Explainability and Stability of Machine Learning Applications — A Financial Risk Management Perspective
Journal: Modern Economics & Management Forum DOI: 10.32629/memf.v5i5.2902
Abstract
With advancement in computing power, hardware, and machine learning algorithms, more and more industry sectors have started to incorporate machine learning in the core business. The adoption of machine learning model in risk management is slower, due to the sensitive nature of the tasks, data involved, and regulatory pressure. This paper evaluates the explainability and stability of machine learning models on a traditional financial risk management task and found out that machine learning models can exhibit an enhanced level of adaptability and stability. However, different models could lead to drastically different performance, which require companies to spend additional resources in training and development. Overall, the net benefits are overwhelming, if done correctly.
Keywords
machine learning, risk management, random forest, gradient boosting, bankruptcy
Full Text
PDF - Viewed/Downloaded: 2 TimesReferences
[2]Leo, M., Sharma, S., & Maddulety, K. (2019). Machine learning in banking risk management:[J] A literature review. Risks, 7(1), 29.
[3]Zhang, R., Cheng, Y., Wang, L., Sang, N., & Xu, J. (2023). Efficient Bank Fraud Detection with Machine Learning.[J] Journal of Computational Methods in Engineering Applications, 1-10.
[4]Cheng, Y., Yang, Q., Wang, L., Xiang, A., & Zhang, J. (2024). Research on Credit Risk Early Warning Model of Commercial Banks Based on Neural Network Algorithm. Financial Engineering and Risk Management (2024) Vol. 7: 11-19.[OL]DOI: http://dx.doi.org/10.23977/ferm.2024.070402.
[5]Wang, L., Cheng, Y., Xiang, A., Zhang, J., & Yang, H. (2024). Application of Natural Language Processing in Financial Risk Detection. Financial Engineering and Risk Management (2024) Vol. 7: 1-10.[OL] DOI: http://dx.doi.org/10.23977/ferm.2024.070401.
[6]Li, S., Dong, X., Ma, D., Dang, B., Zang, H., & Gong, Y. (2024). Utilizing the lightgbm algorithm for operator user credit assessment research.[J] Applied and Computational Engineering,75,36-47.
[7]Dang, B., Zhao, W., Li, Y., Ma, D., Yu, Q., & Zhu, E. Y. (2024). Real-Time pill identification for the visually impaired using deep learning.[M]arXiv preprint arXiv:2405.05983.
[8]Dang, B., Ma, D., Li, S., Qi, Z., & Zhu, E. Y. (2024) Deep Learning-Based Snore Sound Analysis for the Detection of Night-time Breathing Disorders.[J]Applied and Computational Engineering,76,109-114.
Copyright © 2024 Liyang Wang, Yu Cheng, Ningjing Sang, You Yao
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License