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Intelligent Credit Risk Decision Support: Architecture and Implementations

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Artificial Intelligence in Financial Markets

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Abstract

Recent breakthroughs in technology relevant to data analytics delivered many challenging opportunities for financial stakeholders. Continuously developed and applied novel artificial intelligence and machine learning techniques were proved to overcome the limitations of traditional statistical techniques and result in superior performance results. The leading financial rating companies, such as, Standard and Poor’s and Moody’s, are known to apply these techniques for practical rating of companies and financial instruments. Considering the growing importance of such techniques in industry and related research, this chapter attempts to present a review of such techniques applied to analyze insolvency, financial problems, develop rating systems and make financial decisions. Expert systems (ES) and decision support systems (DSS) are other important topics as their development involves many issues including consideration of which financial data should be used to implement such systems, the availability, integrity and quality. Several standards, such as XBRL (Extensible Business Language) or SDMX (Statistical Data and Metadata eXchange), provide means to solve these issues by ensuring relevant characteristics. As at the time of writing no modern DSS framework is known to propose such functionality, this chapter also presents an example framework for such financial standards-based DSS for credit risk evaluation, with respect to its architecture and implementation topics.

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Notes

  1. 1.

    The Vapnik-Chervonenkis dimension, or VC dimension, measures the flexibility of the classification algorithm [97].

  2. 2.

    A measure based on entropy from the area of information theory.

  3. 3.

    Machine learning repository, hosted by Center for Machine Learning and Intelligent Systems at the University of California, Irvine, which provides many of datasets that are frequently used in ML-related research.

References

  1. Shearer, C. (2000). The CRISP-DM model: The new blueprint for data mining. Journal of Data Warehousing, 5(4), 13–22.

    Google Scholar 

  2. Dunham, M. H. (2003). Data Mining: Introductory and advanced topics. Upper Saddle River: Prentice-Hall.

    Google Scholar 

  3. Danenas, P., & Garsva, G. (2009). Support vector machines and their application in credit risk evaluation process. Transformations in Business & Economics, 8, No. 3(18), 46–58.

    Google Scholar 

  4. Jones, S., Johnstone, D., & Wilson, R. (2015). An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes. Journal of Banking & Finance, 56, 72–85.

    Article  Google Scholar 

  5. Lin, W.-Y., Hu, Y.-H., & Tsai, C.-F. (2011). Machine Learning in Financial Crisis Prediction: A Survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(4), 421–436.

    Google Scholar 

  6. Wang, G., Ma, J., & Yang, S. (2014). An improved boosting based on feature selection for corporate bankruptcy prediction. Expert Systems with Applications, 41(5), 2353–2361.

    Article  Google Scholar 

  7. Diederich, J. (2008). Rule extraction from support vector machines. Berlin Heidelberg: Springer.

    Book  Google Scholar 

  8. Engelbrecht, A. P. (2007). Computational intelligence: An introduction (2 ed.). Chichester, UK: John Wiley & Sons Ltd.

    Book  Google Scholar 

  9. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). New York: Springer.

    Book  Google Scholar 

  10. Danenas, P., & Garsva, G. (2014). Intelligent techniques and systems in credit risk analysis and forecasting : A review of patents. Recent Patents on Computer Science, 7, 12–23.

    Article  Google Scholar 

  11. Marques, A. I., Garcia, V., & Sanchez, J. S. (2013). A literature review on the application of evolutionary computing to credit scoring. Journal of the Operational Research Society, 64(9), 1384–1399.

    Article  Google Scholar 

  12. Paliwal, M., & Kumar, U. A. (2009). Neural networks and statistical techniques: A review of applications. Expert Systems with Applications, 36(1), 2–17.

    Article  Google Scholar 

  13. Verikas, A., Kalsyte, Z., Bacauskiene, M., & Gelzinis, A. (2010). Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey. Soft Computing, 14(9), 995–1010.

    Article  Google Scholar 

  14. Engelbrecht A.P., Computational Intelligence: An Introduction, 2nd ed., John Wiley & Sons, Ltd., 2007.

    Google Scholar 

  15. Vellido, A., Lisboa, P. J. G., & Vaughan, J. (1999). Neural networks in business: A survey of applications (1992–1998). Expert Systems with Applications, 17(1), 51–70.

    Article  Google Scholar 

  16. Yu, Q., Miche, Y., Séverin, E., & Lendasse, A. (2014). Bankruptcy prediction using Extreme Learning Machine and financial expertise. Neurocomputing, 128, 296–302.

