Next-Gen Risk Frameworks ML Integration for Credit Monitoring and Governance
Abstract
The existing traditional credit risk systems are falling short of what Basel III/IV and FINOS v2.0 require and predicted increases in non-performing loans by 2025 will only add to these concerns. This paper outlines a Hybrid machine learning (ML) - Blockchain framework that uses Ensemble (XGBoost-LSTM) to create predictions, SHAP for explainability, and Polygon Smart Contracts for a secure audit trail. By leveraging this Hybrid machine-learning-block chain model to create Rapid Probability of Default (PD) scores at a significantly reduced cost than traditional models, the proposed solution will address current limitations in the way credit risk has been historically monitored, particularly with respect to Basel III/IV and FINOS v2.0. The empirical back testing results of this research, which analyzed over 5 Million loans, demonstrated dramatic improvements in the number of loans examined (increased recall) and in the reduced number of false positives compared to the traditional credit risk monitoring processes. Additionally, the use of Blockchain Oracles enables more efficient KYC and compliance processes, while Micro-batching has addressed the issues with Real-Time Processing and produces significant cost savings associated with monitoring credit risk. This Hybrid machine learning-blockchain framework is poised to establish new standards for Credit Monitoring by 2026. Future developments of Quantum Computing and Federated Learning may lead to additional advancements over the next decade.
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 9 No. 2 (2026): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
435-443 |
Published |
March 20, 2026 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Mohan Kumar Sonne Gowda (%2026). Next-Gen Risk Frameworks ML Integration for Credit Monitoring and Governance. International Journal of Science, Research and Technology , Vol. 9 No. 2 (2026): International Journal of Science, Research and Technology (IJSRAT) , pp. 435-443. https://doi.org/10.15662/IJSRAT.2023.0605004 |
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