IQNN-CS: Interpretable Quantum Neural Network for Credit Scoring

• URL: http://arxiv.org/abs/2510.15044v1
• Date: Thu, 16 Oct 2025 18:02:03 GMT
• Title: IQNN-CS: Interpretable Quantum Neural Network for Credit Scoring
• Authors: Abdul Samad Khan, Nouhaila Innan, Aeysha Khalique, Muhammad Shafique,
• Abstract summary: We present IQNN-CS, an interpretable quantum neural network framework for multiclass credit risk classification.<n>ICAA is a novel metric that quantifies attribution divergence across predicted classes, revealing how the model distinguishes between credit risk categories.<n>Our results highlight a practical path toward transparent and accountable QML models for financial decision-making.
• Score: 2.2133667529581933
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Feature Ranking in Credit-Risk with Qudit-Based Networks [0.0]
  In finance, predictive models must balance accuracy and interpretability.<n>We present a quantum neural network (QNN) based on a single qudit.<n>We benchmark our model on a real-world, imbalanced credit-risk dataset from Taiwan.
  arXiv  Detail & Related papers  (2025-11-24T14:15:57Z)
• A Novel XAI-Enhanced Quantum Adversarial Networks for Velocity Dispersion Modeling in MaNGA Galaxies [14.016108312641101]
  We propose a novel quantum adversarial framework that integrates a hybrid quantum neural network (QNN) with classical deep learning layers.<n>In the proposed model, an adversarial evaluator concurrently guides the QNN by computing feedback loss, thereby optimizing both prediction accuracy and model explainability.<n> Empirical evaluations show that the Vanilla model achieves RMSE = 0.27, MSE = 0.071, MAE = 0.21, and R2 = 0.59, delivering the most consistent performance across regression metrics compared to adversarial counterparts.
  arXiv  Detail & Related papers  (2025-10-28T16:27:10Z)
• Hybrid Quantum-Classical Neural Networks for Few-Shot Credit Risk Assessment [52.05742536403784]
  This work tackles the challenge of few-shot credit risk assessment.<n>We design and implement a novel hybrid quantum-classical workflow.<n>A Quantum Neural Network (QNN) was trained via the parameter-shift rule.<n>On a real-world credit dataset of 279 samples, our QNN achieved a robust average AUC of 0.852 +/- 0.027 in simulations and yielded an impressive AUC of 0.88 in the hardware experiment.
  arXiv  Detail & Related papers  (2025-09-17T08:36:05Z)
• Comparative Performance Analysis of Quantum Machine Learning Architectures for Credit Card Fraud Detection [3.5418331252013897]
  This study examines how different quantum feature map and ansatz configurations affect the performance of three QML-based classifiers.<n>The VQC consistently demonstrates strong classification results, achieving an F1 score of 0.88, while the SQNN also delivers promising outcomes.<n>In contrast, the EQNN struggles to produce robust results, emphasizing the challenges presented by non-standardized data.
  arXiv  Detail & Related papers  (2024-12-27T04:17:34Z)
• LEP-QNN: Loan Eligibility Prediction using Quantum Neural Networks [4.2435928520499635]
  We propose a novel approach that employs Quantum Machine Learning (QML) for Loan Eligibility Prediction using Quantum Neural Networks (LEP-QNN)<n>Our innovative approach achieves an accuracy of 98% in predicting loan eligibility from a single, comprehensive dataset.<n>This research showcases the potential of QML in financial predictions and establishes a foundational guide for advancing QML technologies.
  arXiv  Detail & Related papers  (2024-12-04T09:35:03Z)
• Exact Certification of (Graph) Neural Networks Against Label Poisoning [50.87615167799367]
  We introduce an exact certification method for label flipping in Graph Neural Networks (GNNs)<n>We apply our method to certify a broad range of GNN architectures in node classification tasks.<n>Our work presents the first exact certificate to a poisoning attack ever derived for neural networks.
  arXiv  Detail & Related papers  (2024-11-30T17:05:12Z)
• QIXAI: A Quantum-Inspired Framework for Enhancing Classical and Quantum Model Transparency and Understanding [0.0]
  Deep learning models are often hindered by their lack of interpretability, rendering them "black boxes" This paper introduces the QIXAI Framework, a novel approach for enhancing neural network interpretability through quantum-inspired techniques. The framework applies to both quantum and classical systems, demonstrating its potential to improve interpretability and transparency across a range of models.
  arXiv  Detail & Related papers  (2024-10-21T21:55:09Z)
• Large Multi-modality Model Assisted AI-Generated Image Quality Assessment [53.182136445844904]
  We introduce a large Multi-modality model Assisted AI-Generated Image Quality Assessment (MA-AGIQA) model. It uses semantically informed guidance to sense semantic information and extract semantic vectors through carefully designed text prompts. It achieves state-of-the-art performance, and demonstrates its superior generalization capabilities on assessing the quality of AI-generated images.
  arXiv  Detail & Related papers  (2024-04-27T02:40:36Z)
• QKSAN: A Quantum Kernel Self-Attention Network [53.96779043113156]
  A Quantum Kernel Self-Attention Mechanism (QKSAM) is introduced to combine the data representation merit of Quantum Kernel Methods (QKM) with the efficient information extraction capability of SAM. A Quantum Kernel Self-Attention Network (QKSAN) framework is proposed based on QKSAM, which ingeniously incorporates the Deferred Measurement Principle (DMP) and conditional measurement techniques. Four QKSAN sub-models are deployed on PennyLane and IBM Qiskit platforms to perform binary classification on MNIST and Fashion MNIST.
  arXiv  Detail & Related papers  (2023-08-25T15:08:19Z)
• Pre-training Tensor-Train Networks Facilitates Machine Learning with Variational Quantum Circuits [70.97518416003358]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning on noisy intermediate-scale quantum (NISQ) devices. While tensor-train networks (TTNs) can enhance VQC representation and generalization, the resulting hybrid model, TTN-VQC, faces optimization challenges due to the Polyak-Lojasiewicz (PL) condition. To mitigate this challenge, we introduce Pre+TTN-VQC, a pre-trained TTN model combined with a VQC.
  arXiv  Detail & Related papers  (2023-05-18T03:08:18Z)
• Quantization-aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks [58.195261590442406]
  We study the problem of training and certifying adversarially robust quantized neural networks (QNNs) Recent work has shown that floating-point neural networks that have been verified to be robust can become vulnerable to adversarial attacks after quantization. We present quantization-aware interval bound propagation (QA-IBP), a novel method for training robust QNNs.
  arXiv  Detail & Related papers  (2022-11-29T13:32:38Z)
• The dilemma of quantum neural networks [63.82713636522488]
  We show that quantum neural networks (QNNs) fail to provide any benefit over classical learning models. QNNs suffer from the severely limited effective model capacity, which incurs poor generalization on real-world datasets. These results force us to rethink the role of current QNNs and to design novel protocols for solving real-world problems with quantum advantages.
  arXiv  Detail & Related papers  (2021-06-09T10:41:47Z)
• Neural Network-based Automatic Factor Construction [58.96870869237197]
  This paper proposes Neural Network-based Automatic Factor Construction (NNAFC) NNAFC can automatically construct diversified financial factors based on financial domain knowledge. New factors constructed by NNAFC can always improve the return, Sharpe ratio, and the max draw-down of a multi-factor quantitative investment strategy.
  arXiv  Detail & Related papers  (2020-08-14T07:44:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.