Hybrid credit ranking intelligent system using expert system and artificial neural networks

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.

[1]  Thomas G. Dietterich.  Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition , 1999, J. Artif. Intell. Res..

[2]  Martin Vojtek,et al.  Credit-Scoring Methods (in English) , 2006 .

[3]  Kay Bryant,et al.  ALEES: an agricultural loan evaluation expert system , 2001, Expert Syst. Appl..

[4]  Ron Sun,et al.  Autonomous learning of sequential tasks: experiments and analyses , 1998, IEEE Trans. Neural Networks.

[5]  Bart Baesens,et al.  COMPARING A GENETIC FUZZY AND A NEUROFUZZY CLASSIFIER FOR CREDIT SCORING , 2002 .

[6]  L. Thomas.  A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers , 2000 .

[7]  Charles W. Anderson,et al.  Determining Mental State from EEG Signals Using Parallel Implementations of Neural Networks , 1995, Sci. Program..

[8]  Bernard Widrow,et al.  30 years of adaptive neural networks: perceptron, Madaline, and backpropagation , 1990, Proc. IEEE.

[9]  Arijit Laha.  Building contextual classifiers by integrating fuzzy rule based classification technique and k-nn method for credit scoring , 2007, Adv. Eng. Informatics.

[10]  David West,et al.  Neural network credit scoring models , 2000, Comput. Oper. Res..

[11]  Jonathan Crook,et al.  Credit Scoring Models in the Credit Union Environment Using Neural Networks and Genetic Algorithms , 1997 .

[12]  Richard S. Sutton,et al.  Reinforcement Learning , 1992, Handbook of Machine Learning.

[13]  Kostas S. Metaxiotis,et al.  Expert systems in business: applications and future directions for the operations researcher , 2003, Ind. Manag. Data Syst..

[14]  Tian-Shyug Lee,et al.  A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines , 2005, Expert Syst. Appl..

[15]  Shu-Hsien Liao,et al.  Expert system methodologies and applications - a decade review from 1995 to 2004 , 2005, Expert Syst. Appl..

[16]  Nan-Chen Hsieh,et al.  Hybrid mining approach in the design of credit scoring models , 2005, Expert Syst. Appl..

[17]  Chih-Fong Tsai,et al.  Using neural network ensembles for bankruptcy prediction and credit scoring , 2008, Expert Syst. Appl..

[18]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[19]  Stephen H. Lane,et al.  Integrating Knowledge-Based System and Neural Network Techniques for Robotic Skill Acquisition , 1989, IJCAI.

[20]  Sukhdev Khebbal,et al.  Intelligent Hybrid Systems , 1994 .

[21]  Leonardo Maria Reyneri.  Weighted Radial Basis Functions for improved pattern recognition and signal processing , 2006, Neural Processing Letters.

[22]  J. Korczak,et al.  Expert system in financial analysis , 1986 .

[23]  Paul Harmon,et al.  Expert systems: artificial intelligence in business , 1985 .

[24]  Ying Cheng,et al.  System integrating connectionist and ibolic approaches for spoken language understanding , 1994, ICSLP.

[25]  Monica Lam,et al.  Neural network techniques for financial performance prediction: integrating fundamental and technical analysis , 2004, Decis. Support Syst..

[26]  E. Stanley Lee,et al.  Modelling credit rating by fuzzy adaptive network , 2007, Math. Comput. Model..

[27]  Chang-tseh Hsieh,et al.  Some Potential Applications of Artificial Neural Systems in Financial Management , 1993 .

[28]  Michel R. Klein,et al.  Finsim expert; A KB/DSS for financial analysis and planning , 1989 .

[29]  Larry R. Medsker,et al.  Hybrid Neural Network and Expert Systems , 1994, Springer US.

[30]  Ahmet Burak Emel,et al.  A credit scoring approach for the commercial banking sector , 2003 .

