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Decision Models in Credit Risk Management

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Decision Models in Engineering and Management

Part of the book series: Decision Engineering ((DECENGIN))

Abstract

Economic crises that emerge from systemic risks suggest that credit risk management in banks is paramount not only for the survival of companies themselves but also for a resilient worldwide economy. Although regulators establish strictly standards for financial institutions, i.e., capital requirements and management best practices, unpredictability of market behavior and complexity of financial products may have strong impact on corporate performance, jeopardizing institutions, and even economies. In this chapter, we will explore decision models to manage credit risks, focusing on probabilistic and statistical methods that are coupled with machine learning techniques. In particular, we discuss and compare two ensemble methods, bagging and boosting, in studies of application scoring.

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References

  • Alfaro E, Garcia N, Gámez M, Elizondo D (2008) Bankruptcy forecasting: an empirical comparison of AdaBoost and neural networks. Decis Support Syst 45(1):110–122

    Article  Google Scholar 

  • Altman E (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J Finance 23(4):589–609

    Article  Google Scholar 

  • Bache K, Lichman M (2013) UCI machine learning repository, School of Information and Computer Science. University of California, Irvine. http://archive.ics.uci.edu/ml. Accessed 10 Sep 2014

  • Bartlett PL, Shawe-Taylor J (1999) Generalization performance of support vector machines and other pattern classifiers. In: Schölkopf B, Burges CJC, Smola AJ (eds) Advances in Kernel Methods—Support Vector Learning. MIT Press, Cambridge, pp 43–54

    Google Scholar 

  • Bauer E, Kohavi R (1999) An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Mach Learn 36:105–139

    Article  Google Scholar 

  • BIS (2006) Bank of International Settlements. Basel II: International Convergence of Capital Measurement and Capital Standards: A Revised Framework—Comprehensive version. http://www.bis.org/publ/bcbs128.pdf. Accessed 10 Sep 2014

  • Breiman L (1996) Bagging predictors. Mach Learn 24:123–140

    MathSciNet  MATH  Google Scholar 

  • Breiman L (1998) Arcing classifiers. Ann Stat 26(3):801–849

    Article  MathSciNet  MATH  Google Scholar 

  • Breiman L, Freidman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth International Group, Wadsworth

    Google Scholar 

  • Bruzzone L, Cossu R, Vernazza G (2004) Detection of land-cover transitions by combining multidate classifiers. Pattern Recogn Lett 25(13):1491–1500

    Article  Google Scholar 

  • Bühlmann P, Yu B (2003) Boosting with the L2 loss: regression and classification. J Am Stat Assoc 98:324–339

    Article  MATH  Google Scholar 

  • Burns P (2002) Retail credit risk modeling and the Basel Capital Accord. Discussion paper, Payment Cards Center. https://www.philadelphiafed.org/consumer-credit-and-payments/payment-cards-center/publications/discussion-papers/2002/CreditRiskModeling_012002.pdf January 2002. Accessed 10 Sept 2014

  • Chien BC, Lin JY, Yang WP (2006) A classification tree based on discriminant functions. J Inf Sci Eng 22(3):573–594

    MathSciNet  Google Scholar 

  • Coffman JY (1986) The proper role of tree analysis in forecasting the risk behavior of borrowers, management decision systems. MDS Reports, Atlanta

    Google Scholar 

  • Dietterich T (2000) An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting and randomization. Mach Learn 40(2):139–157

    Article  Google Scholar 

  • Durand D (1941) Risk elements in consumer installment financing. Studies in consumer installment financing: Study 8, National Bureau of Economic Research

    Google Scholar 

  • Feldesman MR (2002) Classification trees as an alternative to linear discriminant analysis. Am J Phys Anthropol 119(3):257–275

    Article  Google Scholar 

  • Fisher R (1936) The use of multiple measurements in taxonomic problems. Ann Hum Genet 7(2):179–188

    Google Scholar 

  • Freund Y, Schapire RE (1998) Discussion of the paper “Arcing Classifiers” by Leo Breiman. Ann Stat 26(3):824–832

    MathSciNet  Google Scholar 

  • Freund Y, Schapire R (1999) A short introduction to boosting. J Jpn Soc Artif Intell 14(5):771–780

    Google Scholar 

  • Hsieh NC, Hung LP (2010) A data driven ensemble classifier for credit scoring analysis. Expert Syst Appl 37(1):534–545

    Article  Google Scholar 

  • Jobson J (1992) Applied multivariate data analysis. Springer Texts in Statistics, New York

    Google Scholar 

  • Johnson RA, Wichern DW (2007) Applied multivariate statistical analysis. 6th edn, Prentice hall Englewood Cliffs

    Google Scholar 

  • Khandani AE, Adlar JK, Lo AW (2010) Consumer credit-risk models via machine-learning algorithms. J Bank Finance 34:2767–2787

    Article  Google Scholar 

  • Klecka WR (1980) Discriminant analysis. Quantitative applications in the social sciences. Sage Publications, Beverly Hills

    Google Scholar 

  • Kuzey C, Uyar A, Delen D (2014) The impact of multinationality on firm value: a comparative analysis of machine learning techniques. Decis Support Syst 59:127–142

    Article  Google Scholar 

  • Lai KL, Yu L, Shouyang W, Zhou L (2006) Credit risk analysis using a reliability-based neural network ensemble model. Lecture notes in computer science, Artificial neural networks—ICANN

