic and evolving nature of fraudulent activities. The escalating threat of online fraud necessitates innovative approaches to enhance the efficacy of fraud detection systems. Using a quantum machine learning (QML) strategy that incorporates Support Vector Machine (SVM) supplemented with quantum annealing solvers, this study has developed and implemented a detection framework. Our evaluation of its detection performance was based on a comparison of the QML application's performance with twelve different machine learning algorithms. This research investigates the fusion of classical machine learning algorithms with quantum annealing solvers as a novel strategy for fortifying online fraud detection. With traditional methods struggling to keep pace with the dynamic nature of fraudulent activities, this paper explores the potential synergy between machine learning and quantum computing to address the evolving challenges in online transactions. Our study aims to demonstrate the feasibility and effectiveness of integrating these technologies, leveraging quantum annealing to optimize the complex decision-making processes inherent in fraud detection. Through an in-depth analysis, we present findings on the performance, speed, and adaptability of the integrated model, showcasing its potential to revolutionize the landscape of online fraud detection and bolster cyber security measures.