Financial credit risk assessment: a recent review

Alaiz-Rodriguez R, Japkowicz N, Tischer P (2008) A visualization-based exploratory tool for classifier comparison with respect to multiple metrics and multiple domains. In: Proceedings of ECML PKDD, pp 660–665 Altman EI (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J Finance 23(4):589–609 Bae JK (2012) Predicting financial distress of the South Korean manufacturing industries. Expert Syst Appl 39(10):9159–9165 Balcaen S, Ooghe H (2006) 35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems. Br Account Rev 38(1):63–93 Bellovary J, Giacomino D, Akers M (2007) A review of bankruptcy prediction studies: 1930 to present. J Financ Educ 33:1–43 Blanco A, Pino-Mejias R, Lara J, Rayo S (2013) Credit scoring models for the microfinance industry using neural networks: evidence from Peru. Expert Syst Appl 40(1):356–364 Brabazon A, Dang J, Dempsey I, O’Neill M, Edelman D (2012) Natural computing in finance: a review. In: Rozenberg G, Back T, Kok J (eds) Handbook of natural computing. Springer, Berlin, pp 1707–1735 Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Wadsworth, Belmont, CA Brezigar-Masten A, Masten I (2009) Comparison of parametric, semi-parametric and non-parametric methods in bankruptcy prediction. IMAD Working Paper Series XVIII, vol 18 Brezigar-Masten A, Masten I (2012) CART-based selection of bankruptcy predictors for the logit model. Expert Syst Appl 39(11):10153–10159 Calderon TG, Cheh JJ (2002) A roadmap for future neural networks research in auditing and risk assessment. Int J Account Inf Syst 3(4):203–236 Canuto AM, Abreu MC, Oliveira LM Jr, Xavier JC, Santos AM (2007) Investigating the influence of the choice of the ensemble members in accuracy and diversity of selection-based and fusion-based methods for ensembles. Pattern Recognit Lett 28(4):472–486 Caruana R, Niculescu-Mizil A (2004) Data mining in metric space: an empirical analysis of suppervised learning performance criteria. In: Proceedings of the 10th international conference on knowledge discovery and data mining Chakraborty S, Sharma SK (2007) Prediction of corporate financial health by artificial neural network. Int J Electron Finance 1(4):442–459 Charalambous C, Charitou A, Kaourou F (2000) Application of feature extractive algorithm to bankruptcy prediction. Int Jt Conf Neural Netw 5:303–308 Chen MY, Chen CC, Liu JY (2013) Credit rating analysis with support vector machines and artificial bee colony algorithm. In: Ali M, Bosse T, Hindriks K, Hoogendoorn M, Jonker CM, Treur J (eds) Recent trends in applied artificial intelligence, LNCS, vol 7906. Springer, Berlin, pp 528–534 Chen N, Chen A, Ribeiro B (2013) Influence of class distribution on cost-sensitive learning: a case study of french bankruptcy analysis. Int J Intell Data Anal 17(3):423–437 Chen N, Ribeiro B (2013) A consensus approach for combining multiple classifiers in cost-sensitive bankruptcy prediction. In: M.T. et al (ed.) 11th international conference on adaptive and natural computing algorithms (ICANNGA’13), LNCS, vol 7824. Springer, Berlin, pp 266–276 Chen N, Ribeiro B, Vieira A, Chen A (2013) Clustering and visualization of bankruptcy trajectory using self-organizing map. Expert Syst Appl 40(1):385–393 Chen N, Ribeiro B, Vieira A, Duarte J, Neves J (2011) A genetic algorithm-based approach to cost-sensitive bankruptcy prediction. Expert Syst Appl 38(10):12939–12945 Chen N, Vieira A (2009) Bankruptcy prediction based on independent component analysis. In: 1st international conference on agents and artificial intelligence (ICAART09). pp 150–155 Chen N, Vieira A, Duarte J, Ribeiro B, Neves J (2009) Cost-sensitive learning vector quantization for financial distress prediction. In: Lecture notes in artificial intelligence (LNAI 5816). Springer, Berlin, pp 374–385 Chen N, Vieira A, Ribeiro B, Duarte J, Neves J (2011) A stable credit rating model based on learning vector quantization. Intell Data Anal 15(2):237–250 Cheng KF, Chu