Assessing the Loss Given Default of Bank Loans Using the Hybrid Algorithms Multi-Stage Model

Author:

Fan Mengting1ORCID,Wu Tsung-Hsien2,Zhao Qizhi1

Affiliation:

1. School of Management, Guangdong University of Technology, Guangzhou 510520, China

2. College of Management, Fu Jen Catholic University, New Taipei City 242062, Taiwan

Abstract

The loss given default (LGD) is an important credit risk parameter in the regulatory system for financial institutions. Due to the complex structure of the LGD distribution, we propose a new approach, called the hybrid algorithms multi-stage (HMS) model, to construct a multi-stage LGD prediction model and test it on the US Small Business Administration (SBA)’s small business credit dataset. We then compare the model’s performance under four routes by different evaluation metrics. Finally, pertinent business information and macroeconomic features datasets are added for robustness validation. The results show that HMS performs well and stably for predicting LGD, confirming the superiority of the proposed hybrid unsupervised and supervised machine learning algorithm. Financial institutions can apply the approach to make default predictions based on other credit datasets.

Publisher

MDPI AG

Subject

Information Systems and Management,Computer Networks and Communications,Modeling and Simulation,Control and Systems Engineering,Software

Reference53 articles.

1. Classification methods applied to credit scoring: Systematic review and overall comparison;Louzada;Surv. Oper. Res. Manag. Sci.,2016

2. Assessing credit risk of commercial customers using hybrid machine learning algorithms;Machado;Expert Syst. Appl.,2022

3. Combining classifiers for credit risk prediction;Twala;J. Syst. Sci. Syst. Eng.,2009

4. Basel Committee on Banking Supervision (2003). Overview of The New Basel Capital Accord, Bank for International Settlements.

5. Improvements in loss given default forecasts for bank loans;Hibbeln;J. Bank. Financ.,2013

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3