Machine Learning for Enhanced Credit Risk Assessment: An Empirical Approach
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References listed on IDEAS
- Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020.
"Empirical Asset Pricing via Machine Learning,"
The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
- Shihao Gu & Bryan T. Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," Swiss Finance Institute Research Paper Series 18-71, Swiss Finance Institute.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," NBER Working Papers 25398, National Bureau of Economic Research, Inc.
- Tobias Berg & Valentin Burg & Ana Gombović & Manju Puri, 2020.
"On the Rise of FinTechs: Credit Scoring Using Digital Footprints,"
The Review of Financial Studies, Society for Financial Studies, vol. 33(7), pages 2845-2897.
- Tobias Berg & Valentin Burg & Ana Gombović & Manju Puri, 2018. "On the Rise of FinTechs – Credit Scoring using Digital Footprints," NBER Working Papers 24551, National Bureau of Economic Research, Inc.
- Finlay, Steven, 2011. "Multiple classifier architectures and their application to credit risk assessment," European Journal of Operational Research, Elsevier, vol. 210(2), pages 368-378, April.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
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Keywords
credit risk; computer methods; machine learning;All these keywords.
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