CK, Hwang R (2010) Predicting bankruptcy using the discrete-time semi-parametric hazard model. Quant Finance 10(9):1055–1066 Chuang CL (2013) Application of hybrid case-based reasoning for enhanced performance in bankruptcy prediction. Inf Sci 236:174–185 Coface, for Safer Trade (2012) Risk assessment of Portugal. http://www.coface.com/Economic-Studies-and-Country-Risks/Portugal Crook JN, Edelman DB, Thomas LC (2007) Recent developments in consumer credit risk assessment. Eur J Oper Res 183(3):1447–1465 Delen D, Kuzey C, Uyar A (2013) Measuring firm performance using financial ratios: a decision tree approach. Expert Syst Appl 40(10):3970–3983 Deligianni D, Kotsiantis S (2012) Forecasting corporate bankruptcy with an ensemble of classifiers. In: Maglogiannis I, Plagianakos V, Vlahavas I (eds) Artificial intelligence: theories and applications, LNCS, vol 7297. Springer, Berlin, pp 65–72 Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30 Dimitras A, Zanakis S, Zopounidis C (1996) A survey of business failures with an emphasis on prediction methods and industrial applications. Eur J Oper Res 90(3):487–513 Domingos P (1999) Metacost: a general method for making classifiers cost-sensitive. In: Proceedings of 5th ACM SIGKDD international conference on knowledge discovery and data mining. pp 155–164 Eitrich T, Kless A, Druska C, Meyer W, Grotendorst J (2007) Classification of highly unbalanced CYP450 data of drugs using cost sensitive machine learning techniques. J Chem Inf Model 47:92–103 Erdal HI (2013) Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction. Eng Appl Artif Intell 26(7):1689–1697 Esfandiary N, Azad I, Eftekhari Moghadam AM (2013) Ldt: layered decision tree based on data clustering. In: Proceedings of the 13th Iranian conference on fuzzy systems (IFSC). pp 1–4 Finlay S (2011) Multiple classifier architectures and their application to credit risk assessment. Eur J Oper Res 210(2):368–378 FitzPatrick PJ (1932) A comparison of the ratios of successful industrial enterprises with those of failed companies. J Account Res 10:598–605 Frank A, Asuncion A (2010) UCI machine learning repository. http://archive.ics.uci.edu/ml Fu-yuan H (2008) A genetic fuzzy neural network for bankruptcy prediction in chinese corporations. In: International conference on risk management and engineering management (ICRMEM ’08). pp 542–546 Garcia S, Fernandez A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Inf Sci 180(10):2044–2064 García V, Sánchez JS, Mollineda RA (2012) On the effectiveness of preprocessing methods when dealing with different levels of class imbalance. Knowl Based Syst 25:13–21 Hand DJ, Henley WE (1997) Statistical classification methods in consumer credit scoring: a review. J R Stat Soc Ser A (Stat Soc) 160(3):523–541 Hansen PR, Timmermann A (2012) Choice of sample split in out-of-sample forecast evaluation. Economics Working Papers ECO2012/10 Huang Z, Chen H, Hsu CJ, Chen WH, Wu S (2004) Credit rating analysis with support vector machines and neural networks: a market comparative study. Decis Support Syst 37(4):543–558 Hung C, Chen JH (2009) A selective ensemble based on expected probabilities for bankruptcy prediction. Expert Syst Appl 36(3, Part 1):5297–5303 Hwang R, Cheng KF, Lee J (2007) A semi-parametric method for predicting bankruptcy. J Forecast 26:317–342 Hwang R, Ruey-Ching, Chung H, Chu C (2010) Predicting issuer credit ratings using a semi-parametric method. J Empir Finance 17(1):120–137 Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323 Japkowicz N, Sanghi P, Tischer P (2008) A projection-based framework for classifier performance evaluation. In: Proceedings of European conference on machine learning and knowledge discovery in databases-part 1, vol 5211. LNCS Springer, Heidelberg, pp 548–563 Jayanthi J, Joseph KS, Vaishnavi J (2011) Bankruptcy prediction using SVM and hybrid SVM survey. Int J Comput Appl 33(7):39–45 Jo H, Han I, Lee H (1997) Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis. Expert