    Article  Google Scholar 

  17. Jeong, C., Min, J. H., & Kim, M. S. (2012). A tuning method for the architecture of neural network models incorporating GAM and GA as applied to bankruptcy prediction. Expert Systems with Applications, 39(3), 3650–3658.

    Article  Google Scholar 

  18. Khashman, A. (2011). Credit risk evaluation using neural networks: Emotional versus conventional models. Applied Soft Computing, 11(8), 5477–5484.

    Article  Google Scholar 

  19. Tseng, F.-M., & Hu, Y.-C. (2010). Comparing four bankruptcy prediction models: Logit, quadratic interval logit, neural and fuzzy neural networks. Expert Systems with Applications, 37(3), 1846–1853.

    Article  Google Scholar 

  20. Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine learning, 20, 273–297.

    Google Scholar 

  21. Vapnik, V. N. (1998). Statistical learning theory (p. 1998). New York: John Wiley & Sons Inc..

    Google Scholar 

  22. Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3), 27.

    Article  Google Scholar 

  23. Huang, T.-M., Kecman, V., & Kopriva, I. (2006). Kernel based algorithms for mining huge data sets: supervised, semi-supervised and unsupervised learning. Berlin, Heidelberg: Springer.

    Google Scholar 

  24. Schölkopf, B. (2001). The kernel trick for distances. Advances in Neural Information Processing Systems, 13, 301–307.

    Google Scholar 

  25. Suykens, J., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural Processing Letters, 9(3), 293–300.

    Article  Google Scholar 

  26. Joachims, T. (1999). Making large-scale SVM learning practical. In B. Schölkopf, C. Burges, & A. Smola (Eds.), Advances in Kernel Methods—Support vector learning. Cambridge, MA: MIT-Press.

    Google Scholar 

  27. Fan, R., Chang, K., Hsieh, C., Wang, X., & Lin, C. (2008). LIBLINEAR: A library for large linear classification. The Journal of Machine Learning Research, 9, 1871–1874.

    Google Scholar 

  28. Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1, 81–106.

    Google Scholar 

  29. Wang, G., Ma, J., Huang, L., & Xu, K. (2012). Two credit scoring models based on dual strategy ensemble trees. Knowledge-Based Systems, 26, 61–68.

    Article  Google Scholar 

  30. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5–32.

    Article  Google Scholar 

  31. Kruppa, J., Schwarz, A., Arminger, G., & Ziegler, A. (2013). Consumer credit risk: Individual probability estimates using machine learning. Expert Systems with Applications, 40(13), 5125–5131.

    Article  Google Scholar 

  32. Malekipirbazari, M., & Aksakalli, V. (2015). Risk assessment in social lending via random forests. Expert Systems with Applications, 42(10), 4621–4631.

    Article  Google Scholar 

  33. Wang Q., Lai K. K., and Niu D. (2011). Green credit scoring system and its risk assessment model with support vector machine. 2011 Fourth International Joint Conference on Computational Sciences and Optimization, pp. 284–287.

    Google Scholar 

  34. Bellovary, J., Giacomino, D., & Akers, M. (2007). A review of Bankruptcy prediction studies : 1930 to present. Journal of Financial Education, 33, 1–43.

    Google Scholar 

  35. Bae, J. K. (2012). Predicting financial distress of the South Korean manufacturing industries. Expert Systems with Applications, 39(10), 9159–9165.

    Article  Google Scholar 

  36. Erdogan, B. E. (2013). Prediction of bankruptcy using support vector machines: an application to bank bankruptcy. Journal of Statistical Computation and Simulation, 83(8), 1543–1555.

    Article  Google Scholar 

  37. Hammer, P. L., Kogan, A., & Lejeune, M. A. (2012). A logical analysis of banks’ financial strength ratings. Expert Systems with Applications, 39(9), 7808–7821.

    Article  Google Scholar 

  38. Horta, I. M., & Camanho, A. S. (2013). Company failure prediction in the construction industry. Expert Systems with Applications, 40(16), 6253–6257.

    Article  Google Scholar 

  39. van Gestel, T., Martens, D., Baesens, B., Feremans, D., Huysmans, J., & Vanthienen, J. (2007). Forecasting and analyzing insurance companies’ ratings. International Journal of Forecasting, 23(3), 513–529.

    Article  Google Scholar 

  40. Chandra, D. K., Ravi, V., & Bose, I. (2009). Failure prediction of dotcom companies using hybrid intelligent techniques. Expert Systems with Applications, 36(3), 4830–4837.

