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An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments

Sustainability, 2019-01, Vol.11 (3), p.699 [Peer Reviewed Journal]

2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2071-1050 ;EISSN: 2071-1050 ;DOI: 10.3390/su11030699

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  • Title:
    An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments
  • Author: Munkhdalai, Lkhagvadorj ; Munkhdalai, Tsendsuren ; Namsrai, Oyun-Erdene ; Lee, Jong ; Ryu, Keun
  • Subjects: Algorithms ; Artificial intelligence ; Artificial neural networks ; Automation ; Benchmarks ; Credit ratings ; Credit scoring ; Datasets ; Decision making ; Domains ; Human performance ; Image classification ; Learning algorithms ; Machine learning ; Machine translation ; Neural networks ; Object recognition ; Real variables ; Regression analysis ; Regression models ; Scoring models ; Speech recognition ; Support vector machines
  • Is Part Of: Sustainability, 2019-01, Vol.11 (3), p.699
  • Description: Machine learning and artificial intelligence have achieved a human-level performance in many application domains, including image classification, speech recognition and machine translation. However, in the financial domain expert-based credit risk models have still been dominating. Establishing meaningful benchmark and comparisons on machine-learning approaches and human expert-based models is a prerequisite in further introducing novel methods. Therefore, our main goal in this study is to establish a new benchmark using real consumer data and to provide machine-learning approaches that can serve as a baseline on this benchmark. We performed an extensive comparison between the machine-learning approaches and a human expert-based model—FICO credit scoring system—by using a Survey of Consumer Finances (SCF) data. As the SCF data is non-synthetic and consists of a large number of real variables, we applied two variable-selection methods: the first method used hypothesis tests, correlation and random forest-based feature importance measures and the second method was only a random forest-based new approach (NAP), to select the best representative features for effective modelling and to compare them. We then built regression models based on various machine-learning algorithms ranging from logistic regression and support vector machines to an ensemble of gradient boosted trees and deep neural networks. Our results demonstrated that if lending institutions in the 2001s had used their own credit scoring model constructed by machine-learning methods explored in this study, their expected credit losses would have been lower, and they would be more sustainable. In addition, the deep neural networks and XGBoost algorithms trained on the subset selected by NAP achieve the highest area under the curve (AUC) and accuracy, respectively.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2071-1050
    EISSN: 2071-1050
    DOI: 10.3390/su11030699
  • Source: GFMER Free Medical Journals
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
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