A hybrid quantum-classical deep network, trained separately on agricultural and personal loan categories, reaches 81-83% accuracy on an imbalanced bank dataset, but without classical baselines or uncertainty estimates the result does not support its stated potential.
Machine learning-driven credit risk: a systemic review
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Quantum Powered Credit Risk Assessment: A Novel Approach using hybrid Quantum-Classical Deep Neural Network for Row-Type Dependent Predictive Analysis
A hybrid quantum-classical deep network, trained separately on agricultural and personal loan categories, reaches 81-83% accuracy on an imbalanced bank dataset, but without classical baselines or uncertainty estimates the result does not support its stated potential.