+7 925 966 4690, 9am6pm (GMT+3), Monday – Friday
ÈÄ «Ôèíàíñû è êðåäèò»

JOURNALS

  

FOR AUTHORS

  

SUBSCRIBE

    
Finance and Credit
 

Methods of fuzzy set theory in credit scoring

Vol. 23, Iss. 35, SEPTEMBER 2017

PDF  Article PDF Version

Received: 4 July 2017

Received in revised form: 9 August 2017

Accepted: 24 August 2017

Available online: 29 September 2017

Subject Heading: Banking

JEL Classification: C38, C55, D81

Pages: 2088–2106

https://doi.org/10.24891/fc.23.35.2088

Volkova E.S. Financial University under Government of Russian Federation, Moscow, Russian Federation
EVolkova@fa.ru

Gisin V.B. Financial University under Government of Russian Federation, Moscow, Russian Federation
VGisin@fa.ru

Solov'ev V.I. Financial University under Government of Russian Federation, Moscow, Russian Federation
VSoloviev@fa.ru

Importance This article provides an overview of the current state of research related to the application of fuzzy set theory and fuzzy logic in credit scoring.
Objectives The article aims to describe and classify fuzzy set theory and fuzzy logic methods used in modern credit scoring models.
Methods To perform the tasks, we have studied relevant scientific publications on the article subject presented in Google Scholar.
Results The article presents a description and analysis of the basic methods of fuzzy set theory used in credit scoring.
Conclusions and Relevance The application of fuzzy sets and fuzzy logic in the models of credit scoring allows for flexible models that allow for a natural and comprehensible interpretation. The most promising direction is the use of fuzzy inference systems.

Keywords: credit score, machine learning, fuzzy sets, fuzzy logic, fizzy inference

References:

