Application of quantum annealing solvers along with machine learning algorithms to identify online deception

Abstract

The rising frequency of online transactions has heightened the potential of online fraud, posing significant concerns for consumers, organizations, and financial institutions. Conventional fraud detection systems frequently inadequately handle the dynamic and shifting characteristics of fraudulent activity. The increasing menace of online fraud requires novel strategies to improve the effectiveness of fraud detection systems. This study has developed and implemented a detection framework utilizing a quantum machine learning (QML) technique that integrates support vector machines (SVM) with quantum annealing solvers. We assessed its detection performance by comparing the QML application's efficacy against twelve distinct ML techniques. This study examines the integration of classical ML algorithms with quantum annealing solutions as an innovative approach to enhance online fraud detection. This study examines the possible integration of ML and quantum computing to tackle the rising issues of fraudulent activities in online transactions, as existing solutions are inadequate. This work seeks to illustrate the viability and efficacy of using these technologies, including quantum annealing to enhance the intricate decision-making processes involved in fraud detection. We offer insights on the performance, speed, and adaptability of the integrated model, highlighting its potential to transform online fraud detection and enhance cyber security measures.