Skip to main content
SpringerLink
Log in

A trust-based bio-inspired approach for credit lending decisions

  • Published:
Computing Aims and scope Submit manuscript

Abstract

Credit scoring computation essentially involves taking into account various financial factors and the previous behavior of the credit requesting person. There is a strong degree of correlation between the compliance level and the credit score of a given entity. The concept of trust has been widely used and applied in the existing literature to determine the compliance level of an entity. However it has not been studied in the context of credit scoring literature. In order to address this shortcoming, in this paper we propose a six-step bio-inspired methodology for trust-based credit lending decisions by credit institutions. The proposed methodology makes use of an artificial neural network-based model to classify the (potential) customers into various categories. To show the applicability and superiority of the proposed algorithm, it is applied to a credit-card dataset obtained from the UCI repository. Due to the varying spectrum of trust levels, we are able to solve the problem of binary credit lending decisions. A trust-based credit scoring approach allows the financial institutions to grant credit-based on the level of trust in potential customers.

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

Similar content being viewed by others

References

  1. Blochlinger A, Leippold M (2006) Economic benefit of powerful credit scoring. J Banking Finance 30(3): 851–873

    Article  Google Scholar 

  2. Jacobson T, Roszbach K (2003) Bank lending policy, credit scoring and value-at-risk. J Banking Finance 27: 615–633

    Article  Google Scholar 

  3. Yu L, Wang S, Lai KK, Zhou L (2008) Bio-inspired credit risk analysis: computational intelligence with support vector machines. Springer, Berlin

    Google Scholar 

  4. Kim YS, Sohn SY (2004) Managing loan customers using misclassification patterns of credit scoring model. Expert Syst Appl 26: 567–568

    Article  Google Scholar 

  5. Chuang C, Lin R (2009) Constructing a reassigning credit scoring model. Expert Syst Appl 36: 1685–1687

    Article  Google Scholar 

  6. Sarlija N, Bensic M, Bohacek Z (2004) Multinomial model in consumer credit scoring. 10th international conference on operational research. Trogir, Croatia

  7. Abdou H, Pointon J, El-Marsy A (2008) Neural nets versus conventional techniques in credit scoring in Egyptian banking. Expert Syst Appl 35: 1277–1279

    Article  Google Scholar 

  8. Islam S, Zhou L, Li F (2009) Application of artificial intelligence (artificial neural network) to assess credit risk: a predictive model for credit card scoring. MSc, School of Management Blekinge Institute of Technology

  9. Jentzsch N (2007) Financial privacy: an international comparison of credit reporting systems (contributions to economics). Springer, Berlin

    Google Scholar 

  10. Chen M, Huang S (2003) Credit scoring and rejected instances reassigning through evolutionary computation technique. Expert Syst Appl 24: 433–434

    Article  MATH  Google Scholar 

  11. Ferretti F (2009) The credit scoring pandemic and the European vaccine: making sense of EU data protection legislation. J Inf Law Technol 1. http://go.warwick.ac.uk/jilt/2009_1/ferretti

  12. Bocij P, Chaffey D, Greasley A, Hickie S (2009) Business information systems: technology. Development and management for the E-business. FT Press, Upper Saddle River

    Google Scholar 

  13. Tsai C, Wu J (2008) Using neural network ensembles for bankruptcy prediction and credit scoring. Expert Syst Appl 34: 2639–2640

    Article  Google Scholar 

  14. Lee T, Chen I (2005) A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines. Expert Syst Appl 28(4): 743–752

    Article  Google Scholar 

  15. Bellotti T, Crook J (2009) Support vector machines for credit scoring and discovery of significant features. Expert Syst Appl 36: 3302

    Article  Google Scholar 

  16. Lee TS, Chiu CC, Lu CJ, Chen I (2002) Credit scoring using the hybrid neural discriminant technique. Expert Syst Appl 23(3): 245–254

    Article  Google Scholar 

  17. Zekic-Susac M, Sarlija N, Bensic M (2004) Small business credit scoring: a comparison of logistic regression, neural networks, and decision tree models. 26th international conference on information technology interfaces, Croatia

  18. Yang Y (2007) Adaptive credit scoring with kernel learning methods. Eur J Oper Res 183(3): 1521–1536

    Article  MATH  Google Scholar 

  19. Leondes CT (2005) Intelligent knowledge-based systems: business and technology in the new millennium. Kluwer, Dordrecht

