Quantum Machine Learning (QML) presents a novel paradigm for financial modeling, particularly for the challenge of few-shot credit risk assessment, which is a critical issue in inclusive finance where data scarcity limits conventional models. To address this, we propose and implement a hybrid quantum-classical workflow. Our methodology first utilizes a classical model ensemble for dimensionality reduction, creating a dense, three-dimensional feature representation from the original data. This representation then serves as input to a Quantum Neural Network (QNN), trained with the parameter-shift rule, which acts as the core classifier. The framework’s efficacy was validated through numerical simulations and on real hardware using the Quafu Cloud Platform’s ScQ-P21 superconducting processor. On a real-world credit dataset of 279 samples, our QNN achieved a robust average AUC of 0.852 ± 0.027 in simulations and an impressive AUC of 0.89 in the hardware experiment. This performance surpasses a suite of classical benchmarks, with a particularly strong result on the recall metric—crucial for minimizing false negatives in risk control. This study provides a pragmatic blueprint for applying QML to data-constrained financial scenarios and offers valuable empirical evidence of its potential in the NISQ era.