Fraud detection in credit card transactions using Quantum-Assisted Restricted Boltzmann Machines
By: João Marcos Cavalcanti de Albuquerque Neto, Gustavo Castro do Amaral, Guilherme Penello Temporão
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.
Similar Papers
Hybrid Quantum-Classical Neural Networks for Few-Shot Credit Risk Assessment
Machine Learning (CS)
Helps banks decide who to lend money to.
QFDNN: A Resource-Efficient Variational Quantum Feature Deep Neural Networks for Fraud Detection and Loan Prediction
Quantum Physics
Finds fake credit card charges faster and better.
Toward Practical Quantum Machine Learning: A Novel Hybrid Quantum LSTM for Fraud Detection
Quantum Physics
Finds fake money faster using quantum power.