Purpose: Traditional credit scoring models rely on static, historical financial data, which limits their effectiveness for individuals with thin credit files and their responsiveness to evolving borrower behavior, particularly in the context of emerging financing models like Buy Now, Pay Later (BNPL). This research aims to develop a novel, deep learning-based framework for highly accurate, real-time credit risk assessment on deep processing loan platforms.

Design/Methodology/Approach: A quantitative, computational approach is employed. The proposed framework, Credit Scoring and Risk Analysis utilizing Deep Processing Loan Platform (CSRA-DPLP-BSCNN), integrates advanced data preprocessing using a Regularized Bias-Aware Ensemble Kalman Filter (RBEKF) to manage multi-source data, handle missing values, and reduce noise in real-time BNPL datasets. The core predictive element is a Binarized Simplicial Convolutional Neural Network (BSCNN), selected for its superior capability to identify complex, non-linear financial and behavioral patterns.

Findings: The CSRA-DPLP-BSCNN model demonstrates exceptional performance suitable for real-time deployment. Empirical results indicate an accuracy of 98%, a precision of 97%, a recall of 96%, and an F1-score of 98%. Critically, the computational time is exceptionally low, measured at 1.159 seconds, significantly outperforming benchmark models. The integrated RBEKF preprocessing is found to substantially enhance the reliability of the input data stream.

Originality/Value: This research delivers a robust, high-performance deep learning architecture that simultaneously addresses the need for real-time decision-making, high predictive accuracy, and efficient handling of alternative data, thereby promoting greater financial inclusion and enhancing systemic risk mitigation in modern lending ecosystems.

Real-Time Credit Scoring, Artificial Intelligence, Deep Learning, Credit Risk

S. Nayak, "The future of SME lending: Innovations in risk assessment and credit scoring models using machine learning in fintech," International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, no. 9, pp. 5627–5642, 2023.

E. K. Avickson, J. S. Omojola, and I. A. Bakare, "The role of revalidation in credit risk management: Ensuring accuracy in borrowers' financial data," International Journal of Research Publication and Reviews, vol. 5, no. 10, pp. 2011-2024, 2024. https://doi.org/10.55248/gengpi.5.1024.2810

T. F. Nuka and A. A. Ogunola, "AI and machine learning as tools for financial inclusion: Challenges and opportunities in credit scoring," International Journal of Science and Research Archive, vol. 13, no. 2, pp. 1052-1067, 2024. https://doi.org/10.30574/ijsra.2024.13.2.2258

Singh, V. (2024). The impact of artificial intelligence on compliance and regulatory reporting. J. Electrical Systems, 20(11s), 4322–4328. https://doi.org/10.52783/jes.8484

E. Ok, J. Aria, D. Jose, and C. Diego, "Transforming Credit Risk Mitigation: The Role of AI, Blockchain, and Predictive Analytics," 2024.

Y. Wang, M. Wang, Y. Pan, and J. Chen, "Joint loan risk prediction based on deep learning-optimized stacking model," Engineering Reports, vol. 6, no. 4, p. e12748, 2024. https://doi.org/10.1002/eng2.12748

S. Nayak, "Developing predictive models for financial stability: Integrating behavioral analytics into credit risk management," Journal of Artificial Intelligence & Cloud Computing. SRC/JAICC-E173, vol. 3, no. 5, pp. 2-10, 2024.

S. Yadav, "AI‑driven credit scoring models: Enhancing accuracy and fairness with explainable machine learning," International Journal of Scientific Research in Engineering and Management, vol. 3, no. 12, pp. 1–6, 2024.

O. A. Bello, "Machine learning algorithms for credit risk assessment: An economic and financial analysis," International Journal of Management, vol. 10, no. 1, pp. 109-133, 2023. https://doi.org/10.37745/ijmt.2013/vol10n1109133

B. J. G. Rozo, J. Crook, and G. Andreeva, "The role of web browsing in credit risk prediction," Decision Support Systems, vol. 164, p. 113879, 2023. https://doi.org/10.1016/j.dss.2022.113879

P. K. Roy and K. Shaw, "A credit scoring model for SMEs using AHP and TOPSIS," International Journal of Finance & Economics, vol. 28, no. 1, pp. 372-391, 2023. https://doi.org/10.1002/ijfe.2425

W. Trissia, "The rise of innovative credit scoring system in Indonesia: Assessing risks and policy challenges," Center for Indonesian Policy Studies, 2023. https://doi.org/10.35497/560780

