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Research on Multi-Classification of Credit Rating of Small and Medium-Sized Enterprises in Growth Enterprises Board Based on Fuzzy Ordinal Regression Support Vector Machine
2012
International Journal of Economics and Finance
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We selected the data of 90 small and medium-sized companies, used fuzzy theory to calculate the qualitative variables, and reformulated support vector machine for ordinal regression method so that different ...
It is necessary to classify credit rating of small and medium-sized companies in Chinese growth enterprises board. ...
Wan proved support vector machines and statistical methods are better, and that based on numerical simulations Gaussian kernel of support vector machines in financial risk assessment of listed companies ...
doi:10.5539/ijef.v4n3p248
fatcat:4npxrmnskzdwniyo7fje5zrmk4
Credit risk assessment using the factorization machine model with feature interactions
2024
Humanities and Social Sciences Communications
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To illustrate the effectiveness of the factorization machine credit risk assessment model and compare its performance with other classification approaches such as logical regression, support vector machine ...
In this article, we apply the factorization machine model to the field of credit risk assessment. ...
Support vector machine. Support Vector Machine is a binary classification model first proposed by Vapnik (Vapnik, 1995) . ...
doi:10.1057/s41599-024-02700-7
fatcat:3jke3bs7uffdtgrqris744f4pm
Research on Multi-Classification of Credit Rating of Small and Medium-Sized Logistics Companies Based on Ordinal Regression Support Vector Machine
2012
International Journal of Business and Management
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Keywords: Support vector machine for ordinal regression method, Small and medium-sized logistical companies, Credit rating Currently, the qualitatively appraised method used in the practice is mainly the ...
We selected the data of small and medium-sized logistical companies in Beijing, Shanghai and Guangzhou, reformulated ordinal regression support vector machine method so that different input points could ...
Wan proved support vector machines and statistical methods are better, and that based on numerical simulations Gaussian kernel of support vector machines in financial risk assessment of listed companies ...
doi:10.5539/ijbm.v7n3p127
fatcat:ise4doendfgf3ppe3larqbwdry
Support Vector Machines Approach to Credit Assessment
[chapter]
2004
Lecture Notes in Computer Science
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This article applies support vector machines (SVM), a relatively new machine learning technique, to the credit assessment problem for better explanatory power. ...
Credit assessment has attracted lots of researchers in financial and banking industry. ...
This paper applies Support Vector Machine (SVM) to the field of credit assessment. ...
doi:10.1007/978-3-540-25944-2_115
fatcat:hkieyjg7qzfvpmym4azva6ypb4
Machine Learning-Based Risk Management of Credit Sales in Small and Midsize Business
2024
Journal of Informatics Education and Research
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This study redefines credit risk assessment for SMBs via the use of machine learning (ML), hence introducing a disruptive methodology. ...
Sustaining and expanding the finances of small and midsize companies (SMBs) depends on efficient credit risk management. ...
Neural Networks, Random Forests, and Support Vector Machines have performed well in financial applications [5] . Data-Based Methods: Machine learning-based credit risk management is data-driven. ...
doi:10.52783/jier.v4i1.583
fatcat:7en3uoc32rdbnpztwy3bj7vnka
Machine Learning in Banking Risk Management: A Literature Review
2019
Risks
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appear commensurate with the current industry level of focus on both risk management and machine learning. ...
The review has shown that the application of machine learning in the management of banking risks such as credit risk, market risk, operational risk and liquidity risk has been explored; however, it doesn't ...
The support vector machine is seen to be a widely tested and proven machine learning approach. Much empirical work is based on observational data. ...
doi:10.3390/risks7010029
fatcat:laddvv3hxbaxzau5zgjrv5pkhe
Ten-year evolution on credit risk research: a systematic literature review approach and discussion
2020
Ingeniería e Investigación
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Among the results, it was found that machine learning is being extensively applied in Credit Risk Assessment, where applications of Artificial Intelligence (AI) were mostly found, more specifically Artificial ...
In this work, a systematic literature review is proposed which considers both "Credit Risk" and "Credit risk" as search parameters to answer two main research questions: are machine learning techniques ...
credit risk -e.g. support vector machines. ...
doi:10.15446/ing.investig.v40n2.78649
doaj:49fab6209b7f4390938e44fa1c83b518
fatcat:tm5glc2tz5hddmlfqaznc5na4q
Credit Risk Assessment using Machine Learning Techniques
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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Analysis of credit scoring is an effective credit risk assessment technique, which is one of the major research fields in the banking sector. ...
This research paper presents a comparison of various machine learning techniques used to evaluate the credit risk. ...
