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Corporate Credit Risk Assessment of BIST Companies

Olcay Erdogan, Zafer Konakli
2018 European Scientific Journal  
The applied methods are discriminant analysis, k nearest neighbor (k-NN), support vector machines (SVM), decision trees (DT) and a new hybrid model, namely Artificial Neural Networks with Adaptive Neuro-Fuzzy  ...  In this study, the constructed risk assessment models are on a sample data which consists of financial ratios of enterprises listed in the Bourse Istanbul (BIST). 356 enterprises are classified into three  ...  A hybrid ensemble approach for enterprise credit risk assessment based on Support Vector Machine. /j.eswa.2011.11.003 33. Wang, Y., Wang, S. & Lai, K. (2005).  ... 
doi:10.19044/esj.2018.v14n1p122 fatcat:lz7rxqbpdjd2hh3m4hxgrzpj6i

Ten-year evolution on credit risk research: a systematic literature review approach and discussion

Fernanda Medeiros Assef, Maria Teresinha Arns Steiner
2020 Ingeniería e Investigación  
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  ...  Different steps were followed to select the papers for the analysis, as well as the exclusion criteria, in order to verify only papers with Machine Learning approaches.  ...  Acknowledgments This study was partially funded by PUCPR and by the Coordination for the Improvement of Education Personnel -Brazil (CAPES, represented by thefirst author) and by the National Council for  ... 
doi:10.15446/ing.investig.v40n2.78649 doaj:49fab6209b7f4390938e44fa1c83b518 fatcat:tm5glc2tz5hddmlfqaznc5na4q

Credit scoring in banks and financial institutions via data mining techniques: A literature review

Seyed Mahdi sadatrasoul, Mohammadreza gholamian, Mohammad Siami, Zeynab Hajimohammadi
2013 Journal of Artificial Intelligence and Data Mining  
Also ensemble methods, support vector machines and neural network methods are the most favorite techniques used recently.  ...  The findings of the review reveals that data mining techniques are mostly applied to individual credit score and there are a few researches on enterprise and SME credit scoring.  ...  There are many data mining techniques for classification including support vector machine, and decision tree.  Hybrid approaches: The main idea behind the hybrid approaches is that different methods have  ... 
doi:10.22044/jadm.2013.124 doaj:8e8e42083d7c4db9a5eef786a0f2eaa9 fatcat:rlic3qipxvdubh5stt6xokdczy

Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination

Ying Liu, Lihua Huang
2020 International Journal of Distributed Sensor Networks  
Recently, support vector machines, a supervised learning algorithm, have been widely used in the scope of credit risk management.  ...  In our work, we propose an ensemble support vector machine model to solve the risk assessment of supply chain finance, combined with reducing noises method.  ...  The literature reveals that support vector machines (SVMs) are new techniques to tackle credit risk problem  ... 
doi:10.1177/1550147720903631 fatcat:lirkxl3aqrf7pbkcgdxqm4oyfm

Enterprise Credit Risk Evaluation models: A Review of Current Research Trends

Ming-Chang Lee
2012 International Journal of Computer Applications  
Enterprise Credit Risk becomes important issue in financial and accounting.  ...  We found that the current research trends are necessary a method for reduction the feature subset, many hybrids SVM based model and rough model are proposed.  ...  Some other techniques, such as kernel learning methods [65] , Least squares support vector machine ensemble models [69] , hybrid support SVM ( [9] , [14] , [26] ) used support vector machines for  ... 
doi:10.5120/6311-8643 fatcat:n4gqs53qqzdg7g3hjrzhlmjd5y

Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach

You Zhu, Li Zhou, Chi Xie, Gang-Jin Wang, Truong V. Nguyen
2019 International Journal of Production Economics  
Nevertheless, traditional credit risk forecasting models cannot meet the needs of such forecasting. Many researchers argue that machine learning (ML) approaches are good tools.  ...  Here we propose an enhanced hybrid ensemble ML approach called RS-MultiBoosting by incorporating two classic ensemble ML approaches, random subspace (RS) and MultiBoosting, to improve the accuracy of forecasting  ...  To enforce the diversity of methods, numerous enhanced hybrid ensemble ML approaches have been proposed, such as RS-boosting by and the random subspace-support vector machine (RSB-SVM) by .  ... 
doi:10.1016/j.ijpe.2019.01.032 fatcat:kymujd32yba6pkuo6nrodyf5fe

A Credit Risk Predicting Hybrid Model Based on Deep Learning Technology

Chong Wu, School of Management, Harbin Institute of Technology, Harbin, 150001 China
2021 International Journal of Machine Learning and Computing  
Index Terms-Credit risk evaluation, deep Boltzmann machine, discriminative restricted Boltzmann machine, hybrid classifier.  ...  Credit risk evaluation (CRE) is a very challenging and important management science problem in the domain of financial analysis.  ...  [24] proposed a personal credit risk assessment model based on Stacking ensemble learning.  ... 
doi:10.18178/ijmlc.2021.11.3.1033 fatcat:rfqwnflll5ecxdo6cgbdg2dqn4

