Improving the Art, Craft and Science of Economic Credit Risk Scorecards Using Random Forests: Why Credit Scorers and Economists Should Use Random Forests-Reference-Cited by-同舟云学术

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Improving the Art, Craft and Science of Economic Credit Risk Scorecards Using Random Forests: Why Credit Scorers and Economists Should Use Random Forests

Published:2011 Issue: Volume: Page:
ISSN:1556-5068
Container-title:SSRN Electronic Journal
language:en
Short-container-title:SSRN Journal

Author:

Sharma Dhruv

Publisher

Elsevier BV

Reference33 articles.

1. Credit risk, credit scoring, and the performance of home mortgages;Robert B Avery;The Federal Reserve Bulletin,1996

2. Managerial control in Credit Scoring Systems;Ga; Overstreet;Journal of Retail Banking,1986

3. The flat-maximum effect and generic linear scoring models: a test;G A J Overstreet;IMA Journal of Mathematics Applied in Business & Industry,1992

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Hybrid Approach for Predicting Corporate Financial Risk: Integrating SMOTE-ENN and NGBoost;IEEE Access;2023

2. What can we learn from what a machine has learned? Interpreting credit risk machine learning models;The Journal of Risk Model Validation;2021

3. Innovation in Corporate Credit Scoring: Z-Score Optimization;SSRN Electronic Journal;2011

4. I*: Optimizing Logistic Regression to Match Ensemble Performance Using Random Forest Variable Importance;SSRN Electronic Journal;2011

5. A Simple Experiment to Test the Interaction Effects Hypothesis for Random Forests and Logistic Regression;SSRN Electronic Journal;2011

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