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Support Vector Machines for Credit Scoring: a performance comparison between classic Machine Learning and Quantum-enhanced approach

Mattia Bacigalupo, Pier Giuseppe Giribone

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.13140/rg.2.2.15937.72804

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I metodi kernel quantistici sono proposti come alternativa ai classificatori classici in ambito finanziario. Uno studio recente confronta Support Vector Machines e Support Vector Regression classici con le rispettive versioni quantum-enhanced su tre problemi di credit scoring e riporta un vantaggio dei modelli quantistici, soprattutto nella classificazione multiclasse. Questo documento riproduce il confronto su un campione sintetico di 4.000 imprese costruito a partire dagli indici dell'Altman Z-score. Con lo stesso protocollo di validazione e con i quantum kernel calcolati in simulazione esatta dello statevector, i modelli classici eguagliano o superano i modelli quantistici su tutti e tre i problemi. English — Quantum kernel methods have been proposed as an alternative to classical classifiers in finance. A recent study compares classical Support Vector Machines and Support Vector Regression with their quantum-enhanced counterparts on three credit-scoring problems and reports an advantage for the quantum models, especially in multiclass classification. This document reproduces the comparison on a synthetic sample of 4,000 firms built from Altman Z-score indices. With the same validation protocol and with quantum kernels computed via exact statevector simulation, the classical models match or outperform the quantum models on all three problems.

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In this study, we develop and compare quantum and classical machine learning-based chronic kidney disease prediction models. We used the "Chronic_Kidney_Disease Data Set" of the UCI Machine Learning Repository. We performed data preprocessing and applied feature engineering techniques to select the best features. We developed two quantum machine learning-based models and two classical machine learning-based models. We used a hybrid classical-quantum environment for building quantum machine learning models. Finally, we compared the performances of all four models. We found that the Quantum Support Vector Machine performs best among the quantum models. The model’s accuracy was 95% with a k-fold cross-validation score of 94.5% and an ROC-AUC score of 0.987. Among the classical models, the Support Vector Machine showed the highest performance with an accuracy of 92.5%, a k-fold cross-validation score of 93.9%, and a ROC-AUC score of 0.974. Overall, the Quantum Support Vector Machine outperformed all other developed models in terms of accuracy and validation scores. If the quantum models can be executed in a quantum computer instead of a hybrid environment, the models will exhibit higher accuracy and faster execution time. As the models are precisely predicting chronic kidney disease with high accuracy, we believe this study will act as an inspiring framework in the less-investigated field of quantum machine learning-based chronic kidney disease prediction.

IEEE Annual International Computer Software and Applications Conference (COMPSAC)•Jul 8, 2025•2 citations
The objective of this paper is to demonstrate that traditional statistical problems in credit scoring can be solved efficiently by implementing a quantum-enhanced version of the traditional Support Vector Machines algorithm. Three significant case studies are presented, involving regression, dichotomous and multi-class classification problems in the field of credit risk management. The analysis highlights the promise of the quantum kernel method as a competitive solution especially for multiclass problems with strongly structured features. Quantum-enhanced models tend to outperform classical approaches because they can capture more intricate non-linear relationships between variables, partly due to their ability to leverage entanglement. This advantage is particularly evident in credit risk assessment, where it can improve the credit evaluation process and help reduce credit losses for financial and insurance institutions.
I metodi kernel quantistici sono proposti come alternativa ai classificatori classici in ambito finanziario. Uno studio recente confronta Support Vector Machines e Support Vector Regression classici con le rispettive versioni quantum-enhanced su tre problemi di credit scoring e riporta un vantaggio dei modelli quantistici, soprattutto nella classificazione multiclasse. Questo documento riproduce il confronto su un campione sintetico di 4.000 imprese costruito a partire dagli indici dell'Altman Z-score. Con lo stesso protocollo di validazione e con i quantum kernel calcolati in simulazione esatta dello statevector, i modelli classici eguagliano o superano i modelli quantistici su tutti e tre i problemi. English — Quantum kernel methods have been proposed as an alternative to classical classifiers in finance. A recent study compares classical Support Vector Machines and Support Vector Regression with their quantum-enhanced counterparts on three credit-scoring problems and reports an advantage for the quantum models, especially in multiclass classification. This document reproduces the comparison on a synthetic sample of 4,000 firms built from Altman Z-score indices. With the same validation protocol and with quantum kernels computed via exact statevector simulation, the classical models match or outperform the quantum models on all three problems.
In this study, we develop and compare quantum and classical machine learning-based chronic kidney disease prediction models. We used the "Chronic_Kidney_Disease Data Set" of the UCI Machine Learning Repository. We performed data preprocessing and applied feature engineering techniques to select the best features. We developed two quantum machine learning-based models and two classical machine learning-based models. We used a hybrid classical-quantum environment for building quantum machine learning models. Finally, we compared the performances of all four models. We found that the Quantum Support Vector Machine performs best among the quantum models. The model’s accuracy was 95% with a k-fold cross-validation score of 94.5% and an ROC-AUC score of 0.987. Among the classical models, the Support Vector Machine showed the highest performance with an accuracy of 92.5%, a k-fold cross-validation score of 93.9%, and a ROC-AUC score of 0.974. Overall, the Quantum Support Vector Machine outperformed all other developed models in terms of accuracy and validation scores. If the quantum models can be executed in a quantum computer instead of a hybrid environment, the models will exhibit higher accuracy and faster execution time. As the models are precisely predicting chronic kidney disease with high accuracy, we believe this study will act as an inspiring framework in the less-investigated field of quantum machine learning-based chronic kidney disease prediction.