Quantum Machine Learning for Credit Scoring

Nikolaos Schetakis (), Davit Aghamalyan, Michael Boguslavsky, Agnieszka Rees, Marc Rakotomalala and Paul Robert Griffin
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Nikolaos Schetakis: Computational Mechanics and Optimization Laboratory, School of Production Engineering and Management, Technical University of Crete, 73100 Chania, Greece
Davit Aghamalyan: School of Computing and Information Systems, Singapore Management University, 81 Victoria Street, Singapore 188065, Singapore
Michael Boguslavsky: Tradeteq Ltd., London EC2M 4YP, UK
Agnieszka Rees: Tradeteq Ltd., London EC2M 4YP, UK
Marc Rakotomalala: Sim Kee Boon Institute for Financial Economics, Singapore Management University, 50 Stamford Road, Singapore 178899, Singapore
Paul Robert Griffin: QUBITECH, Quantum Technologies, 15231 Athens, Greece

Mathematics, 2024, vol. 12, issue 9, 1-12

Abstract: This study investigates the integration of quantum circuits with classical neural networks for enhancing credit scoring for small- and medium-sized enterprises (SMEs). We introduce a hybrid quantum–classical model, focusing on the synergy between quantum and classical rather than comparing the performance of separate quantum and classical models. Our model incorporates a quantum layer into a traditional neural network, achieving notable reductions in training time. We apply this innovative framework to a binary classification task with a proprietary real-world classical credit default dataset for SMEs in Singapore. The results indicate that our hybrid model achieves efficient training, requiring significantly fewer epochs (350) compared to its classical counterpart (3500) for a similar predictive accuracy. However, we observed a decrease in performance when expanding the model beyond 12 qubits or when adding additional quantum classifier blocks. This paper also considers practical challenges faced when deploying such models on quantum simulators and actual quantum computers. Overall, our quantum–classical hybrid model for credit scoring reveals its potential in industry, despite encountering certain scalability limitations and practical challenges.

Keywords: quantum machine learning; quantum classifiers; quantum credit scoring; quantum algorithms (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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