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Supervised Machine Learning Techniques: An Overview with Applications to Banking

Author

Listed:
  • Linwei Hu
  • Jie Chen
  • Joel Vaughan
  • Hanyu Yang
  • Kelly Wang
  • Agus Sudjianto
  • Vijayan N. Nair

Abstract

This article provides an overview of Supervised Machine Learning (SML) with a focus on applications to banking. The SML techniques covered include Bagging (Random Forest or RF), Boosting (Gradient Boosting Machine or GBM) and Neural Networks (NNs). We begin with an introduction to ML tasks and techniques. This is followed by a description of: i) tree-based ensemble algorithms including Bagging with RF and Boosting with GBMs, ii) Feedforward NNs, iii) a discussion of hyper-parameter optimization techniques, and iv) machine learning interpretability. The paper concludes with a comparison of the features of different ML algorithms. Examples taken from credit risk modeling in banking are used throughout the paper to illustrate the techniques and interpret the results of the algorithms.

Suggested Citation

  • Linwei Hu & Jie Chen & Joel Vaughan & Hanyu Yang & Kelly Wang & Agus Sudjianto & Vijayan N. Nair, 2020. "Supervised Machine Learning Techniques: An Overview with Applications to Banking," Papers 2008.04059, arXiv.org.
    • Handle: RePEc:arx:papers:2008.04059
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    References listed on IDEAS

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    1. Jing Lei & Max G’Sell & Alessandro Rinaldo & Ryan J. Tibshirani & Larry Wasserman, 2018. "Distribution-Free Predictive Inference for Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1094-1111, July.
    2. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
    3. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
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    Cited by:

    1. Berthine Nyunga Mpinda & Jules Sadefo-Kamdem & Salomey Osei & Jeremiah Fadugba, 2021. "Accuracies of Model Risks in Finance using Machine Learning," Working Papers hal-03191437, HAL.

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