Syst Appl 13(2):97–108 Khalilia M, Chakrabort S, Popescu M (2011) Predicting disease risks from highly imbalanced data using random forest. BMC Med Inform Decis Mak 11(17):51 Kim MJ, Kang DK (2012) Classifiers selection in ensembles using genetic algorithms for bankruptcy prediction. Expert Syst Appl 39(10):9308–9314 Klein RW, Spady RH (1993) An efficient semiparametric estimator for binary response models. Econometrica 61(2):387–421 Korol T (2013) Early warning models against bankruptcy risk for central european and latin american enterprises. Econ Model 31:22–30 Kouki M, Elkhaldi A (2011) Toward a predicting model of firm bankruptcy: evidence from the Tunisian context. Middle East Finance Econ 14:26–43 Kuncheva LI (2004) Combining pattern classifiers. Wiley, New York Kuncheva LI, Whitaker CJ (2003) Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach Learn 51(2):181–207 Kwak W, Shi Y, Kou G (2012) Bankruptcy prediction for Korean firms after the 1997 financial crisis: using a multiple criteria linear programming data mining approach. Rev Quant Finance Account 38(4):441–453 Lam M, Trinkle BS (2014) Using prediction intervals to improve information quality of bankruptcy prediction models, chap. 8, pp 37–52 Li H, Adeli H, Sun J, Han JG (2011) Hybridizing principles of TOPSIS with case-based reasoning for business failure prediction. Comput Oper Res 38(2):409–419 Li H, Sun J (2011) Empirical research of hybridizing principal component analysis with multivariate discriminant analysis and logistic regression for business failure prediction. Expert Syst Appl 38(5):6244–6253 Li H, Sun J (2011) Principal component case-based reasoning ensemble for business failure prediction. Inf Manage 48(6):220–227 Li H, Sun J (2013) Predicting business failure using an RSF-based case-based reasoning ensemble forecasting method. J Forecast 32(2):180–192 Li H, Sun J, Wu J (2010) Predicting business failure using classification and regression tree: an empirical comparison with popular classical statistical methods and top classification mining methods. Expert Syst Appl 37(8):5895–5904 Li J, Pan L, Chen M, Yang X (2014) Parametric and non-parametric combination model to enhance overall performance on default prediction. J Syst Sci Complex 27(5):950–969. doi:10.1007/s11424-014-3273-8 Li MYL, Miu P (2010) A hybrid bankruptcy prediction model with dynamic loadings on accounting-ratio-based and market-based information. J Empir Finance 17(4):818–833 Lin F, Yeh C, Lee M (2013) A hybrid business failure prediction model using locally linear embedding and support vector machines. Rom J Econ Forecast 1:82–97 Lin F, Yeh CC, Lee MY (2011) The use of hybrid manifold learning and support vector machines in the prediction of business failure. Knowl Based Syst 24(1):95–101 Lin SW, Ying KC, Chen SC, Lee ZJ (2008) Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Syst Appl 35(4):1817–1824 Lin WY, Hu YH, Tsai CF (2012) Machine learning in financial crisis prediction: a survey. IEEE Trans Syst Man Cybern C Appl Rev 42(4):421–436 Lin Y, Lee Y, Wahba G (2002) Support vector machines for classification in nonstandard situations. Mach Learn 46:191–202 Liu XY, Zhou Z (2006) The influence of class imbalance on cost-sensitive learning: An empirical study. In: Proceedings of 6th IEEE international conference on data mining (ICDM06). pp 970–974 Lorena AC, Carvalho AC, Gama JM (2008) A review on the combination of binary classifiers in multiclass problems. Artif Intell Rev 30(1–4):19–37 Lourenco A, Bulo SR, Rebagliati N, Fred ALN, Figueiredo MAT, Pelillo M (2015) Probabilistic consensus clustering using evidence accumulation. Mach Learn 98(1–2):331–357 Marinakis Y, Marinaki M, Doumpos M, Zopounidis C (2009) Ant colony and particle swarm optimization for financial classification problems. Expert Syst Appl 36(7):10604–10611 Marqués A, García V, Sánchez J (2012) Two-level classifier ensembles for credit risk assessment. Expert Syst Appl 39(12):10916–10922 Min JH, Jeong C, Kim