[31]  Chih-Chou Chiu,et al.  Credit scoring using the hybrid neural discriminant technique , 2002, Expert Syst. Appl..

[32]  Louis F. Pau,et al.  Artificial Intelligence in Economics and Management , 1986 .

[33]  D. Michie,et al.  Expert systems in business , 1983 .

[34]  Luke Hodgkinson,et al.  An expert system for credit evaluation and explanation , 2003 .

[35]  C ONG,et al.  Building credit scoring models using genetic programming , 2005, Expert Syst. Appl..

[36]  Bo K. Wong,et al.  Expert system applications in business: A review and analysis of the literature (1977-1993) , 1995, Inf. Manag..

[37]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[38]  C. Lee Giles,et al.  Neural Information Processing Systems 7 , 1995 .

[39]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[40]  Rashmi Malhotra,et al.  Differentiating between Good Credits and Bad Credits Using Neuro-Fuzzy Systems , 2001, Eur. J. Oper. Res..

[41]  R. J. Lickley,et al.  Proceedings of the International Conference on Spoken Language Processing. , 1992 .

[42]  Volodymyr Turchenko,et al.  Sensor errors prediction using neural networks , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[43]  Kanaan A. Faisal,et al.  Connectionism and Determinism in a Syntactic Parser , 1990 .

[44]  Eric B. Bartlett,et al.  An information theoretic approach for combining neural network process models , 1999, Neural Networks.

[45]  Suran Asitha Goonatilake,et al.  Intelligent Systems for Finance and Business , 1995 .

[46]  Selwyn Piramuthu,et al.  Financial credit-risk evaluation with neural and neurofuzzy systems , 1999, Eur. J. Oper. Res..

[47]  Herbert L. Jensen,et al.  Using Neural Networks for Credit Scoring , 1992 .

[48]  E. Kočenda,et al.  Credit Scoring Methods , 2006 .

[49]  Young-Chan Lee,et al.  A practical approach to credit scoring , 2008, Expert Syst. Appl..

[50]  William E. Faller,et al.  Real-time prediction of unsteady aerodynamics: Application for aircraft control and manoeuvrability enhancement , 1995, IEEE Trans. Neural Networks.

[51]  David J. Hand,et al.  Statistical Classification Methods in Consumer Credit Scoring: a Review , 1997 .

[52]  Claude F. Touzet,et al.  Neural reinforcement learning for behaviour synthesis , 1997, Robotics Auton. Syst..

[53]  Ismail Taha.  A hybrid intelligent architecture and revising domain knowledge , 1998 .

[54]  C. W. Chan,et al.  An implemented architecture for construction of hybrid intelligent forecasting systems , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[55]  Application of fuzzy neural networks on financial problems , 1997, 1997 Annual Meeting of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.97TH8297).

[56]  Brad S. Trinkle,et al.  Interpretable credit model development via artificial neural networks , 2007 .

[57]  I A Basheer,et al.  Artificial neural networks: fundamentals, computing, design, and application. , 2000, Journal of microbiological methods.

[58]  Stephen Thomas,et al.  Modelling sovereign credit ratings: Neural networks versus ordered probit , 2006, Expert Syst. Appl..

[59]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[60]  Craig Ellis,et al.  Expert System Portfolios of Australian and UK Securitised Property Investments , 2006 .

[61]  Stefan Wermter,et al.  SCREEN: learning a flat syntactic and semantic spoken language analysis using artificial neural networks , 1997 .

[62]  Malcolm J. Beynon,et al.  Expositing stages of VPRS analysis in an expert system: Application with bank credit ratings , 2005, Expert Syst. Appl..

[63]  Clyde W. Holsapple,et al.  Refining the behavior of multiple expert systems: a concept and empirical results , 1998, Intell. Syst. Account. Finance Manag..

[64]  Partha Niyogi,et al.  Active Learning for Function Approximation , 1994, NIPS.

[65]  Bo K. Wong,et al.  A bibliography of expert system applications for business (1984–1992) , 1995 .