    Book  Google Scholar 

  20. Timofeev R (2004) Classification and regression trees theory and applications. Master, Humboldt University, Center of Applied Statistics and Economics

  21. Thomas LC (2000) A survey of credit scoring and behavioural scoring: forcasting financial risk of lending to customers. Int J Forecast 16: 151–152

    Article  Google Scholar 

  22. Huang CL, Chen MC, Wang CJ (2007) Credit scoring with a data mining approach based on support vector machine. Expert Syst Appl 33(4): 847–856

    Article  MathSciNet  Google Scholar 

  23. Huang J, Tzeng GH, Ong CH (2006) Two stage genetic programming (2SGP) for credit scoring model. Appl Math Comput 174(2): 1039–1053

    Article  MathSciNet  MATH  Google Scholar 

  24. Pérez JM, Albisua I, Arbelaitz O, Gurrutxaga I, Martín J, Muguerza J, Perona I (2010) Consolidated trees versus bagging when explanation is required. Computing 89: 113–145

    Article  MathSciNet  MATH  Google Scholar 

  25. Sathasivam S, Wan Abdullah WA (2011) Logic mining in neural network: reverse analysis method. Computing 91: 119–133

    Article  MathSciNet  MATH  Google Scholar 

  26. Zhang GP (2003) Neural networks in business forecasting. Information Science Publishing, New York

    Book  Google Scholar 

  27. Burger M, Hofinger A (2005) Regularized greedy algorithms for network training with data noise. Computing 74: 1–22

    Article  MathSciNet  MATH  Google Scholar 

  28. Liang Q (2003) Corporate financial distress diagnosis in China: empirical analysis using credit scoring models. Hitotsubashi J Commerce Manag 38(1): 13–28

    Google Scholar 

  29. Jabeen H, Beig AR (2010) Review of classification using genetic programming. Int J Eng Sci Technol 2(2): 94–97

    Google Scholar 

  30. Auria L, Moro RA (2008) Support vector machines as a technique for solvency analysis. German Institute for Economic Research. http://www.diw.de/english/products/publications/discussion_papers/27539.html

  31. Hsieh NC (2005) Hybrid mining approach in the design of credit scoring model. Expert Syst Appl 28: 656–657

    Article  Google Scholar 

  32. Martens D, Baesens B, Gestel TV, Vanthienen J (2007) Comprehensible credit scoring models using rule extraction from support vector machines. Eur J Oper Res 183: 1471–1472

    Article  Google Scholar 

  33. Xu X, Zhou C, Wang Z (2009) Credit scoring algorithm based on link analysis ranking with support vector machine. Expert Syst Appl 36: 2627–2628

    Google Scholar 

  34. Bharadwaj K, Al-Shamri MH (2009) Fuzzy computational models for trust and reputation systems. Electr Commerce Res Appl 8: 38–39

    Google Scholar 

  35. Hussain FK, Chang E, Dillon T (2006) Trust and reputation for service-oriented environments, vol 1: Technologies for building business intelligence and consumer confidence. Wiley, West Sussex

  36. Frank A, Asuncion A (2010) UCI machine learning repository. University of California, School of Information and Computer Science, Irvine. http://archive.ics.uci.edu/ml

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, University of Tafresh, Tafresh, Iran

    Monireh Sadat Mirtalaei, Morteza Saberi & Behzad Ashjari

  2. School of Information Systems, Curtin Business School, Curtin University Perth, Perth, WA, Australia

    Morteza Saberi & Omar Khadeer Hussain

  3. School of Software, Decision Support and e-Service Intelligence Lab, Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, Ultimo, NSW, 2007, Australia

    Farookh Khadeer Hussain

Authors
  1. Monireh Sadat Mirtalaei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Morteza Saberi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Omar Khadeer Hussain
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Behzad Ashjari
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Farookh Khadeer Hussain
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Farookh Khadeer Hussain.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mirtalaei, M.S., Saberi, M., Hussain, O.K. et al. A trust-based bio-inspired approach for credit lending decisions. Computing 94, 541–577 (2012). https://doi.org/10.1007/s00607-012-0190-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-012-0190-3

Keywords

Mathematics Subject Classification (2000)

Search

Navigation