Matthew Harding, Gabriel F. R. Vasconcelos
arXiv 9 Feb 2022 · Econometrics · 2 citations (OpenAlex)
arXiv:2202.04218 · PDF · DOI · OpenAlex · Extracted main text
We use machine learning techniques to investigate whether it is possible to replicate the behavior of bank managers who assess the risk of commercial loans made by a large commercial US bank. Even though a typical bank already relies on an algorithmic scorecard process to evaluate risk, bank managers are given significant latitude in adjusting the risk score in order to account for other holistic factors based on their intuition and experience. We show that it is possible to find machine learning algorithms that can replicate the behavior of the bank managers. The input to the algorithms consists of a combination of standard financials and soft information available to bank managers as part of the typical loan review process. We also document the presence of significant heterogeneity in the adjustment process that can be traced to differences across managers and industries. Our results highlight the effectiveness of machine learning based analytic approaches to banking and the potential challenges to high-skill jobs in the financial sector.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Leo Breiman (2017) Classification and regression trees | 0.644 | 2 | 2 | 100% |
| 2 | Max Kuhn et al (2008) Building predictive models in r using the caret package | 0.644 | 2 | 2 | 100% |
| 3 | Jerome H Friedman (2001) Greedy function approximation: a gradient boosting machine | 0.511 | 2 | 1 | 100% |
| 4 | Tianqi Chen, Tong He, Michael Benesty, Vadim Khotilovich, Yuan Tang,… (2019) xgboost: Extreme Gradient Boosting, 2019 | 0.511 | 2 | 1 | 100% |
| 5 | Simona Abis (2017) Man vs. machine: Quantitative and discretionary equity management | 0.405 | 1 | 1 | 100% |
| 6 | David Autor, David Mindell, and Elizabeth Reynolds (2020) The work of the future: Building better jobs in an age of intelligent machines | 0.405 | 1 | 1 | 100% |
| 7 | Andrii Babii, Xi Chen, Eric Ghysels, and Rohit Kumar (2020) Binary choice with asymmetric loss in a data-rich environment: Theory and an application to racial justice | 0.405 | 1 | 1 | 100% |
| 8 | Leo Breiman (1996) Bagging predictors | 0.405 | 1 | 1 | 100% |
| 9 | Leo Breiman (2001) Random forests | 0.405 | 1 | 1 | 100% |
| 10 | Peter Bühlmann (2006) Boosting for high-dimensional linear models | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 40 scored citations.