Z. Wang, J. Xiao, L. Wang, and J. Yao, "A novel federated learning approach with knowledge transfer for credit scoring," Decision Support Systems, vol. 177, p. 114084, 2024. https://doi.org/10.1016/j.dss.2023.114084

M. Abdoli, M. Akbari, and J. Shahrabi, "Bagging supervised autoencoder classifier for credit scoring," Expert Systems with Applications, vol. 213, p. 118991, 2023. https://doi.org/10.1016/j.eswa.2022.118991

S. Chatterjee, D. Corbae, K. Dempsey, and J.-V. Ríos-Rull, "A quantitative theory of the credit score," Econometrica, vol. 91, no. 5, pp. 1803-1840, 2023. https://doi.org/10.3982/ECTA18771

Sai Nikhil Donthi. (2025). Improvised Failure Detection for Centrifugal Pumps Using Delta and Python: How Effectively Iot Sensors Data Can Be Processed and Stored for Monitoring to Avoid Latency in Reporting. Frontiers in Emerging Computer Science and Information Technology, 2(10), 24–37. https://doi.org/10.64917/fecsit/Volume02Issue10-03

H. He, Z. Wang, H. Jain, C. Jiang, and S. Yang, "A privacy-preserving decentralized credit scoring method based on multi- party information," Decision Support Systems, vol. 166, p. 113910, 2023. https://doi.org/10.1016/j.dss.2022.113910

M. Md Rakib, H. Md Refadul, A. Tanvir, H. Md Nazmul Hasan, and H. Md Minzamul, "Advanced ai-driven credit risk assessment for buy now, pay later (BNPL) and e-commerce financing: Leveraging machine learning, alternative data, and predictive analytics for enhanced financial scoring," Journal of Business and Management Studies, vol. 6, no. 2, pp. 180- 189, 2024. https://doi.org/10.32996/jbms.2024.6.2.19

M. T. Stow, "Credit risk evaluation in the financial sector using deep learning," GSJ, vol. 12, no. 3, pp. 205-218, 2024. https://doi.org/10.13140/RG.2.2.22311.36008

Kesarpu, S., & Hari Prasad Dasari. (2025). Kafka Event Sourcing for Real-Time Risk Analysis. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3715

E. O. Alonge, N. L. Eyo-Udo, B. C. Ubanadu, A. I. Daraojimba, E. D. Balogun, and K. O. Ogunsola, "Developing an advanced machine learning decision-making model for banking: Balancing risk, speed, and precision in credit assessments," International Journal of Multidisciplinary Research and Growth Evaluation, vol. 5, no. 1, pp. 1567–1581, 2024. https://doi.org/10.54660/.IJMRGE.2024.5.1.1567-1581

J. Eniola and G. Amos, "AI and blockchain: A new era for credit risk mitigation in financial services," International Journal of Science and Research Archive, vol. 13, no. 1, pp. 575–582, 2024.

Adams and A. Owen, "Enhancing Financial Stability through Real-Time Credit Risk Monitoring Using Machine Learning Techniques and Advanced Data Analytics," 2024.

Building Compliance-Driven AI Systems: Navigating IEC 62304 and PCI-DSS Constraints. (2025). International Journal of Networks and Security, 5(01), 62-90. https://doi.org/10.55640/ijns-05-01-06

S. Yadav, "Real-time data processing in credit risk assessment: Enhancing predictive models and decision-making," Journal of Artificial Intelligence, Machine Learning & Data Science, vol. 1, no. 3, pp. 1849–1852, 2023.

G. Nwachukwu, "Enhancing credit risk management through revalidation and accuracy in financial data: The impact of credit history assessment on procedural financing," International Journal of Research Publication and Reviews, vol. 5, no. 11, pp. 631-644, 2024.

Chandra Jha, A. (2025). VXLAN/BGP EVPN for Trading: Multicast Scaling Challenges for Trading Colocations. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3478

Nóvoa, A. Racca, and L. Magri, "Inferring unknown unknowns: Regularized bias-aware ensemble Kalman filter," Computer Methods in Applied Mechanics and Engineering, vol. 418, p. 116502, 2024. https://doi.org/10.1016/j.cma.2023.116502

Y. Yan and E. E. Kuruoglu, "Binarized simplicial convolutional neural networks," Neural Networks, vol. 183, p. 106928, 2025. https://doi.org/10.1016/j.neunet.2024.106928

Jain, R., Sai Santosh Goud Bandari, & Naga Sai Mrunal Vuppala. (2025). Polynomial Regression Techniques in Insurance Claims Forecasting. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3519