Explainability and Stability of Machine Learning Applications — A Financial Risk Management Perspective

Journal: Modern Economics & Management Forum DOI: 10.32629/memf.v5i5.2902

Liyang Wang1, Yu Cheng2, Ningjing Sang2, You Yao3

1. Olin Business School, Washington University in St. Louis, St. Louis, MO, 63130, USA
2. The Fu Foundation School of Engineering and Applied Science, Columbia University, New York, NY, 10027, USA
3. Viterbi School of Engineering, University of Southern California, Los Angeles, California. 90089 USA

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

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Copyright © 2024 Liyang Wang, Yu Cheng, Ningjing Sang, You Yao

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