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

Enterprise Credit Risk becomes important issue in financial and accounting. It includes bankruptcy prediction, financial distress, corporate performance clustering / prediction and credit risk estimation. This research aims to provide a state-of-the art review of the relative literature and indicate relevant research opportunities. 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. Another consideration which requires future research is the evaluation of relative cost of TypeⅠand TypeⅡerrors.

[1]  Anthony C. Antonakis,et al.  Assessing naïve Bayes as a method for screening credit applicants , 2009 .

[2]  Jian-guo Zhou,et al.  Credit Risk Assessment Using Rough Set Theory and GA-Based SVM , 2008, 2008 The 3rd International Conference on Grid and Pervasive Computing - Workshops.

[3]  Vadlamani Ravi,et al.  Soft computing system for bank performance prediction , 2008, Appl. Soft Comput..

[4]  Raja Noor Ainon,et al.  Credit risk evaluation decision modeling through optimized fuzzy classifier , 2008, 2008 International Symposium on Information Technology.

[5]  Bart Baesens,et al.  Using Neural Network Rule Extraction and Decision Tables for Credit - Risk Evaluation , 2003, Manag. Sci..

[6]  Hiok Chai Quek,et al.  GenSo-EWS: a novel neural-fuzzy based early warning system for predicting bank failures , 2004, Neural Networks.

[7]  Te-Sheng Li,et al.  FEATURE SELECTION FOR CLASSIFICATION BY USING A GA-BASED NEURAL NETWORK APPROACH , 2006 .

[8]  Feng-Chia Li Comparison of the Primitive Classifiers without Features Selection in Credit Scoring , 2009, 2009 International Conference on Management and Service Science.

[9]  Mhand Hifi,et al.  A Hybrid Credit Scoring Model Based on Genetic Programming and Support Vector Machines , 2008, 2008 Fourth International Conference on Natural Computation.

[10]  Thomas E. McKee Rough sets bankruptcy prediction models versus auditor signalling rates , 2003 .

[11]  Hui Li,et al.  Predicting business failure using classification and regression tree: An empirical comparison with popular classical statistical methods and top classification mining methods , 2010, Expert Syst. Appl..

[12]  Soushan Wu,et al.  Credit rating analysis with support vector machines and neural networks: a market comparative study , 2004, Decis. Support Syst..

[13]  Cheng-Ying Wu Using Non-Financial Information to Predict Bankruptcy: A Study of Public Companies in Taiwan , 2004 .

[14]  H. Bian,et al.  Fuzzy-rough nearest-neighbor classification approach , 2003, 22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003.

[15]  So Young Sohn,et al.  Reject inference in credit operations based on survival analysis , 2006, Expert Syst. Appl..

[16]  Arijit Laha Building contextual classifiers by integrating fuzzy rule based classification technique and k-nn method for credit scoring , 2007, Adv. Eng. Informatics.

[17]  Edward I. Altman,et al.  FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY , 1968 .

[18]  Hui Li,et al.  Financial distress prediction based on serial combination of multiple classifiers , 2009, Expert Syst. Appl..

[19]  Lin Ma,et al.  Mining the customer credit using hybrid support vector machine technique , 2009, Expert Syst. Appl..

[20]  S. K. Michael Wong,et al.  Comparison of Rough-Set and Statistical Methods in Inductive Learning , 1986, Int. J. Man Mach. Stud..

[21]  Constantin Zopounidis,et al.  Business failure prediction using rough sets , 1999, Eur. J. Oper. Res..

[22]  Kuldeep Kumar,et al.  Artificial neural network vs linear discriminant analysis in credit ratings forecast: A comparative study of prediction performances , 2006 .

[23]  Maher A. Sid-Ahmed,et al.  Investigating the Performance of Naive- Bayes Classifiers and K- Nearest Neighbor Classifiers , 2007 .

[24]  Sumit Sarkar,et al.  Bayesian Models for Early Warning of Bank Failures , 2001, Manag. Sci..

[25]  Mu-Chen Chen,et al.  Credit scoring with a data mining approach based on support vector machines , 2007, Expert Syst. Appl..

[26]  E. Laitinen Predicting a corporate credit analyst's risk estimate by logistic and linear models , 1999 .

[27]  James A. Ohlson FINANCIAL RATIOS AND THE PROBABILISTIC PREDICTION OF BANKRUPTCY , 1980 .

[28]  Bor-Wen Cheng,et al.  Prediction model building with clustering-launched classification and support vector machines in credit scoring , 2009, Expert Syst. Appl..

[29]  Bart Baesens,et al.  Decision Diagrams in Machine Learning: An Empirical Study on Real-Life Credit-Risk Data , 2004, Diagrams.

[30]  Raquel Florez-Lopez,et al.  Modelling of insurers’ rating determinants. An application of machine learning techniques and statistical models , 2007 .

[31]  Kin Keung Lai,et al.  Least squares support vector machines ensemble models for credit scoring , 2010, Expert Syst. Appl..

[32]  Kyung-shik Shin,et al.  An application of support vector machines in bankruptcy prediction model , 2005, Expert Syst. Appl..

[33]  Huseyin Ince,et al.  A comparison of data mining techniques for credit scoring in banking: A managerial perspective , 2009 .

