Skip to main content
SpringerLink
Log in

Financial credit risk assessment: a recent review

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

The assessment of financial credit risk is an important and challenging research topic in the area of accounting and finance. Numerous efforts have been devoted into this field since the first attempt last century. Today the study of financial credit risk assessment attracts increasing attentions in the face of one of the most severe financial crisis ever observed in the world. The accurate assessment of financial credit risk and prediction of business failure play an essential role both on economics and society. For this reason, more and more methods and algorithms were proposed in the past years. From this point, it is of crucial importance to review the nowadays methods applied to financial credit risk assessment. In this paper, we summarize the traditional statistical models and state-of-the-art intelligent methods for financial distress forecasting, with the emphasis on the most recent achievements as the promising trend in this area.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • 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

    Article  Google Scholar 

  • Bae JK (2012) Predicting financial distress of the South Korean manufacturing industries. Expert Syst Appl 39(10):9159–9165

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Bellovary J, Giacomino D, Akers M (2007) A review of bankruptcy prediction studies: 1930 to present. J Financ Educ 33:1–43

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Chapter  Google Scholar 

  • Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Wadsworth, Belmont, CA

    MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Charalambous C, Charitou A, Kaourou F (2000) Application of feature extractive algorithm to bankruptcy prediction. Int Jt Conf Neural Netw 5:303–308

    Google Scholar 

  • 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

    Chapter  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • Cheng KF, Chu CK, Hwang R (2010) Predicting bankruptcy using the discrete-time semi-parametric hazard model. Quant Finance 10(9):1055–1066

    Article  MATH  MathSciNet  Google Scholar 

  • Chuang CL (2013) Application of hybrid case-based reasoning for enhanced performance in bankruptcy prediction. Inf Sci 236:174–185

    Article  MathSciNet  Google Scholar 

  • 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

    Article  MATH  MathSciNet  Google Scholar 

  • Delen D, Kuzey C, Uyar A (2013) Measuring firm performance using financial ratios: a decision tree approach. Expert Syst Appl 40(10):3970–3983

    Article  Google Scholar 

  • 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

    Chapter  Google Scholar 

  • Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    MATH  MathSciNet  Google Scholar 

  • 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

    Article  MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • FitzPatrick PJ (1932) A comparison of the ratios of successful industrial enterprises with those of failed companies. J Account Res 10:598–605

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Hung C, Chen JH (2009) A selective ensemble based on expected probabilities for bankruptcy prediction. Expert Syst Appl 36(3, Part 1):5297–5303

    Article  Google Scholar 

  • Hwang R, Cheng KF, Lee J (2007) A semi-parametric method for predicting bankruptcy. J Forecast 26:317–342

    Article  MathSciNet  Google Scholar 

  • 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

    Article  Google Scholar 

  • Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Kim MJ, Kang DK (2012) Classifiers selection in ensembles using genetic algorithms for bankruptcy prediction. Expert Syst Appl 39(10):9308–9314

    Article  MathSciNet  Google Scholar 

  • Klein RW, Spady RH (1993) An efficient semiparametric estimator for binary response models. Econometrica 61(2):387–421

    Article  MATH  MathSciNet  Google Scholar 

  • Korol T (2013) Early warning models against bankruptcy risk for central european and latin american enterprises. Econ Model 31:22–30

    Article  Google Scholar 

  • Kouki M, Elkhaldi A (2011) Toward a predicting model of firm bankruptcy: evidence from the Tunisian context. Middle East Finance Econ 14:26–43

    Google Scholar 

  • Kuncheva LI (2004) Combining pattern classifiers. Wiley, New York

    Book  MATH  Google Scholar 

  • Kuncheva LI, Whitaker CJ (2003) Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach Learn 51(2):181–207

    Article  MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Li H, Sun J (2011) Principal component case-based reasoning ensemble for business failure prediction. Inf Manage 48(6):220–227

    Article  Google Scholar 

  • Li H, Sun J (2013) Predicting business failure using an RSF-based case-based reasoning ensemble forecasting method. J Forecast 32(2):180–192