Machine Learning Applied to Banking Supervision a Literature Review

Pedro Guerra, Mauro Castelli
2021 Risks  
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).  ...  The objective of this review is to provide a comprehensive walk-through of how the most common ML techniques have been applied to risk assessment in banking, focusing on a supervisory perspective.  ...  Authors Year Affiliation Title Citations Abellan et al. 2017 academia A comparative study on base classifiers in ensemble methods for credit scoring 88 Ala'raj et al. 2016 academia A new hybrid ensemble  ... 
doi:10.3390/risks9070136 fatcat:gqjub6czvjao3fbqwf34otgwre

A Hybrid XGBoost-MLP Model for Credit Risk Assessment on Digital Supply Chain Finance

Yixuan Li, Charalampos Stasinakis, Wee Meng Yeo
2022 Forecasting  
Based on the empirical results, we find that the XGBoost-MLP model has good performance in credit risk assessment, where XGBoost feature selection is important for the credit risk assessment model.  ...  This paper uses a hybrid Extreme Gradient Boosting Multi-Layer Perceptron (XGBoost-MLP) model to assess the credit risk of Digital SCF (DSCF).  ...  Data Availability Statement: Data is publicly available on CSMAR:, accessed on 24 May 2021 and Qichacha:, accessed on 14 May 2021.  ... 
doi:10.3390/forecast4010011 fatcat:kvmx3jgjnfcmfoalpkso5soysa

A Fuzzy Inference System for Credit Scoring using Boolean Consistent Fuzzy Logic

Milica Latinovic, Ivana Dragovic, Vesna Bogojevic Arsic, Bratislav Petrovic
2018 International Journal of Computational Intelligence Systems  
This study proposes implementation of Boolean consistent fuzzy inference system for credit scoring purposes.  ...  Consistent fuzzy logic could contribute to the rightful approval of more loans which in turn would have positive effects on economic growth.  ...  vector machine for the assessment of the potential credit card holders.  ... 
doi:10.2991/ijcis.11.1.31 fatcat:wraujwu25jehzfixyarxz5vtza

Machine Learning in Banking Risk Management: A Literature Review

Martin Leo, Suneel Sharma, K. Maddulety
2019 Risks  
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

Credit risk assessment with a multistage neural network ensemble learning approach

2008 Expert systems with applications  
In this study, a multistage neural network ensemble learning model is proposed to evaluate credit risk at the measurement level. The proposed model consists of six stages.  ...  In the first stage, a bagging sampling approach is used to generate different training data subsets especially for data shortage.  ...  Acknowledgements The authors would like to thank the Editor-in-Chief and reviewers for their recommendation and comments. This  ... 
doi:10.1016/j.eswa.2007.01.009 fatcat:dimmdsso6rejxcjmpp7oliwwoe

Credit Evaluation of SMEs Based on GBDT-CNN-LR Hybrid Integrated Model

Lei Zhang, Qiankun Song, Yingjie Wang
2022 Wireless Communications and Mobile Computing  
Under the background of the increasing demand for credit evaluation and risk prediction, the establishment of an effective credit evaluation model for small- and medium-sized enterprises has become a research  ...  Based on previous studies, this paper proposes a two-layer feature extraction method based on Gradient Boosting Decision Tree (GBDT) and Convolutional Neural Network (CNN).  ...  Based on the analysis and discussion above, this paper aims to establish a GBDT-CNN-LR-based credit risk assessment model for SMEs. e frame diagram is shown in Figure 1 .  ... 
doi:10.1155/2022/5251228 fatcat:vxt3kydhjzfm5cerwbbxrbueay

A literature review on the application of evolutionary computing to credit scoring

A I Marqués, V García, J S Sánchez
2013 Journal of the Operational Research Society  
The last years have seen the development of many credit scoring models for assessing the creditworthiness of loan applicants.  ...  However, the importance of credit grant decisions for financial institutions has caused growing interest in using a variety of computational intelligence techniques.  ...  Acknowledgements-This work has partially been supported by the Spanish Ministry of Education and Science under grant TIN2009-14205 and the Generalitat Valenciana under grant PROMETEO/2010/028.  ... 
doi:10.1057/jors.2012.145 fatcat:3rjlq6nupvcfxnkusxw7a5sohi

Classification methods applied to credit scoring: A systematic review and overall comparison [article]

Francisco Louzada and Anderson Ara and Guilherme B. Fernandes
2016 arXiv   pre-print
The need for controlling and effectively managing credit risk has led financial institutions to excel in improving techniques designed for this purpose, resulting in the development of various quantitative  ...  This paper, therefore, aims to present a systematic literature review relating theory and application of binary classification techniques for credit scoring financial analysis.  ...  support vector machine.  ... 
arXiv:1602.02137v1 fatcat:dhwifp4ypfd43k5wi4y52m7zca
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