[34]  Catalina Stefanescu,et al.  The credit rating process and estimation of transition probabilities: A Bayesian approach , 2009 .

[35]  Melody Y. Kiang,et al.  Managerial Applications of Neural Networks: The Case of Bank Failure Predictions , 1992 .

[36]  Arindam Chaudhuri,et al.  Fuzzy Support Vector Machine for bankruptcy prediction , 2011, Appl. Soft Comput..

[37]  Tian-Shyug Lee,et al.  A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines , 2005, Expert Syst. Appl..

[38]  Kin Keung Lai,et al.  Credit scoring using support vector machines with direct search for parameters selection , 2008, Soft Comput..

[39]  Patrick Brézillon,et al.  Lecture Notes in Artificial Intelligence , 1999 .

[40]  Ramesh Sharda,et al.  A neural network model for bankruptcy prediction , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[41]  Wei-Sen Chen,et al.  Using neural networks and data mining techniques for the financial distress prediction model , 2009, Expert Syst. Appl..

[42]  Jen-Ying Shih,et al.  A study of Taiwan's issuer credit rating systems using support vector machines , 2006, Expert Syst. Appl..

[43]  Kenneth Carling,et al.  Corporate credit risk modeling and the macroeconomy , 2007 .

[44]  Ruo-wei Ma,et al.  Building up Default Predicting Model based on Logistic Model and Misclassification Loss , 2007 .

[45]  Kin Keung Lai,et al.  Designing a Hybrid Intelligent Mining System for Credit Risk Evaluation , 2008, J. Syst. Sci. Complex..

[46]  Yingxu Yang,et al.  Adaptive credit scoring with kernel learning methods , 2007, Eur. J. Oper. Res..

[47]  J Vaishnavi.,et al.  Bankruptcy Prediction using SVM and Hybrid SVM Survey , 2011 .

[48]  Mu-Yen Chen,et al.  Predicting corporate financial distress based on integration of decision tree classification and logistic regression , 2011, Expert Syst. Appl..

[49]  Feng-Chia Li,et al.  Combination of feature selection approaches with SVM in credit scoring , 2010, Expert Syst. Appl..

[50]  Jian-Hua Luo,et al.  Empirical Study of Corporation Credit Default Probability Based on Logit Model , 2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.

[51]  Yuzhou Liang,et al.  Application of Discretization in the Use of Logistic Financial Rating , 2009, 2009 International Conference on Business Intelligence and Financial Engineering.

[52]  Rod Ellis,et al.  Investigating the Performance of Tasks , 2012 .

[53]  Huimin Zhao,et al.  A multi-objective genetic programming approach to developing Pareto optimal decision trees , 2007, Decis. Support Syst..

[54]  Chung-Hsing Yeh,et al.  Improving Business Failure Predication Using Rough Sets with Non-financial Variables , 2007, ICANNGA.

[55]  Feng-Chia Li,et al.  The hybrid credit scoring model based on KNN classifier , 2009 .

[56]  Vadlamani Ravi,et al.  Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review , 2007, Eur. J. Oper. Res..

[57]  Shamik Sural,et al.  Credit card fraud detection: A fusion approach using Dempster-Shafer theory and Bayesian learning , 2009, Inf. Fusion.

[58]  So Young Sohn,et al.  Support vector machines for default prediction of SMEs based on technology credit , 2010, Eur. J. Oper. Res..

[59]  Jonathan Crook,et al.  Modelling profitability using survival combination scores , 2007, Eur. J. Oper. Res..

[60]  Zhi Xiao,et al.  The prediction for listed companies' financial distress by using multiple prediction methods with rough set and Dempster-Shafer evidence theory , 2012, Knowl. Based Syst..

[61]  Kin Keung Lai,et al.  A new fuzzy support vector machine to evaluate credit risk , 2005, IEEE Transactions on Fuzzy Systems.

[62]  Ashay Kadam,et al.  Bayesian Inference for Issuer Heterogeneity in Credit Ratings Migration , 2008 .

[63]  Niloofar Yousefi,et al.  A Proposed Classification of Data Mining Techniques in Credit Scoring , 2011 .

[64]  Ricardo Cao,et al.  Modelling consumer credit risk via survival analysis , 2009 .

[65]  Chen Xinhui,et al.  On Consumer Credit Scoring Based on Multi-criteria Fuzzy Logic , 2009, 2009 International Conference on Business Intelligence and Financial Engineering.

[66]  Johan A. K. Suykens,et al.  Faculteit Economie En Bedrijfskunde Hoveniersberg 24 B-9000 Gent Bayesian Kernel-based Classification for Financial Distress Detection Dirk Van Den Poel 4 Bayesian Kernel Based Classification for Financial Distress Detection , 2022 .

[67]  Tsau Young Lin,et al.  A New Rough Sets Model Based on Database Systems , 2003, Fundam. Informaticae.

[68]  Kin Keung Lai,et al.  Dynamic Credit Scoring on Consumer Behavior Using Fuzzy Markov Model , 2009, 2009 Fourth International Multi-Conference on Computing in the Global Information Technology.

[69]  Maria Stepanova,et al.  Survival Analysis Methods for Personal Loan Data , 2002, Oper. Res..