IQNN-CS: Interpretable Quantum Neural Network for Credit Scoring
This addresses the need for transparent and accountable quantum models in high-stakes financial services, though it is incremental in applying existing interpretability techniques to quantum neural networks.
The paper tackled the problem of making quantum machine learning models interpretable for credit scoring, introducing IQNN-CS, which achieved competitive predictive performance and enhanced interpretability on real-world datasets.
Credit scoring is a high-stakes task in financial services, where model decisions directly impact individuals' access to credit and are subject to strict regulatory scrutiny. While Quantum Machine Learning (QML) offers new computational capabilities, its black-box nature poses challenges for adoption in domains that demand transparency and trust. In this work, we present IQNN-CS, an interpretable quantum neural network framework designed for multiclass credit risk classification. The architecture combines a variational QNN with a suite of post-hoc explanation techniques tailored for structured data. To address the lack of structured interpretability in QML, we introduce Inter-Class Attribution Alignment (ICAA), a novel metric that quantifies attribution divergence across predicted classes, revealing how the model distinguishes between credit risk categories. Evaluated on two real-world credit datasets, IQNN-CS demonstrates stable training dynamics, competitive predictive performance, and enhanced interpretability. Our results highlight a practical path toward transparent and accountable QML models for financial decision-making.