Abstract
The main goal of all commercial banks is to collect the savings of legal and real persons and allocate them as credit to industrial, services and production companies. Non repayment of such credits cause many problems to the banks such as incapability to repay the central bank’s loans, increasing the amount of credit allocations comparing to credit repayment and incapability to allocate more credits to customers. The importance of credit allocation in banking industry and it’s important role in economic growth and employment creation leads the development of many models to evaluate the credit risk of applicants. But many of these models are classic and are incapable to do credit evaluation completely and efficiently. Therefore the demand to use artificial intelligence in this field has grown up. In this paper after providing appropriate credit ranking model and collecting expert’s knowledge, we design a hybrid intelligent system for credit ranking using reasoning-transformational models. Expert system as symbolic module and artificial neural network as non-symbolic module are components of this hybrid system. Such models provide the unique features of each components, the reasoning and explanation of expert system and the generalization and adaptability of artificial neural networks. The results of this system demonstrate hybrid intelligence system is more accurate and powerful in credit ranking comparing to expert systems and traditional banking models.
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References
Emel AB, Oral M, Reisman A, Yolalan R (2003) A credit scoring approaches for the commercial banking sector. Socio-Econ Plan Sci 37:103–123
West D (2000) Neural network credit scoring models. Comput Oper Res 27:1131–1152
Min JH, Lee Y-C (2007) A practical approach to credit scoring. Expert Syst Appl 35(4):1762–1770
Kohonen T (1984) Self-organization and associative memory. Springer, Berlin
Rumelhart D, Hinton G, Williams R (1986) Learning internal representations by error propagation. Parallel distributed processing: explorations in the microstructure of cognition, vol. I & II. MIT Press, Cambridge
Haykin S (1988) Neural networks—a comprehensive foundation. Mcmillan College Publishing
Sutton RS, Barto AG (1988) Reinforcement learning. MIT Press, Cambridge
Widrow B, Lehr MA (1990) 30 years of adaptive neural networks: perceptron, madaline and backpropagation. Proc IEEE 78(9):1415–1442
Reyneri LM (1995) Weighted radial basis functions for improved pattern recognition and signal processing. Neural Process Lett 2(3):2–6
Haykin S (1999) Neural networks—a comprehensive foundation. Prentice Hall, Englewood Cliffs
Anderson CW, Devulapalli SV, Stolz EA (1995) Determining mental state from EEG signals using parallel implementations of neural networks. Sci Program Special Issue Appl Anal 4(3):171–183
Sachenko A, Kochan V, Turchenko V, Golovko V, Savitsky J, Dunets A, Laopoulos T (2000) Sensor errors prediction using neural networks. In: IJCNN’2000, Jul 24–27 2000. Como, Italy, pp 441–446
Tremiolles, G. De (1998) Contribution to the theoretical study of neuro-mimetic models and to their experimental validation: a panel of industrial applications. PhD Report. University of Paris 12 (in French)
Touzet CF (1997) Neural reinforcement learning for behavior synthesis. Robot Auton Syst 22:251–281
Sang KK, Niyogi P (1995) Active learning for function approximation. In: Tesauro G (ed) Neural information processing systems 7. MIT Press, Cambridge, pp 497–504
Dietterich TG (2000) Hierarchical reinforcement learning with the MAXQ value function decomposition. J Artif Intell Res 13:227–303
Faller W, Schreck S (1995) Real-time prediction of unsteady aerodynamics: Application for aircraft control and maneuverability enhancement. IEEE Trans Neural Netw 6:6
Sridhar DV, Bartlett EB (1999) An information theoretic approach for combining neural network process models. Neural Netw 12:915–926
Goonatilake S, Khebbal S (1996) In: Intelligent hybrid systems: issues, classification and future directions, intelligent hybrid systems. Wiley, New York, pp 1–20
Vojtek M, Kočenda E (2006) Credit-scoring methods. Czech J Econ Finance 56(3-4):152–167
Hand DJ, Henley WE (1997) Statistical classification methods in consumer credit scoring: a review. J R Stat Soc Ser A 160:523–541
Thomas LC (2000) A survey of credit and behavioral scoring; forecasting financial risk of lending to consumers. Int J Forecast 16(2):149–172
Jiao Y (2003) Fuzzy adaptive networks and applications to humanistic systems. PhD Report. Department of Industrial and Manufacturing Systems Engineering. College of Engineering, Kansas University
Trinkle BS (2006) Interpretable credit model development via artificial neural network. PhD Report. University of Alabama
Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 43:3–31
Yi W (2005) Artificial Neural Network, 339229. Available at: http://www2.in.tuclausthal.de/~hammer/lectures/seminar_ml/neural-network.pdf. Accessed 27 September 2007
Lam M (2004) Neural network techniques for financial performance prediction: integrating fundamental and technical analysis. Decis Support Syst 37:567–581
Hsieh C-T (1993) Some potential applications of artificial neural systems in financial management. J Syst Manag 44(4):12–16
Jenson H (1992) Using neural network for credit scoring. Manag Finance 18(6):15–26
Goonatilake S, Treleavan P (1995) Intelligent systems for finance and business. Wiley, New York
Desai V, Crook J, Overstreet G (1997) Credit scoring models in the credit union environment using neural networks and genetic algorithms. IMA J Math Appl Bus Ind 8(4):232–256
