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Lecture notes in networks and systems

Bridging the Divide: A Comparative Study of Quantum and Classical Machine Learning for Loan Prediction

Dhyey Vyas, Isha Shah, Archana Nanade, Krishna Samdani

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.1007/978-3-032-31119-1_4

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Predicting loan defaults is important to financial institutions for risk management and improving loaning policies. This research uses the LendingClub Loan Dataset comprising 2.26 million records with features such as credit score, debt income ratio, and loan amount, and applies a novel methodology for loan default and repayment behavior prediction with quantum-enhanced predictive analytics. The methodology uses embedded learning to disperse high-dimensional financial data into a low-dimensional quantum feature space. Coupled with a QML classifier, the framework demonstrated solid performance within a federated setting. The experimental findings indicate a ROC AUC of 0.897, surpassing classical machine learning baselines. The method preserves the data privacy through federated learning, thus applicable for sensitive financial contexts. This work demonstrates the ability of quantum analytics to enhance the predictive accuracy of loan defaults at scale.
Accurate prediction of future loan defaults is a critical capability for financial institutions that provide lines of credit. For institutions that issue and manage extensive loan volumes, even a slight improvement in default prediction precision can significantly enhance financial stability and regulatory adherence, resulting in better customer experience and satisfaction. Datasets associated with credit default prediction often exhibit temporal correlations and high dimensionality. These attributes can lead to accuracy degradation and performance issues when scaling classical predictive algorithms tailored for these datasets. Given these limitations, quantum algorithms, leveraging their innate ability to handle high-dimensionality problems, emerge as a promising new avenue alongside classical approaches. To assess the viability and effectiveness of quantum methodologies, we investigate a hybrid quantum-classical algorithm, utilizing a publicly available "Default Prediction Dataset" released as part of a third-party data science competition. Specifically, we employ hybrid quantum-classical machine learning models based on projected quantum feature maps and their ensemble integration with classical models to examine the problem of credit card default prediction. Our results indicate that the ensemble models based on the projected quantum features were capable of slightly improving the purely classical results expressed via a "Composite Default Risk" (CDR) metric. Furthermore, we discuss the practical applicability of the studied quantum-classical machine learning techniques and address open questions concerning their implementation.