Quantum Powered Credit Risk Assessment: A Novel Approach using hybrid Quantum-Classical Deep Neural Network for Row-Type Dependent Predictive Analysis

TL;DR

This work proposes a hybrid quantum-classical deep neural network for credit risk assessment that incorporates Row-Type Dependent Predictive Analysis (RTDPA) and SMOTE to tailor predictions to different loan types. It details a Quantum Powered Credit Risk Assessment architecture, including quantum embedding, entangling quantum layers, and a fusion with classical dense networks, optimized via a binary cross-entropy loss. The experimental evaluation on a 25k-sample bank dataset demonstrates competitive accuracy and highlights class-imbalance challenges, with PCA used for dimensionality reduction constrained by current quantum simulators. The study discusses computational constraints, limitations, and future directions for scalable, interpretable quantum-enhanced predictive finance applications.

Abstract

The integration of Quantum Deep Learning (QDL) techniques into the landscape of financial risk analysis presents a promising avenue for innovation. This study introduces a framework for credit risk assessment in the banking sector, combining quantum deep learning techniques with adaptive modeling for Row-Type Dependent Predictive Analysis (RTDPA). By leveraging RTDPA, the proposed approach tailors predictive models to different loan categories, aiming to enhance the accuracy and efficiency of credit risk evaluation. While this work explores the potential of integrating quantum methods with classical deep learning for risk assessment, it focuses on the feasibility and performance of this hybrid framework rather than claiming transformative industry-wide impacts. The findings offer insights into how quantum techniques can complement traditional financial analysis, paving the way for further advancements in predictive modeling for credit risk.

Figures (6)

• Figure 1: Neural Network Architecture with Weights and Biases
• Figure 2: Comprehensive Workflow of the QRTDPA Methodology. This diagram provides a detailed representation of the Quantum Row-Type Dependent Predictive Analysis (QRTDPA) framework, encompassing all critical phases of the experimentation process. Starting with the data flow and identification of heterogeneity in data types, the methodology includes feature engineering, data augmentation, and cross-validation steps. The workflow further incorporates quantum encoding, which transforms classical data into quantum states, followed by the integration of quantum processing layers with classical deep learning layers. The final stages involve algorithm training, hyperparameter optimization, validation on a dedicated dataset, testing on unseen data, and robust model evaluation. This pipeline is designed to synergize quantum and classical approaches for optimal predictive performance.
• Figure 3: Quantum-Classical Circuit Architecture. The circuit illustrates the hybrid quantum-classical architecture used in this study. The input features ($\ket{x_1}, \ket{x_2}, ..., \ket{x_n}$) are encoded into quantum states via parameterized quantum gates $U(\theta)$. Quantum processing is followed by measurement operations ($\langle Z \rangle$) to extract classical features, which are fed into dense neural network layers for further processing. The architecture leverages quantum advantages for feature transformation and classical layers for predictive tasks.
• Figure 4: PCA
• Figure 5: Training and Validation Loss for Personal Loans