    Article  Google Scholar 

  41. Kim, S. Y., & Upneja, A. (2014). Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models. Economic Modelling, 36, 354–362.

    Article  Google Scholar 

  42. Chen, M.-Y. (2011). Using a hybrid evolution approach to forecast financial failures for Taiwan-listed companies. Quantitative Finance, 14(6), 1–12.

    Google Scholar 

  43. van Gestel, T., Baesens, B., Suykens, J. A. K., van den Poel, D., Baestaens, D. E., & Willekens, M. (2006). Bayesian kernel based classification for financial distress detection. European Journal of Operational Research, 172(3), 979–1003.

    Article  Google Scholar 

  44. Geng, R., Bose, I., & Chen, X. (2015). Prediction of financial distress: An empirical study of listed Chinese companies using data mining. European Journal of Operational Research, 241(1), 236–247.

    Article  Google Scholar 

  45. Oreski, S., & Oreski, G. (2014). Genetic algorithm-based heuristic for feature selection in credit risk assessment. Expert Systems with Applications, 41(4), 2052–2064.

    Article  Google Scholar 

  46. Boguslauskas, V., & Mileris, R. (2009). Estimation of credit risk by artificial neural networks models. Engineering Economics, 4, 7–14.

    Google Scholar 

  47. Purvinis, O., Virbickaite, R., & Šukys, P. (2008). Interpretable nonlinear model for enterprise bankruptcy prediction. Nonlinear Analysis: Modelling and Control, 13(1), 61–70.

    Google Scholar 

  48. Kennedy, K., Mac, N. B., Delany, S. J., O’Sullivan, M., & Watson, N. (2013). A window of opportunity: Assessing behavioural scoring. Expert Systems with Applications, 40(4), 1372–1380.

    Article  Google Scholar 

  49. Chen, M.-Y. (2012). Visualization and dynamic evaluation model of corporate financial structure with self-organizing map and support vector regression. Applied Soft Computing, 12(8), 2274–2288.

    Article  Google Scholar 

  50. Kohonen, T. (1995). Self-organizing maps. Berlin, Heidelberg: Springer.

    Book  Google Scholar 

  51. Huysmans, J., Baesens, B., Vanthienen, J., & van Gestel, T. (2006). Failure prediction with self organizing maps. Expert Systems with Applications, 30(3), 479–487.

    Article  Google Scholar 

  52. Kiviluoto, K. (1998). Predicting bankruptcies with the self-organizing map. Neurocomputing, 21, 191–201.

    Article  Google Scholar 

  53. Merkevicius, E., Garsva, G., & Girdzijauskas, S. (2006). A hybrid SOM-Altman model for bankruptcy prediction. Lecture Notes in Computer Science, 3994, 364–371.

    Article  Google Scholar 

  54. Chen, N., Ribeiro, B., Vieira, A., & Chen, A. (2013). Clustering and visualization of bankruptcy trajectory using self-organizing map. Expert Systems with Applications, 40(1), 385–393.

    Article  Google Scholar 

  55. Sun, J., Li, H., Huang, Q.-H., & He, K.-Y. (2014). Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowledge-Based Systems, 57, 41–56.

    Article  Google Scholar 

  56. Tsai, C.-F. (2009). Feature selection in bankruptcy prediction. Knowledge-Based Systems, 22(2), 120–127.

    Article  Google Scholar 

  57. Lin, F., Liang, D., Yeh, C.-C., & Huang, J.-C. (2014). Novel feature selection methods to financial distress prediction. Expert Systems with Applications, 41(5), 2472–2483.

    Article  Google Scholar 

  58. Chen, F.-L., & Li, F.-C. (2010). Combination of feature selection approaches with SVM in credit scoring. Expert Systems with Applications, 37(7), 4902–4909.

    Article  Google Scholar 

  59. Liang, D., Tsai, C.-F., & Wu, H.-T. (2015). The effect of feature selection on financial distress prediction. Knowledge-Based Systems, 73, 289–297.

    Article  Google Scholar 

  60. Freund, Y. (1995). Boosting a weak learning algorithm by majority. Information and Computation, 121(2), 256–285.

    Article  Google Scholar 

  61. Ye, S., Xiao, Z., & Zhu, G. (2014). Identification of supply chain disruptions with economic performance of firms using multi-category support vector machines. International Journal of Production Research, 53(10), 3086–3103.

