<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE ArticleSet PUBLIC "-//NLM//DTD PubMed 2.7//EN" "https://dtd.nlm.nih.gov/ncbi/pubmed/in/PubMed.dtd">
<ArticleSet>
<Article>
<Journal>
				<PublisherName>Iranian Institute of Industrial Engineering</PublisherName>
				<JournalTitle>Journal of Industrial and Systems Engineering</JournalTitle>
				<Issn>1735-8272</Issn>
				<Volume>15</Volume>
				<Issue>1</Issue>
				<PubDate PubStatus="epublish">
					<Year>2023</Year>
					<Month>11</Month>
					<Day>03</Day>
				</PubDate>
			</Journal>
<ArticleTitle>A review of Credit Rating models: A combined analysis and suggestions for future research.</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>306</FirstPage>
			<LastPage>327</LastPage>
			<ELocationID EIdType="pii">178167</ELocationID>
			
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>Seyed Farid</FirstName>
					<LastName>Mousavi</LastName>
<Affiliation>Department of Information Technology and Operations Management,  Kharazmi University, Tehran,  Iran</Affiliation>
<Identifier Source="ORCID">0000-0001-7617-7994</Identifier>

</Author>
<Author>
					<FirstName>Arezoo</FirstName>
					<LastName>Gazori-Nishabori</LastName>
<Affiliation>Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2023</Year>
					<Month>03</Month>
					<Day>04</Day>
				</PubDate>
			</History>
		<Abstract>The key to solving the problem of obtaining complex facilities is to create a suitable credit rating model that can provide technical support for the approval of granting facilities provided by small and micro enterprises. Credit rating agencies perform assessment to support financial institutions in processing debts. Added literature in the field of credit rating from January 2015 to Aguest 2023 was analyzed to discover opportunities for further research. Bibliometric analysis was used to understand the existing literature. Subsequently, through structured review theories, the methods used by researchers and credit rating agencies were examined. A hybrid literature review was developed by integrating bibliometric and structured review of research articles from widely recognized databases. A sample of 72 articles has been made and studied to identify the gaps in the field of credit rating and create a suitable solution to fill such gaps. The results showed that most studies appeared as post-financial crisis effects reported in 2016 and 2023. It contributes to the existing literature by encouraging researchers and credit rating agencies to develop a specific credit rating system by evaluating existing models and improvising them by adopting advanced techniques such as multiple regression, neural networks, aggregate learning, and machine learning.</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">credit rating</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">credit rating models</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Ensemble Learning</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Machine Learning</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Statistical method</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://www.jise.ir/article_178167_af266ea950e7af1405a054f7a04fca78.pdf</ArchiveCopySource>
</Article>
</ArticleSet>