Bennell JA, Crabbe D, Thomas S, Gwilym OA (2006) Modeling sovereign credit ratings: neural networks versus ordered profit. Expert Syst Appl 30(3):415–425
Tsai C-F, Wu J-W (2008) Using neural network ensembles for bankruptcy prediction and credit scoring. Expert Syst Appl 34(4):2639–2649
Harmon P, King D (1985) Artificial intelligence in business—expert systems. Wiley, New York
Ellis C, Wilson P (2005) Expert system portfolios of Australian and UK securitized property investments. Pac Rim Prop Res J 1(1):107–127
Metaxiotis KS, Psarras JE (2003) Expert systems in business: applications and future directions for the operations researcher. Ind Manag Data Syst (IMDS) 103(5):361–368
Wilson PJ (1987) Expert systems in business, vols 1, 2. MTE, Sydney
Wong BK, Monaco JA (1995) A bibliography of expert system applications in business (1984–1992). Eur J Oper Res 85:416–432
Wong BK, Monaco JA (1995) Expert system applications in business: a review and analysis of the literature (1977–1993). Inf Manag 29:141–152
Liao S-H (2005) Expert system methodologies and applications—a decade review from 1995 to 2004. Expert Syst Appl 28:93–103
Holsapple C, Lee A, Otto J (1989) Refining the behavior of multiple expert systems: a concept and empirical results. Int J Intell Syst Account Finance Manag 7(2):81–90
Piketty L (1987) The authorizer’s assistant: a large commercial expert system application. In: Proceedings of the AI and advanced computer technology conference. Long Beach, CA
Zocco D (1985) A framework for expert systems in bank loan management. J Commer Bank Lend 67(2):47–55
Iwasieczko B, Korczak J, Kwiecien M, Muszynska J (1986) Expert system in financial analysis. In: Pau LF (ed) Artificial intelligence in economics and management. North-Holland, Amsterdam/New York/Oxford/Tokyo, pp 113–120
Bryant B (2001) ALEES: an agricultural loan evaluation expert system. Expert Syst Appl 21:75–85
Klein M (2002) Finsim expert; A KB/DSS for financial analysis and planning. Eng Costs Prod Econ 17(1–4):359–367
Walker E, Hodgkinson L (2003) An expert system for credit evaluation and explanation. In: CCSC, Midwestern Conference
Griffiths B, Beynon MJ (2005) Expositing stages of VPRS analysis in an expert system: application with bank credit ratings. Expert Syst Appl 29:879–888
Taha IE (1997) A hybrid intelligent architecture for revising domain knowledge. PhD Report. University of Texas at Austin
Lertpalangsunti N (1997) An implemented framework for the construction of hybrid intelligent forecasting systems. PhD Report. University of Regina
Goonatilake S, Khebbal S (1995) Intelligent hybrid systems: issue, classifications and future directions in intelligent hybrid systems. Wiley, New York
Medsker L (1994) Hybrid neural network and expert system. Kluwer Academic, Amsterdam
Cheng Y, Fortier P, Normandin Y (1994) A system integrating connectionist and symbolic approaches for spoken language understanding. In: Proceedings of the international conference on spoken language processing. Yokohama, pp 1511–1514
Sun R, Peterson T (1998) Autonomous learning of sequential tasks: experiments and analyses. IEEE Trans Neural Netw 9(6):1217–1234
Gelfand J, Handleman D, Lane S (1989) Integrating knowledge-based systems and neural networks for robotic skill. In: Proceedings of the international joint conference on artificial intelligence, pp 193–198
Kwasny SC, Faisal KA (1992) Connectionism and determinism in a syntactic Parser. In: Connectionist natural language processing, pp 119–162
Wermter S, Weber V (1997) SCREEN: Learning a at syntactic and semantic spoken language analysis using artificial neural networks. J Artif Intell Res 6(1):35–85
Rast M (1997) Application of fuzzy neural network on financial problems. In: Proceedings of north America fuzzy information processing society, Annual Meeting, Syracuse. NY. September 21–24, pp 347–349
Piramuthu S (1999) Financial credit risk evaluation with neural and neuro-fuzzy systems. Eur J Oper Res 112:310–321
Malhorta R, Malhorta DK (2002) Differentiating between good credits and bad credits using neuro-fuzzy systems. Eur J Oper Res 136(1):190–211
Hoffmann F, Baesens B, Martens J, Put F, Vanthienen J (2002) Comparing a genetic fuzzy and a neurofuzzy classifier for credit scoring. Int J Int Syst 17(11):1067–1083
Lee TS, Chiu CC, Lu CJ, Chen IF (2002) Credit scoring using the hybrid neural discriminant technique. Expert Syst Appl 23(3):245–254
Lee TS, Chen IF (2005) A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines. Expert Syst Appl 28(4):743–752
Hsieh NC (2005) Hybrid mining approach in the design of credit scoring models. Expert Syst Appl 28(4):655–665
Laha A (2007) Building contextual classifiers by integrating fuzzy rule based classification technique and k–nn method for credit scoring. Adv Eng Inf 21:281–291
Jiao Y, Syaub Y-R, Lee ES (2007) Modeling credit rating by fuzzy adaptive network. Math Comput Model 45:717–731
Roozbahani MK (2005) Designing, building and programming an expert system shell: FOOPES, Master Thesis Report. College of Engineering. Tarbiat Modares University
Ong C-S, Huang J-J, Tzeng G-H (2005) Building credit scoring models using genetic programming. Expert Syst Appl 29:41–47
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Bahrammirzaee, A., Ghatari, A.R., Ahmadi, P. et al. Hybrid credit ranking intelligent system using expert system and artificial neural networks. Appl Intell 34, 28–46 (2011). https://doi.org/10.1007/s10489-009-0177-8
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DOI: https://doi.org/10.1007/s10489-009-0177-8