Quantum machine learning for credit scoring

Author

Nikolaos SCHETAKIS, Technical University of Crete
Davit AGHAMALYAN, Agency for Science, Technology and Research
Micheael BOGUSLAVSKY, Tradeteq Ltd
Agnieszka REES, Tradeteq Ltd
Marc RAKOTOMALALA, Singapore Management UniversityFollow
Paul Robert GRIFFIN, Singapore Management UniversityFollow

Publication Type

Journal Article

Version

publishedVersion

Publication Date

5-2024

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

Discipline

Databases and Information Systems | Finance and Financial Management | Theory and Algorithms

Research Areas

Information Systems and Management

Publication

Mathematics

Volume

12

Issue

9

First Page

1

Last Page

12

ISSN

2227-7390

Identifier

10.3390/math12091391

Publisher

MDPI

Embargo Period

7-26-2022

Citation

SCHETAKIS, Nikolaos; AGHAMALYAN, Davit; BOGUSLAVSKY, Micheael; REES, Agnieszka; RAKOTOMALALA, Marc; and GRIFFIN, Paul Robert. Quantum machine learning for credit scoring. (2024). Mathematics. 12, (9), 1-12.
Available at: https://ink.library.smu.edu.sg/sis_research/7186

Copyright Owner and License

Authors

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Additional URL

https://doi.org/10.3390/math12091391