2025/02/06 by Rath Minati, Minati, Rath, D. Deva Hema +1 · 1 citation
Business, Management and Accounting · #Artificial Intelligence (cs.AI) #Computational Finance (q-fin.CP) #FOS: Computer and information sciences #FOS: Economics and business #Financial Distress and Bankruptcy Prediction #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2502.07806
openalex publication_date 2025/02/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.