Quantum Machine Learning For Credit Risk: A Next-Generation Approach To Risk Assessment

Abstract

Financial sectors are based on credit risk modeling to make decisions on lending and regulatory compliance. Although classical machine learning has been used to increase predictiveness, such models are faced with mounting limitations as complex and high-dimensional financial data is processed. Quantum Machine Learning (QML) is an aspect of quantum computing that has offered a potential resolution to the issues, integrating quantum computing and machine learning to speed up the calculations and extract more insight into the patterns. QML has theoretical benefits in classifying credit risk via Quantum Support Vector Machines, Quantum Neural Networks, and hybrid quantum-classical models, through superposition and entanglement. Initial applications show good performance in portfolio optimization, default forecasting, and simulation of risks with the existing hardware constraints. QML demonstrates specific potential in the context of using non-traditional data sources and finding hidden correlations that could reflect creditworthiness, which may allow inclusion-based lending practices without sacrificing risk assessment. With the development of quantum hardware, the financial services sector will gradually adopt quantum capabilities via realistic hybrid strategies that eventually revolutionize the way credit risk is assessed in the world markets.