M (2011) Tuning the architecture of support vector machine: the case of bankruptcy prediction. Int J Manage Sci 17(1):1–116 Min JH, Lee YC (2005) Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Syst Appl 28(4):603–614 Musehane R, Netshiongolwe F, Nelwamondo FV, Masisi L, Marwala T (2008) Relationship between diversity and perfomance of multiple classifiers for decision support. Comput Res Repos. abs/0810.3 Nanni L, Lumini A (2009) An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring. Expert Syst Appl 36(2, Part 2):3028–3033 Orsenigo C, Vercellis C (2013) Linear versus nonlinear dimensionality reduction for banks credit rating prediction. Knowl Based Syst 47:14–22 Pai GR, Annapoorani R, Pai GV (2004) Performance analysis of a statistical and an evolutionary neural network based classifier for the prediction of industrial bankruptcy. In: IEEE conference on cybernetics and intelligent systems. pp 1033–1038 Park CS, Han I (2002) A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction. Expert Syst Appl 23(3):255–264 Pendharkar P (2008) A threshold varying bisection method for cost sensitive learning in neural networks. Expert Syst Appl 34:1456–1464 Peng Y, Kou G, Shi Y, Chen Z (2005) Improving clustering analysis for credit card accounts classification. Lect Notes Comput Sci 3516:548–553 Rafiei FM, Manzari S, Bostanian S (2011) Financial health prediction models using artificial neural networks, genetic algorithm and multivariate discriminant analysis: Iranian evidence. Expert Syst Appl 38(8):10210–10217 Ravi V, Kurniawan H, Thai PNK, Kumar PR (2008) Soft computing system for bank performance prediction. Appl Soft Comput 8(1):305–315 Ravi Kumar P, Ravi V (2007) Bankruptcy prediction in banks and firms via statistical and intelligent techniques: a review. Eur J Oper Res 180(1):1–28 Ravikumar P, Ravi V (2006) Bankruptcy prediction in banks by an ensemble classifier. In: IEEE international conference on industrial technology. pp 2032–2036 Ravisankar P, Ravi V, Bose I (2010) Failure prediction of dotcom companies using neural networkcgenetic programming hybrids. Inf Sci 180(8):1257–1267 Ribeiro B, Chen N (2011) Graph weighted subspace learning models in bankruptcy. In: Proceedings IEEE international joint conference on neural networks (IJCNN). pp 2055–2061 Ribeiro B, Chen N (2012a) Biclustering and subspace learning with regularization for financial risk analysis. In: Proceedings of international conference on neural information processing, part II, LNCS, vol 7664. pp 616–623 Ribeiro B, Chen N (2012b) Biclustering and subspace learning with regularization for financial risk analysis. In: T.H. et al. (ed.) Proceedings of the 19th international conference on neural information processing (ICONIP), part III, LNCS, vol 7665. Springer, Berlin, pp 228–235 Ribeiro B, Silva C, Chen N, Vieira A, Neves J (2012) Enhanced default disk models with SVM+. Expert Syst Appl 39:10140–10152 Ribeiro B, Vieira A, Duarte J, Silva C, Neves J, Liu Q, Sung A (2009) Learning manifolds for bankruptcy analysis. In: M. Köppen, et al. (eds.) International conference on neural information processing, vol 5506. LNCS, Springer, Berlin, pp 722–729 Ribeiro B, Vieira A, Neves JC (2008) Supervised Isomap with dissimilarity measures in embedding learning. LNCS 5197:389–396 Rokach L (2010) Pattern classification using ensemble methods. World Scientific Publishing, Singapore Serrano-Cinca C, Gutierrez-Nieto B (2013) Partial least square discriminant analysis for bankruptcy prediction. Decis Support Syst 54(3):1245–1255 Soltan A, Mohammadi M (2012) A hybrid model using decision tree and neural network for credit scoring problem. Manage Sci Lett 2(5):1683–1688 Sun J, Jia M, Li H (2011) AdaBoost ensemble for financial distress prediction: an empirical comparison with data from Chinese listed companies. Expert Syst Appl 38(8):9305–9312 Sun J, Li H (2008) Listed companies’ financial distress prediction based on weighted majority voting combination of multiple