Credit scoring methods: Latest trends and points to consider

被引:22
|
作者
Markov, Anton [1 ]
Seleznyova, Zinaida [1 ]
Lapshin, Victor [1 ]
机构
[1] Natl Res Univ Higher Sch Econ, 20 Myasnitskaya Ulitsa, Moscow 101000, Russia
来源
基金
俄罗斯科学基金会;
关键词
Credit scoring; Survey; Statistics; Machine learning; Data mining; Performance assessment; SUPPORT VECTOR MACHINES; FEATURE-SELECTION; ENSEMBLE CLASSIFICATION; GENETIC ALGORITHM; BIG DATA; DEFAULT PREDICTION; RISK-ASSESSMENT; MODEL; OPTIMIZATION; SYSTEM;
D O I
10.1016/j.jfds.2022.07.002
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
Credit risk is the most significant risk by impact for any bank and financial institution. Accurate credit risk assessment affects an organisation's balance sheet and income statement, since credit risk strategy determines pricing, and might even influence seemingly unrelated domains, e.g. marketing, and decision-making. This article aims at providing a systemic review of the most recent (2016-2021) articles, identifying trends in credit scoring using a fixed set of questions. The survey methodology and questionnaire align with previous similar research that analyses articles on credit scoring published in 1991-2015. We seek to compare our results with previous periods and highlight some of the recent best practices in the field that might be useful for future researchers. (c) 2022 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
引用
收藏
页码:180 / 201
页数:22
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