    Google Scholar 

  • Lai KL, He K, Yen J (2007) Modeling VaR in crude oil market: a multi scale nonlinear ensemble approach incorporating wavelet analysis and ANN. Lecture notes in computer science, computational science—ICCS

    Google Scholar 

  • Langley P (1995) Elements of machine learning. Morgan Kaufmann Series in Machine Learning. 1st edn, Morgan Kaufmann, Burlington

    Google Scholar 

  • Leigh W, Purvis R, Ragusa JM (2002) Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural networks, and genetic algorithm: a case study in romantic decision support. Decis Support Syst 32(4):361–377

    Article  Google Scholar 

  • Maclin R, Opitz D (1997) An empirical evaluation of bagging and boosting. In: Proceedings of the 14th national conference on artificial intelligence. Cambridge, MA, pp 546–551

    Google Scholar 

  • Maimon O, Rokach L (2004) Ensemble of decision trees for mining manufacturing data sets. Mach Eng 4:l–2

    Google Scholar 

  • Mokeddem D, Belbachir H (2009) A survey of distributed classification based ensemble data mining methods. J Appl Sci 9(20):3739–3745

    Article  Google Scholar 

  • Opitz D, Maclin R (1999) Popular ensemble methods: an empirical study. J Artif Intell Res 11:169–198

    Google Scholar 

  • Paleologo G, Elisseef A, Antonini G (2010) Subagging for credit scoring models. Eur J Oper Res 201(2):490–499

    Article  Google Scholar 

  • Pampel FC (2000) Logistic regression: a primer. Quantitative applications in the social sciences. Sage Publications, Beverly Hills

    Google Scholar 

  • Press JS, Wilson S (1978) Choosing between logistic regression and discriminant analysis. J Am Stat Assoc 73(364):699–705

    Article  MATH  Google Scholar 

  • Quinlan R (1987) Simplifying decision trees. Int J Man-Mach Stud 27:221–234

    Article  Google Scholar 

  • Quinlan R (1992) C4.5: programs for machine learning. Morgan Kaufmann, Los Altos

    Google Scholar 

  • Rokach L (2005) Ensemble methods for classifiers. Data mining and knowledge. discovery handbook. Springer, New York

    Google Scholar 

  • Rokach L (2009) Taxonomy for characterizing ensemble methods in classification tasks: a review and annotated bibliography. Comput Stat Data Anal 53(12):4046–4072

    Article  MathSciNet  MATH  Google Scholar 

  • Schooner HM, Talor MW (2010) The new capital adequacy framework: Basel II and credit risk. Global Bank Regulation, pp 147–164

    Google Scholar 

  • Skurichina M, Duin RPW (2002) Bagging, boosting and the random subspace method for linear classifiers. Pattern Anal Appl 5:121–135

    Article  MathSciNet  MATH  Google Scholar 

  • Tan AC, Gilbert D, Deville Y (2003) Multi-class protein fold classification using a new ensemble machine learning approach. Genome Inf 14:206–217

    Google Scholar 

  • Thomas LC, Edelman DB, Crook JN (2002) Credit scoring and its applications. Society for Industrial and Applied Mathematics, Philadelphia

    Google Scholar 

  • Tukey JW (1977) Exploratory data analysis. Addison-Wesley, Reading

    Google Scholar 

  • Tumer K, Ghosh J (2001) Robust order statistics based ensembles for distributed data mining. In Kargupta H, Chan P (eds) Advances in distributed and parallel knowledge discovery. AAAIMIT Press, Cambridge, pp 185–210

    Google Scholar 

  • Ugalde HMR, Carmona JC, Alvarado VM, Reyes-Reyes J (2013) Neural network design and model reduction approach for black box nonlinear system identification with reduced number of parameters. Neurocomputing 101:170–180

    Article  Google Scholar 

  • Xie H, Han S, Shu X, Yang X, Qu X, Zheng S (2009) Solving credit scoring problem with ensemble learning: a case study. In: Proceedings of the 2nd international symposium on knowledge acquisition and modeling. Wuhan, China, pp 51–54

    Google Scholar 

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Authors and Affiliations

  1. Faculdade de Economia, Administração e Contabilidade, Universidade de Brasília, Campus Darcy Ribeiro Prédio da FACE Asa Norte, Brasília, DF, 70910900, Brazil

    Herbert Kimura

  2. Universidade Presbiteriana Mackenzie, Rua da Consolação, 930 - Consolação, São Paulo, 01302090, Brazil

    Leonardo Fernando Cruz Basso

  3. Faculdade de Economia, Administração e Contabilidade FEA-USP, Avenida Professor Luciano Gualberto, 908, Butantã, São Paulo, SP, 05508010, Brazil

    Eduardo Kazuo Kayo

Authors
  1. Herbert Kimura
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  2. Leonardo Fernando Cruz Basso
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  3. Eduardo Kazuo Kayo
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Corresponding author

Correspondence to Herbert Kimura .

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Editors and Affiliations

  1. Faculdade de Economia, Administração e Contabilidade, University of Brasilia, Brasília, Brazil

    Patricia Guarnieri

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Kimura, H., Basso, L.F.C., Kayo, E.K. (2015). Decision Models in Credit Risk Management. In: Guarnieri, P. (eds) Decision Models in Engineering and Management. Decision Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-11949-6_4

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  • DOI: https://doi.org/10.1007/978-3-319-11949-6_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11948-9

  • Online ISBN: 978-3-319-11949-6

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