Credit Risk Assessment using Machine Learning Techniques Varsha Aithal, Roshan David Jathanna Credit Risk Assessment using Machine Learning Techniques An effective method was needed to evaluate the credit ...
doi:10.35940/ijitee.a4936.119119
fatcat:qhsbe4dlrrgf7lbwqpv7433lye
Optimization Strategy of Credit Scoring System based on Support Vector Machine
2024
Advances in Engineering Technology Research
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Focusing on the importance of personal credit scoring in today's credit dynamics, the article explores SVM's versatility in various domains through a literature review. ...
In short, the article presents an original, comprehensive approach to credit risk management that integrates theoretical foundations, literature findings, and empirical experiments to improve the accuracy ...
The Optimization Strategy of Credit Scoring System based on Support Vector Machine For the credit scoring model, the 4 aspects are considered in this section and regarded as the input for the SVM model ...
doi:10.56028/aetr.9.1.558.2024
fatcat:sfleogh6irduvpu4qsslhureti
Machine Learning Applied to Banking Supervision a Literature Review
2021
Risks
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The most relevant ML techniques encompass k-nearest neighbours (KNN), support vector machines (SVM), tree-based models, ensembles, boosting techniques, and artificial neural networks (ANN). ...
Credit risk assessment and stress testing are highlighted topics as well as other risk perspectives, with some references to ML application surveys. ...
Gogas et al. 2018 Outperforming the Ohlson's score with stress-testing tool based on a support-vector machine model to forecast bank failures. ...
doi:10.3390/risks9070136
fatcat:gqjub6czvjao3fbqwf34otgwre
A New Ensemble Model based Support Vector Machine for Credit Assessing
2016
International Journal of Grid and Distributed Computing
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In this paper, we propose a new Support Vector Machine (SVM) based ensemble model (SVM-BRS) to address the issue of credit analysis. ...
With the rapid growth of internet finance, the credit assessing is becoming more and more important. ...
Support Vector Machine The support vector machine, which was developed by Cortes and Vapnik [21] , is a kind of modern classification method applied to credit risk assessment. ...
doi:10.14257/ijgdc.2016.9.6.16
fatcat:cjyoa3fd5bchzjqu3sb2z2rgl4
Machine learning predictivity applied to consumer creditworthiness
2020
Future Business Journal
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We develop Support Vector Machine, Decision Trees, Bagging, AdaBoost and Random Forest models, and compare their predictive accuracy with a benchmark based on a Logistic Regression model. ...
In particular, default prediction is one of the most challenging activities for managing credit risk. ...
Acknowledgements The authors acknowledge CNPq for financial support. ...
doi:10.1186/s43093-020-00041-w
fatcat:zpr3uj2lfvb7be5zhkfn4ptf4a
Credit scoring using the clustered support vector machine
2015
Expert systems with applications
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T. (2015) 'Credit scoring using the clustered support vector machine.', Expert systems with applications., 42 (2). pp. 741-750. ...
ABSTRACT This work investigates the practice of credit scoring and introduces the use of the Clustered Support Vector Machine (CSVM) for credit scorecard development. ...
), K means plus support vector machine with a RBF kernel (K means + SVM-RBF), support vector machine with a RBF kernel (SVM-RBF), linear clustered support vector machine (CSVM-linear), K means plus support ...
doi:10.1016/j.eswa.2014.08.029
fatcat:zztq6wmn2nalzdljhmhv5q4qw4
A novel method for credit scoring based on feature transformation and ensemble model
2021
PeerJ Computer Science
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In this paper, we propose a credit score prediction method based on feature transformation and ensemble model, which is essentially a cascade approach. ...
Credit scoring is a very critical task for banks and other financial institutions, and it has become an important evaluation metric to distinguish potential defaulting users. ...
For example, machine learning techniques such as Bayesian networks, decision trees, and support vector machines have been widely applied to user credit assessment. ...
doi:10.7717/peerj-cs.579
pmid:34151000
pmcid:PMC8189024
fatcat:kuuiyyahynenve7cchbp4ugbk4
Challenges of Financial Risk Management: AI Applications
2021
Management
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Data: The analysis was conducted by reviewing various papers, books and reports on AI applications in financial risk management. ...
Contribution: This paper provides a review of artificial intelligence applications in market risk management, credit risk management and operational risk management. ...
A combination of support vector machine with some other machine learning techniques (e.g., neural networks) showed advantages in comparison with traditional ones. ...
doi:10.7595/management.fon.2021.0015
fatcat:3tpozph4tjcktafm67iu5swgl4
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