Generative AI and Dynamic Modeling for Real-Time Cloud-Based Credit Risk
Keywords:
Credit Risk Management, Synthetic Data Augmentation, Temporal Sequence Modeling, Event-Driven Architecture, Explainable AI (XAI)Abstract
The high rate of the digital financial ecosystem development has revealed the structural weaknesses of the old credit risk systems based on the fixed models and batch-processing setting. These systems are unable to record the real-time borrower activities and are not flexible to macroeconomic fluctuations. In order to fill this gap in architecture, this research project suggests Generative-Dynamic Risk Architecture (GDRA). The aim of the research is to synthesize the progress in four existing siloed technological areas: cloud-native orchestration, Generative AI, transformer-based sequence modeling, and Explainable AI (XAI). Our comparative study provides a number of main conclusions. First, event-driven microservices can dramatically improve the resilience of the system by 40% of the Mean Time To Recovery (MTTR). Second, Generative Adversarial Networks (GANs) and diffusion models can effectively reduce extreme class imbalance and simulate stress situations and maintain data privacy. Third, the predictive core uses Transformer networks to learn long-range temporal dependencies, and it persistently achieves better results in default prediction than classic recurrent models. Last but not least, SHAP-based XAI integration will make these complex models adhere to the stringent regulatory transparency requirements. We find that the GDRA offers a needed paradigm shift that offers a constantly recalibrating, fault-tolerant infrastructure that balances high predictive accuracy with regulatory accountability in contemporary credit risk management.
Downloads
References
Maher Ala’raj et al., “Modelling customers' credit card behaviour using bidirectional LSTM neural networks,” Springer, 2021. https://link.springer.com/content/pdf/10.1186/s40537-021-00461-7.pdf
Fadi Thabtah et al., “Data imbalance in classification: Experimental evaluation,” ScienceDirect, 2020. https://www.sciencedirect.com/science/article/abs/pii/S0020025519310497
Georgios Lambropoulos et al., “Emerging Technologies in Financial Services: From Virtualization and Cloud Infrastructures to Edge Computing Applications,” MDPI, 2026. https://www.mdpi.com/2073-431X/15/1/41
Anil Kumar et al., “Machine Learning (ML) Technologies for Digital Credit Scoring in Rural Finance: A Literature Review,” MDPI, 2021. https://www.mdpi.com/2227-9091/9/11/192
Niklas Bussmann et al., “Explainable Machine Learning in Credit Risk Management,” Springer, 2021. https://link.springer.com/content/pdf/10.1007/s10614-020-10042-0.pdf
Faisal Ramzan et al., “Generative Adversarial Networks for Synthetic Data Generation in Finance: Evaluating Statistical Similarities and Quality Assessment,” MDPI, 2024. https://www.mdpi.com/2673-2688/5/2/35
Nari Park et al., “Synthesizing Individual Consumers′ Credit Historical Data Using Generative Adversarial Networks,” MDPI, 2021. https://www.mdpi.com/2076-3417/11/3/1126
Mohammed Hameed Alhameed, “Deep Learning-Based Privacy Preserving Diffusion-Driven Synthetic Finance for Robust Stress Testing,” IEEE, 2026. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=11443110
Julius Olaniyan et al., “Graph-Temporal Contrastive Transformer for Financial Fraud Detection Using Transaction Behavior Modeling,” MDPI, 2025. https://www.mdpi.com/1999-4893/18/12/770
Lucas Coelho e Silva et al., “Transformers and attention-based networks in quantitative trading: a comprehensive survey,” https://dl.acm.org/doi/pdf/10.1145/3677052.3698684
Saurabh Kohli, “AI-Driven Orchestration Systems in Cloud-Native Financial Applications: A Framework for Next-Generation Investment Platforms,” Sarcouncil Journal of Engineering and Computer Sciences, 2025. https://sarcouncil.com/download-article/SJECS-477-2025-356-363.pdf
Sravan Komar Reddy Pullamma, “Event-Driven Microservices for Real-Time Revenue Recognition in Cloud-Based Enterprise Applications,” SAMRIDDHI, 2022. https://www.smsjournals.com/index.php/SAMRIDDHI/article/view/3417
Giorgio Visani et al., “Statistical stability indices for LIME: obtaining reliable explanations for machine learning models,” arXiv:2001.11757v2, 2020. https://arxiv.org/pdf/2001.11757
Maryan Rizinski et al., “Ethically Responsible Machine Learning in Fintech,” IEEE Access, 2022. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9869843
Matthew J. Page et al., “The PRISMA 2020 statement: an updated guideline for reporting systematic reviews,” BMJ, 2021. https://www.bmj.com/content/372/bmj.n71
Toon Calders and Sicco Verwer, “Three naive Bayes approaches for discrimination-free classification," Springer, 2010. https://link.springer.com/content/pdf/10.1007/s10618-010-0190-x.pdf
Mohsen Khosravi et al., “Artificial Intelligence and Decision-Making in Healthcare: A Thematic Analysis of a Systematic Review of Reviews,” Health Services Research and Managerial Epidemiology, 2024. https://journals.sagepub.com/doi/pdf/10.1177/23333928241234863
Satyadhar Joshi, “Gradient Boosting and Explainable AI for Financial Risk Management: A Comprehensive Review,” Preprints, 2025. https://www.preprints.org/frontend/manuscript/9a5116442bda6481224c96533ed8b194/download_pub
Chongren Wang and Zhuoyi Xiao, “A Deep Learning Approach for Credit Scoring Using Feature Embedded Transformer,” MDPI, 2022. https://www.mdpi.com/2076-3417/12/21/10995
Emilija Strelcenia and Simant Prakoonwit, “A Survey on GAN Techniques for Data Augmentation to Address the Imbalanced Data Issues in Credit Card Fraud Detection,” MDPI, 2023. https://www.mdpi.com/2504-4990/5/1/19
Dr. Manish Jain, “Survey Of Resilience Strategies In Cloud Platforms: From Fault Detection To Auto-Recovery,” JGRMA, 2025. https://www.researchgate.net/profile/Manish-Jain-61/publication/396925780_
Hunter Heidenreich et al., “Deconstructing Recurrence, Attention, and Gating: Investigating the transferability of Transformers and Gated Recurrent Neural Networks in forecasting of dynamical systems,” arXiv:2410.02654v1, 2024. https://arxiv.org/pdf/2410.02654
V. Lanzetta, “Transfer learning for financial data predictions: a systematic review,” arXiv:2409.17183, 2024. https://arxiv.org/abs/2409.17183
Arthur Charpentier et al., “Reinforcement Learning in Economics and Finance,” arXiv:2003.10014v1, 2020. https://arxiv.org/pdf/2003.10014
A. S. M. Fahim et al., “Algorithmic Accountability in U.S. Consumer FinTech: Governance Mechanisms for Credit Risk, Fair Lending, and Financial Stability,” Journal of Economics, Finance and Accounting Studies, 2023. https://al-kindipublishers.org/index.php/jefas/article/view/12016
Gowtham Reddy Enjam and Komal Manohar Tekale, “Self-Healing Microservices for Insurance Platforms: A Fault-Tolerant Architecture Using AWS and PostgreSQL,” International Journal of AI, Big Data, Computational, and Management Studies, 2024. https://ijaibdcms.org/index.php/ijaibdcms/article/view/252
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
