Benchmarking Quantum Machine Learning Algorithms for Credit Scoring and Default Prediction in Financial Services

Abstract

Abstract
Credit scoring and default‐prediction remain central tasks for financial institutions, and machine learning (ML) has brought major improvements in predictive power over classical statistical methods. At the same time, quantum computing and quantum machine learning (QML) have emerged as potentially transformative technologies for finance. This paper presents a comprehensive benchmark study of a range of quantum and hybrid quantum-classical machine learning algorithms applied to credit scoring and default prediction in financial services. We review relevant literature from both classical ML and QML in credit risk, derive full mathematical formulations for classical logistic/ML and quantum variational-circuit models, propose a benchmarking framework including datasets, evaluation metrics, and computational resource considerations, and present simulated empirical results comparing classical and QML approaches under a variety of conditions (feature dimensionality, class imbalance, quantum noise). We analyse where quantum approaches may offer practical benefits (e.g., in training speed, smaller parameter sets, potential quantum-advantage) and where current limitations remain (hardware noise, qubit count, interpretability, regulatory constraints). We further discuss industry implementation issues in banking, regulatory and governance implications, and chart future research directions. Our findings suggest that while QML does not yet deliver large accuracy gains in real‐world credit-scoring tasks, it shows promise in training efficiency and parameter reduction, thus warranting further investment and study.
Keywords: quantum machine learning, credit scoring, default prediction, quantum‐classical hybrid, benchmark, financial services, credit risk.