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

Abstract

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.