    Article  Google Scholar 

  62. Turban, E., Aronson, J. E., Liang, T.-P., & Sharda, R. (2006). Decision support and business intelligence systems (8th ed.). Upper Saddle River: Prentice Hall.

    Google Scholar 

  63. Liao, S. H. (2005). Expert system methodologies and applications-a decade review from 1995 to 2004. Expert Systems with Applications, 28(1), 93–103.

    Article  Google Scholar 

  64. Raynor, W. J. (1999). The international dictionary of artificial intelligence. London: The Glenlake Publishing Company, Ltd.

    Google Scholar 

  65. Beemer, B. A., & Gregg, D. G. (2008). Advisory systems to support decision making. In F. Burstein & C. W. Holsapple (Eds.), Handbook on Decision Support Systems 1: Basic Themes (pp. 511–527). Berlin Heidelberg: Springer.

    Chapter  Google Scholar 

  66. Turban, E., & Watkins, P. R. (1986). Integrating expert systems and decision support systems. MIS Quarterly, 10(2), 121–136.

    Article  Google Scholar 

  67. Alter, S. L. (1980). Decision support systems: Current practice and continuing challenge. Reading MA: Addison-Wesley.

    Google Scholar 

  68. Holsapple, C., & Whinston, A. (1996). Decision support systems: A knowledge-based approach. Eagan, MN: West Publishing Company.

    Google Scholar 

  69. OMG. (2009). Production Rule Representation (PRR) v.1.0 (December 2009).

    Google Scholar 

  70. OMG. (2013). Semantics of Business Vocabulary and Business Rules (SBVR) v.1.2 (November 2013).

    Google Scholar 

  71. Holsapple, C. W. (2008). DSS architecture and types. In F. Burstein & C. W. Holsapple (Eds.), Handbook on decision support systems 1: Basic themes (pp. 163–189). Berlin, Heidelberg: Springer.

    Chapter  Google Scholar 

  72. OMG. (2015). Decision Model and Notation (DMN) v.1.0 (March 2015).

    Google Scholar 

  73. Kennedy, J., Eberhart, R. C., & Shi, Y. (2001). Swarm intelligence. San Francisco, CA: Morgan Kaufmann Publishers.

    Google Scholar 

  74. Arnott, D., & Pervan, G. (2005). A critical analysis of decision support systems research. Journal of Information Technology, 20(2), 67–87.

    Article  Google Scholar 

  75. Power, D. J. (2008). Decision support systems: A historical overview. In F. Burstein & C. W. Holsapple (Eds.), Handbook on decision support systems 1: Basic themes. Berlin Heidelberg: Springer.

    Google Scholar 

  76. Cheng, H., Lu, Y.-C., & Sheu, C. (2009). An ontology-based business intelligence application in a financial knowledge management system. Expert Systems with Applications, 36(2), 3614–3622.

    Article  Google Scholar 

  77. Guo-an Y., Hong-bing X., and Chao W. (2003) Design and implementation of an agent-oriented expert system of loan risk evaluation. Proc. Of International Conference on Integration of Knowledge Intensive Multi-Agent Systems (pp. 41–45).

    Google Scholar 

  78. Huai, W. (2010) The framework design and research on enterprises group financial decision support system. In: Proceedings of Management and Service Science (MASS), Wuhan, China, pp.1–4.

    Google Scholar 

  79. Kuo, H.-C. (1997). Cognitive management system based support for bank credit granting decision: An integrated and practical design for Taiwan. Journal of Systems Integration, 7, 77–91.

    Article  Google Scholar 

  80. Nedović, L., & Devedžić, V. (2002). Expert systems in finance—A cross-section of the field. Expert Systems With Applications, 23(1), 49–66.

    Article  Google Scholar 

  81. Matsasinis, F. N. (2002). CCAS: An intelligent decision support system for credit card assessment. Journal Of Multi-Criteria Decision Analysis, 11, 213–235.

    Article  Google Scholar 

  82. Zhang, M., Gu, Y., and Zhu, J. (2009). Analysis of the framework for financial decision support system. Proceedings of 2009 International Conference on Wireless Networks and Information Systems, Shanghai, pp. 241–244.

    Google Scholar 

  83. Mahmoud M., Algadi N., and Ali A. (2008) Expert System for Banking Credit Decision. 2008 International Conference on Computer Science and Information Technology, 813–819.

    Google Scholar 

  84. Tsaih, R., Liu, Y.-J., Liu, W., & Lien, Y.-L. (2004). Credit scoring system for small business loans. Decision Support Systems, 38(1), 91–99.