    Article  MathSciNet  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  MathSciNet  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Lin Y, Lee Y, Wahba G (2002) Support vector machines for classification in nonstandard situations. Mach Learn 46:191–202

    Article  MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  MATH  MathSciNet  Google Scholar 

  • 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

    Article  Google Scholar 

  • Marqués A, García V, Sánchez J (2012) Two-level classifier ensembles for credit risk assessment. Expert Syst Appl 39(12):10916–10922

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Orsenigo C, Vercellis C (2013) Linear versus nonlinear dimensionality reduction for banks credit rating prediction. Knowl Based Syst 47:14–22

    Article  Google Scholar 

  • 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

    Article  MathSciNet  Google Scholar 

  • Pendharkar P (2008) A threshold varying bisection method for cost sensitive learning in neural networks. Expert Syst Appl 34:1456–1464

    Article  Google Scholar 

  • Peng Y, Kou G, Shi Y, Chen Z (2005) Improving clustering analysis for credit card accounts classification. Lect Notes Comput Sci 3516:548–553

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Ravi V, Kurniawan H, Thai PNK, Kumar PR (2008) Soft computing system for bank performance prediction. Appl Soft Comput 8(1):305–315

    Article  Google Scholar 

  • 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

    Article  MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • Rokach L (2010) Pattern classification using ensemble methods. World Scientific Publishing, Singapore

    MATH  Google Scholar 

  • Serrano-Cinca C, Gutierrez-Nieto B (2013) Partial least square discriminant analysis for bankruptcy prediction. Decis Support Syst 54(3):1245–1255

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  MathSciNet  Google Scholar 

  • 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

    Article  Google Scholar 

  • Sun J, Li H (2012) Financial distress prediction using support vector machines: ensemble versus individual. Appl Soft Comput 12(8):2254–2265

    Article  Google Scholar 

  • 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

    Article  MATH  Google Scholar 

  • Sun Y, Wong AC, Kamel MS (2009) Classification of imbalanced data: a review. Int J Pattern Recognit Artif Intell 23(4):687–719

    Article  Google Scholar 

  • Thomas LC (2000) A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers. Int J Forecast 16(2):149–172

    Article  MATH  Google Scholar 

  • Ting K (2002) An instance-weighting method to induce costsensitive trees. IEEE Trans Knowl Data Eng 14(3):659–665

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Tsai CF, Eberle W, Chu CY (2013) Genetic algorithms in feature and instance selection. Knowl Based Syst 39:240–247

    Article  Google Scholar 

  • 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

    Chapter  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Wong BK, Selvi Y (1998) Neural network applications in finance: a review and analysis of literature (1990–1996). Inf Manage 34(3):129–139

    Article  Google Scholar 

  • Wozniaka M, Granb M, Corchado E (2014) A survey of multiple classifier systems as hybrid systems. Inf Fusion 16:3–17

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

Download references

Acknowledgments

This work was partly supported by Natural Science Foundation of China (Contract No. 91024004).

Author information

Authors and Affiliations

  1. College of Computer Science and Technology (Software College), Henan Polytechnic University, 2001 Century Avenue, Jiaozuo, 454003, Henan, People’s Republic of China

    Ning Chen

  2. GECAD, Instituto Superior de Engenharia do Porto, Instituto Politécnico do Porto, Rua Dr. Antonio Bernardino de Almeida, 431, 4200-072, Porto, Portugal

    Ning Chen

  3. CISUC, Department of Informatics Engineering, University of Coimbra, Rua Silvio Lima-Polo II, 3030-790, Coimbra, Portugal

    Bernardete Ribeiro

  4. Safety and Emergency Management Research Center, Henan Polytechnic University, 2001 Century Avenue, Jiaozuo, 454003, Henan, People’s Republic of China

    An Chen

  5. Institute of Policy and Management, Chinese Academy of Sciences, Beijing, People’s Republic of China

    An Chen

Authors
  1. Ning Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bernardete Ribeiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. An Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ning Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, N., Ribeiro, B. & Chen, A. Financial credit risk assessment: a recent review. Artif Intell Rev 45, 1–23 (2016). https://doi.org/10.1007/s10462-015-9434-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-015-9434-x

Keywords

Search

Navigation