Abstract

The growing importance of machine learning in decision-support systems has spurred substantial research into applying machine learning models to data from multiple sectors. Available open-source data on loan defaults has been used to train a variety of machine learning models from statistical models to complex neural networks. The performance of the models is evaluated using various information-theoretic and statistical measures. In addition to a detailed evaluation of the models, several elements associated with their practical adoption in a bank have been presented. Machine learning algorithms are now widely used and external advances in these methods have come, in large part, from academic research. That said, the focus for these models is now shifting to practical elements such as deployment, interpretability, regulatory compliance and operational aspects. To this end, data publicly available from the Lending Club, a leading US provider of loans to individuals and small businesses, has been used to develop different machine learning algorithms for prediction of the default of loans. The deployed solution uses tree-based methods both for interpretability as well as compliance with regulations.

Keywords

  • Machine Learning In Decision Support
  • Loan Default Prediction
  • Credit Risk Modeling
  • Open-Source Fin

References

  1. Lessmann, S., Baesens, B., Seow, H. V., & Thomas, L. C. Benchmarking state-of-the-art classification algorithms for credit scoring. European Journal of Operational Research.
  2. Inala, R. Advancing Group Insurance Solutions Through Ai-Enhanced Technology Architectures And Big Data Insights.
  3. Brown, I., & Mues, C. An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Systems with Applications.
  4. Garapati, R. S. A Web-Centric Cloud Framework for Real-Time Monitoring and Risk Prediction in Clinical Trials Using Machine Learning. Current Research in Public Health.
  5. Louzada, F., Ara, A., & Fernandes, G. B. Classification methods applied to credit scoring. Revista Brasileira de Biometria.
  6. Avinash Reddy Aitha. Deep Neural Networks for Property Risk Prediction Leveraging Aerial and Satellite Imaging. International Journal of Communication Networks and Information Security (IJCNIS). Retrieved from https://www.ijcnis.org/index.php/ijcnis/article/view/.
  7. Huang, C. L., Chen, M. C., & Wang, C. J. Credit scoring with a data mining approach. Expert Systems with Applications.
  8. Nagabhyru, K. C. Bridging Traditional ETL Pipelines with AI Enhanced Data Workflows: Foundations of Intelligent Automation in Data Engineering. Available at SSRN.
  9. Khandani, A. E., Kim, A. J., & Lo, A. W. Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance.
  10. Gottimukkala, V. R. R. Licensing Innovation in the Financial Messaging Ecosystem: Business Models and Global Compliance Impact. International Journal of Scientific Research and Modern Technology.
  11. Abellán, J., Castellano, J. G., & Moral, S. Building classification trees using the total uncertainty criterion. International Journal of Intelligent Systems.
  12. Avinash Reddy Segireddy. Terraform and Ansible in Building Resilient Cloud-Native Payment Architectures. International Journal of Intelligent Systems and Applications in Engineering. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/.
  13. Wang, G., Hao, J., Ma, J., & Jiang, H. A comparative assessment of ensemble learning for credit scoring. Expert Systems with Applications.
  14. Amistapuram, K. Fraud Detection and Risk Modeling in Insurance: Early Adoption of Machine Learning in Claims Processing. Available at SSRN.
  15. García, V., Marqués, A. I., & Sánchez, J. S. Improving risk predictions by preprocessing imbalanced credit data. Knowledge-Based Systems.
  16. Rongali, S. K. AI-Driven Automation in Healthcare Claims and EHR Processing Using MuleSoft and Machine Learning Pipelines. Available at SSRN.
  17. Finlay, S. Multiple classifier architectures and their application to credit risk assessment. European Journal of Operational Research.
  18. Vadisetty, R., Polamarasetti, A., Guntupalli, R., Raghunath, V., Jyothi, V. K., & Kudithipudi, K. AI-Driven Cybersecurity: Enhancing Cloud Security with Machine Learning and AI Agents. Sateesh kumar and Raghunath, Vedaprada and Jyothi, Vinaya Kumar and Kudithipudi, Karthik, AI-Driven Cybersecurity: Enhancing Cloud Security with Machine Learning and AI Agents (February).
  19. Bravo, C., & Batta, R. Explaining credit scoring models. European Journal of Operational Research.
  20. Varri, D. B. S. AI-Driven Risk Assessment And Compliance Automation In Multi-Cloud Environments. Journal of International Crisis and Risk Communication Research. https://doi.org/jicrcr.vi.
  21. Poursabzi-Sangdeh, F., Goldstein, D. G., Hofman, J. M., Vaughan, J. W., & Wallach, H. Manipulating and measuring model interpretability. Proceedings of the ACM CHI Conference.
  22. Kalisetty, S. Leveraging Cloud Computing and Big Data Analytics for Resilient Supply Chain Optimization in Retail and Manufacturing: A Framework for Disruption Management.
  23. Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Pedreschi, D., & Giannotti, F. A survey of methods for explaining black box models. ACM Computing Surveys.
  24. Kothapalli Sondinti, L. R., & Syed, S. The Impact of Instant Credit Card Issuance and Personalized Financial Solutions on Enhancing Customer Experience in the Digital Banking Era. Universal Journal of Finance and Economics. Retrieved from https://www.scipublications.com/journal/index.php/ujfe/article/view/.
  25. Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., et al. Explainable artificial intelligence. Information Fusion.
  26. Annapareddy, V. N. Integrating AI, Machine Learning, and Cloud Computing to Drive Innovation in Renewable Energy Systems and Education Technology Solutions. Available at SSRN.
  27. Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. Explainable machine learning in credit risk management. Computational Economics.
  28. Chakilam, C., Suura, S. R., Koppolu, H. K. R., & Recharla, M. From Data to Cure: Leveraging Artificial Intelligence and Big Data Analytics in Accelerating Disease Research and Treatment Development. Journal of Survey in Fisheries Sciences. https://doi.org/sfs.v.i.
  29. Frost, J., Gambacorta, L., Huang, Y., Shin, H. S., & Zbinden, P. BigTech and the changing structure of financial intermediation. Economic Policy.
  30. Annapareddy, V. N. AI-Driven Optimization of Solar Power Generation Systems Through Predictive Weather and Load Modeling. Available at SSRN.