    Article  Google Scholar 

  85. Kotsiantis, S. B., Kanellopoulos, D., Karioti, V., and Tampakas, V. (2009). An ontology-based portal for credit risk analysis. 2nd IEEE International Conference on Computer Science and Information Technology (pp. 165–169).

    Google Scholar 

  86. Open Knowledge. (2015). Open knowledge: What is open? https://okfn.org/opendata/, date accessed 2015.08.20.

  87. Bank for International Settlements (BIS) (2015). International regulatory framework for banks (Basel III), http://www.bis.org/bcbs/basel3.htm, date accessed 2015.08.20.

  88. Balthazar, L. (2006). From Basel 1 to Basel 3: The integration of state-of-the-art risk modeling in banking regulation. UK: Palgrave Macmillan.

    Book  Google Scholar 

  89. Patni A. (2015) Basel III is here—What are the implications for your business? Infosys whitepaper, http://www.infosys.com/industries/financial-services/white-papers/Documents/basel-here.pdf, date accessed 2015.08.20

  90. Garsva, G., & Danenas, P. (2011). XBRL integration into intelligent system for credit risk evaluation. Transformations in Business & Economics, 10, No. 2(23), 88–103.

    Google Scholar 

  91. XBRL Inc. (2010). XBRL International Inc. Extensible Business Reporting Language (XBRL) 2.1. http://xbrl.org/Specification/XBRL-RECOMMENDATION-2003-12-31+Corrected-Errata-2008-07-02.htm, date accessed 2015.07.04

  92. XBRL Inc. (2006). XBRL International Inc. XBRL Dimensions 1.0. http://www.xbrl.org/Specification/XDTREC-2006-09-18.htm, date accessed 2015.07.04.

  93. XBRL Inc. (2009). XBRL International Inc. Formula Specifications. http://www.xbrl.org/Specification/formula/REC-2009-06-22/index.htm, date accessed 2015.07.04

  94. XBRL Inc. (2007) XBRL International Inc. XBRL Rendering Requirements, Public Working Draft. https://www.xbrl.org/technical/requirements/REN-REQ-PWD-2007-07-24.htm, date accessed 2015.07.04.

  95. XBRL Inc. (2007). XBRL International Inc. XBRL Versioning Specification 1.0, Public Working Draft. http://www.xbrl.org/Specification/Versioning/XVS-PWD-2007-11-28.htm, referred on 24/11/2010, date accessed 2015.07.04

  96. Danenas P. and Garsva G. (2012) Domain driven development and feature driven development for development of decision support systems. Proceedings of Information and Software Technologies—18th International Conference ICIST 2012. Communications in Computer and Information Science (Vol. 319, pp. 187–198).

    Google Scholar 

  97. Danenas P. and Garsva G. (2011) SVM and XBRL based decision support system for credit risk evaluation. Proceedings of the 17th International Conference on Information and Software Technologies (IT 2011), Kaunas, Lithuania, 190–198.

    Google Scholar 

  98. Cao, J., Lu, H., Wang, W., & Wang, J. (2012). A novel five-category loan-risk evaluation model using multiclass LS-SVM By PSO. International Journal of Information Technology & Decision Making, 11(4), 857–874.

    Article  Google Scholar 

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Authors and Affiliations

  1. Center of Information Systems Design Technologies, Kaunas University of Technology, K. Baršausko Str. 59, Kaunas, Lithuania

    Paulius Danenas & Gintautas Garsva

  2. Department of Informatics, Kaunas Faculty, Vilnius University, Muitines Str. 8, Kaunas, Lithuania

    Paulius Danenas & Gintautas Garsva

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  1. Paulius Danenas
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Editors and Affiliations

  1. ACANTO Holding, Hannover, Germany

    Christian L. Dunis

  2. University of Liverpool, Liverpool, United Kingdom

    Peter W. Middleton

  3. American University of Beirut (AUB), Beirut, Lebanon

    Andreas Karathanasopolous

  4. University of Patras, Patras, Greece

    Konstantinos Theofilatos

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Danenas, P., Garsva, G. (2016). Intelligent Credit Risk Decision Support: Architecture and Implementations. In: Dunis, C., Middleton, P., Karathanasopolous, A., Theofilatos, K. (eds) Artificial Intelligence in Financial Markets. New Developments in Quantitative Trading and Investment. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-137-48880-0_7

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  • DOI: https://doi.org/10.1057/978-1-137-48880-0_7

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