classifiers. Expert Syst Appl 35(3):818–827 Sun J, Li H (2012) Financial distress prediction using support vector machines: ensemble versus individual. Appl Soft Comput 12(8):2254–2265 Sun Y, Kamel MS, Wang Y (2006) Boosting for learning multiple classes with imbalanced class distribution. In: Proceedings of the sixth IEEE international conference on data mining. pp 592–602 Sun Y, Kamela M, Wong A, Wang Y (2007) Cost-sensitive boosting for classification of imbalanced data. Pattern Recogn 40:3358–3378 Sun Y, Wong AC, Kamel MS (2009) Classification of imbalanced data: a review. Int J Pattern Recognit Artif Intell 23(4):687–719 Thomas LC (2000) A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers. Int J Forecast 16(2):149–172 Ting K (2002) An instance-weighting method to induce costsensitive trees. IEEE Trans Knowl Data Eng 14(3):659–665 Ting KM (1994) The problem of small disjuncts: its remedy in decision trees. In: Proceedings of the tenth Canadian conference on artificial intelligence. pp 91–97 Tsai CF (2009) Feature selection in bankruptcy prediction. Knowl Based Syst 22(2):120–127 Tsai CF, Eberle W, Chu CY (2013) Genetic algorithms in feature and instance selection. Knowl Based Syst 39:240–247 Tulyakov S, Jaeger S, Govindaraju V, Doermann D (2008) Review of classifier combination methods. In: Marinai S, Fujisawa H (eds) Machine learning in document analysis and recognition, studies in computational intelligence, vol 90. Springer, Berlin, pp 361–386 Turney P (2000) Types of cost in inductive concept leaning. In: Workshop on cost-sensitive learning at 7th international conference on machine learning Vellido A, Lisboa P, Vaughan J (1999) Neural networks in business: a survey of applications (1992–1998). Expert Syst Appl 17(1):51–70 Verikas A, Kalsyte Z, Bacauskiene M, Gelzinis A (2010) Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey. Soft Comput 14(9):995–1010 Vo N, Won Y (2007) Classification of unbalanced medical data with weighted regularized least squares. In: Frontiers in the convergence of bioscience and information technologies. pp 347–352 Wang G, Ma J (2012) A hybrid ensemble approach for enterprise credit risk assessment based on support vector machine. Expert Syst Appl 39(5):5325–5331 Wong BK, Bodnovich TA, Selvi Y (1997) Neural network applications in business: a review and analysis of the literature (1988–1995). Decis Support Syst 19(4):301–320 Wong BK, Selvi Y (1998) Neural network applications in finance: a review and analysis of literature (1990–1996). Inf Manage 34(3):129–139 Wozniaka M, Granb M, Corchado E (2014) A survey of multiple classifier systems as hybrid systems. Inf Fusion 16:3–17 Xie G, Zhao Y, Jiang M, Zhang N (2013) A novel ensemble learning approach for corporate financial distress forecasting in fashion and textiles supply chains. Math Probl Eng 23(2):388–400 Yang Z, You W, Ji G (2011) Using partial least squares and support vector machines for bankruptcy prediction. Expert Syst Appl 38(7):8336–8342 Yeh CC, Lin F, Hsu CY (2012) A hybrid KMV model, random forests and rough set theory approach for credit rating. Knowl Based Syst 33:166–172 Yin H, Leong T (2010) A model driven approach to imbalanced data sampling in medical decision making. Stud Health Technol Inform 160(Pt 2):856–860 Zadrozny B, Elkan C (2001) Learning and making decisions when costs and probabilities are both unknown. In: Proceedings of the seventh international conference on knowledge discovery and data mining. pp 204–213 Zhang L, Zhang L, Teng W, Chen Y (2013) Based on information fusion technique with data mining in the application of finance early-warning. Proc Comput Sci 17:695–703 Zhou L, Lai KK, Yen J (2012) Empirical models based on features ranking techniques for corporate financial distress prediction. Comput Math Appl 64(8):2484–2496 Zhou Z (2012) Ensemble methods: foundations and algorithms. CRC Press, Boca Racton Zhou Z, Liu X (2006) Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Trans Knowl Data Eng 18(1):63–77