Research ArticleOpen AccessGoogle Scholar indexed
Harnessing Machine Learning Emerging Technology in Financial Investment Industry: Machine Learning Credit Rating Model Implementation
Large Financial International Institution, Washington D.C., USA
Large Financial International Institution, Washington D.C., USA
Large Financial International Institution, Beijing, China
Large Financial International Institution, Washington D.C., USA
Large Financial International Institution, Washington D.C., USA
- 1 Large Financial International Institution, Washington D.C., USA
- 2 Large Financial International Institution, Washington D.C., USA
- 3 Large Financial International Institution, Beijing, China
- 4 Large Financial International Institution, Washington D.C., USA
- 5 Large Financial International Institution, Washington D.C., USA
Journal of Financial Risk Management·Volume 10 (2021)·Pages 317–341·Published 2 August 2021·DOI10.4236/jfrm.2021.103019
Copy link · social · email
Abstract
Credit risk ratings consist of assessing the creditworthiness of the issuer and gauge the risks associated with buying its debt. Any delay in updating the credit risk ratings could have a severe impact on the financial system such as the financial crisis in 2008. This paper discusses a case that leverages emerging technology and breakthrough cognitive analytics in the financial industry. It specifically describes the design and implementation of a predictive modeling case based on the Machine Learning Approach and its application in credit risk forecasting and portfolio management. Using big data and Machine Learning, it is possible to improve credit risk analysis and forecasting by allowing the algorithms to search for patterns using large sets of data.
KeywordsMachine LearningCredit RatingCredit Portfolio ManagementRandom Forest MethodologyMachine Learning Training and Model ValidationArtificial Intelligence
- Bacham, D., & Zhao, J. (2017). Machine Learning: Challenges, Lessons, and Opportunities in Credit Modeling. Moody’s Analytics Risk Perspectives
- Bachar, D., & Zhao, J. (2017). Machine Learning: Challenges, Lessons, and Opportunities in Credit Risk Modeling. Moody’s Analytics Risk Perspectives: Managing Disruption, IX.
- Barboza, F., Kimura, H., & Altman, E. (2017). Machine Learning Models and Bankruptcy Prediction. Expert Systems with Applications, 83, 405-417. https://doi.org/10.1016/j.eswa.2017.04.006
- Bernard, J., Chang, T.-W., Popescu, E., & Graf, S. (2017). Learning Style Identifier: Improving the Precision of Learning Style Identification through Computational Intelligence Algorithms. Expert Systems with Applications, 75, 94-108. https://doi.org/10.1016/j.eswa.2017.01.021
- Boivin, J., & Ng, S. (2006). Are More Data Always Better for Factor Analysis? Journal of Econometrics, 132, 169-194. https://doi.org/10.1016/j.jeconom.2005.01.027
- Breiman, L. (2001). Random Forest. Machine Learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324
- Butaru, F., Chen, Q., Clark, B., Das, S., Lo, A. W., & Siddique, A. (2016). Risk and Risk Management in the Credit Card Industry. Journal of Banking & Finance, 72, 218-239. https://doi.org/10.1016/j.jbankfin.2016.07.015
- Cano, I. (2019). Optimizing Distributed Systems Using Machine Learning. Thesis, University of Washington.
- Caruana, R., & Niculescu-Mizil (2006). An Empirical Comparison of Supervised Learning Algorithms. Proceedings of the 23rd international Conference on Machine Learning, June 2006, Pittsburgh, 161-168. https://doi.org/10.1145/1143844.1143865
- Chen, J., Chollete, L., & Ray, R. (2010). Financial Distress and Idiosyncratic Volatility: An Empirical Investigation. Journal of Financial Markets, 13, 249-267. https://doi.org/10.1016/j.finmar.2009.10.003
- De Menezes, F., Liska, G. R., Cirillo, M., & Vivanco, M. (2016). Data Classification with Binary Response through the Boosting Algorithm and Logistic Regression. Expert Systems with Applications, 69, 62-73. https://doi.org/10.1016/j.eswa.2016.08.014
- Deloitte (2018). Using Random Forest for Credit Risk Models-Machine Learning and Credit Risk: A Suitable Marriage. Deloitte Risk.
- Gambacorta, L., Huang, Y., Qiu, H., & Wang, J. (2019). How Do Machine Learning and Non-Traditional Data Affect Credit Scoring? New Evidence from a Chinese Fintech Firm. BIS Working Papers.