Fraud detection in credit card transactions using Quantum-Assisted Restricted Boltzmann Machines
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Abstract (arXiv)
Use cases for emerging quantum computing platforms become economically relevant as the efficiency of processing and availability of quantum computers increase. We assess the performance of Restricted Boltzmann Machines (RBM) assisted by quantum computing, running on real quantum hardware and simulators, using a real dataset containing 145 million transactions provided by Stone, a leading Brazilian fintech, for credit card fraud detection. The results suggest that the quantum-assisted RBM method is able to achieve superior performance in most figures of merit in comparison to classical approaches, even using current noisy quantum annealers. Our study paves the way for implementing quantum-assisted RBMs for general fault detection in financial systems.
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References · 5
- 2020Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlationcited by 425
- 2015Application of Quantum Annealing to Training of Deep Neural Networkscited by 37
- 2022Gaussian-Bernoulli RBMs Without Tearscited by 17
- 2020Quantum Computing Assisted Deep Learning for Fault Detection and Diagnosis in Industrial Process Systemscited by 5
- 2023QUBO.jl: A Julia Ecosystem for Quadratic Unconstrained Binary Optimizationcited by 2
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