  1. Chaudhuri A. Modified Fuzzy Support Vector Machine for Credit Approval Classification. AI Communications, 2014, vol. 27, iss. 2, pp. 189–211.
  2. Yi B., Zhu J. Credit Scoring with an Improved Fuzzy Support Vector Machine Based on Grey Incidence Analysis. Proc. Int. Conf. on Grey Systems and Intelligent Services (GSIS), 2015, IEEE, 2015, pp. 173–178.
  3. Shi J., Xu B. Credit Scoring by Fuzzy Support Vector Machines with a Novel Membership Function. Journal of Risk and Financial Management, 2016, vol. 9, iss. 4, pp. 1–10. doi: 10.3390/jrfm9040013
  4. Xinhui C., Zhong Q. On Consumer Credit Scoring Based on Multi-criteria Fuzzy Logic. Proc. Int. Conf. Business Intelligence and Financial Engineering, 2009, BIFE'09. IEEE, 2009, pp. 765–768.
  5. Lukashevich N.S. The Credit Scoring System for Evaluating Personal Loans Based on the Fuzzy Sets Theory. World Applied Sciences Journal, 2014, vol. 31, iss. 5, pp. 840–845.
  6. Gorlushkina N.N., Shin E.V. [Reengineering of business process of lending and the use of fuzzy sets for the classification of borrowers in the problem of credit scoring]. Internet-zhurnal Naukovedenie, 2015, vol. 7, no. 2, pp. 1–11. (In Russ.) URL: Link
  7. Mammadli S. Fuzzy Logic Based Loan Evaluation System. Procedia Computer Science, 2016, vol. 102, pp. 495–499. URL: Link
  8. Wu Y., Zhang B., Lu J., Du K.L. Fuzzy Logic and Neuro-fuzzy Systems: A Systematic Introduction. International Journal of Artificial Intelligence and Expert Systems (IJAE), 2011, vol. 2, iss. 2, pp. 47–80.
  9. Abdulrahman U.F.I., Panford J.K., Hayfron-Acquah J.B. Fuzzy Logic Approach to Credit Scoring for Micro Finance in Ghana: A Case Study of KWIQPLUS Money Lending. International Journal of Computer Applications, 2014, vol. 94, no. 8, pp. 11–18. URL: Link_ Approach_to_Credit_ Scoring_for_Micro_ Finance_in_Ghana_ A_Case_Study_of_KWIQPLUS _Money_Lending
  10. Sadatrasoul S., Gholamian M., Shahanaghi K. Combination of Feature Selection and Optimized Fuzzy Apriori Rules: The Case of Credit Scoring. The International Arab Journal of Information Technology, 2015, vol. 12, iss. 2, pp. 138–145. URL: Link
  11. Yu L., Wang S., Lai K.K. An Intelligent-Agent-Based Fuzzy Group Decision Making Model for Financial Multicriteria Decision Support: The Case of Credit Scoring. European Journal of Operational Research, 2009, vol. 195, iss. 3, pp. 942–959.
  12. Che Z.H., Wang H.S., Chuang C.L. A Fuzzy AHP and DEA Approach for Making Bank Loan Decisions for Small and Medium Enterprises in Taiwan. Expert Systems with Applications, 2010, vol. 37, iss. 10, pp. 7189–7199.
  13. Ignatius J., Hatami-Marbini A., Rahman A. et al. A Fuzzy Decision Support System for Credit Scoring. Neural Computing and Applications, 2016, pp. 1–17. doi: 10.1007/s00521-016-2592-1
  14. Lughofer E. Evolving Fuzzy Systems-Methodologies, Advanced Concepts and Applications. Berlin, Springer, 2011, 454 p. doi: 10.1007/978-3-642-18087-3
  15. Bosque G., del Campo I., Echanobe J. Fuzzy Systems, Neural Networks and Neuro-Fuzzy Systems: A Vision on Their Hardware Implementation and Platforms over Two Decades. Engineering Applications of Artificial Intelligence, 2014, vol. 32, pp. 283–331. doi: Link
  16. Zeng G., Zhao Q. A Rule of Thumb for Reject Inference in Credit Scoring. Mathematical Finance Letters, 2014, vol. 2014, pp. 1–13. URL: Link
  17. Piramuthu S. Financial Credit-Risk Evaluation with Neural and Neurofuzzy Systems. European Journal of Operational Research, 1999, vol. 112, iss. 2, pp. 310–321.
  18. Malhotra R., Malhotra D.K. Differentiating Between Good Credits and Bad Credits Using Neuro-Fuzzy Systems. European Journal of Operational Research, 2002, vol. 136, iss. 1, pp. 190–211.
  19. Baesens B., Setiono R., Mues C., Vanthienen J. Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation. Management Science, 2003, vol. 49, iss. 3, pp. 312–329.
  20. Baesens B., Van Gestel T., Viaene S. et al. Benchmarking State-of-the-Art Classification Algorithms for Credit Scoring. Journal of the Operational Research Society, 2003, vol. 54, iss. 6, pp. 627–635.
  21. Laha A. Building Contextual Classifiers by Integrating Fuzzy Rule Based Classification Technique and k-nn Method for Credit Scoring. Advanced Engineering Informatics, 2007, vol. 21, iss. 3, pp. 281–291.
  22. Jiao Y., Syau Y.R., Lee E.S. Modelling Credit Rating by Fuzzy Adaptive Network. Mathematical and Computer Modelling, 2007, vol. 45, iss. 5-6, pp. 717–731.
  23. Khashei M., Rezvan M.T., Hamadani A.Z., Bijari M. A Bi‐level Neural‐Based Fuzzy Classification Approach for Credit Scoring Problems. Complexity, 2013, vol. 18, iss. 6, pp. 46–57.
  24. Hájek P., Olej V. Intuitionistic Fuzzy Neural Network: The Case of Credit Scoring Using Text Information. Engineering Applications of Neural Networks – 16th International Conference, EANN 2015, Rhodes, Greece, September 25–28, 2015, Proceedings. Communications in Computer and Information Science 517, Springer, 2015, pp. 337–346.
  25. Derhami S., Smith A.E. An Integer Programming Approach for Fuzzy Rule-Based Classification Systems. European Journal of Operational Research, 2017, vol. 256, iss. 3, pp. 924–934. URL: Link
  26. Baradaran V., Keshavarz M. An Integrated Approach of System Dynamics Simulation and Fuzzy Inference System for Retailers' Credit Scoring. Economic Research – Ekonomska Istraživanja, 2015, vol. 28, iss. 1, pp. 959–980. doi: 10.1080/1331677X.2015.1087873
  27. Hoffmann F. Boosting a Genetic Fuzzy Classifier. Proc. Joint 9th IFSA World Congress and 20th NAFIPS International Conference, 2001. IEEE, 2001, vol. 3, pp. 1564–1569.
  28. Hoffmann F., Baesens B., Martens J. et al. Comparing a Genetic Fuzzy and a Neurofuzzy Classifier for Credit Scoring. International Journal of Intelligent Systems, 2002, vol. 17, iss. 11, pp. 1067–1083.
  29. Hoffmann F. Combining Boosting and Evolutionary Algorithms for Learning of Fuzzy Classification Rules. Fuzzy Sets and Systems, 2004, vol. 141, iss. 1, pp. 47–58.
  30. Hoffmann F., Baesens B., Mues C. et al. Inferring Descriptive and Approximate Fuzzy Rules for Credit Scoring Using Evolutionary Algorithms. European Journal of Operational Research, 2007, vol. 177, iss. 1, pp. 540–555.
  31. Del Jesus M.J., Hoffmann F., Navascués L.J., Sánchez L. Induction of Fuzzy-Rule-Based Classifiers with Evolutionary Boosting Algorithms. IEEE Transactions on Fuzzy Systems, 2004, vol. 12, iss. 3, pp. 296–308.
  32. Lahsasna A., Ainon R.N., Wah T.Y. Credit Risk Evaluation Decision Modeling through Optimized Fuzzy Classifier. Proc. Int. Symp. on Information Technology, 26–28 Aug., 2008, Kuala Lumpur, Malaysia. 2008. IEEE, 2008, vol. 1, pp. 1–8. doi: 10.1109/ITSIM.2008.4631606
  33. Lahsasna A., Ainon R.N., Wah T.Y. Enhancement of Transparency and Accuracy of Credit Scoring Models through Genetic Fuzzy Classifier. Maejo International Journal of Science and Technology, 2010, vol. 4, iss. 1, pp. 136–158.
  34. Sánchez L., Otero J. Boosting Fuzzy Rules in Classification Problems under Single‐Winner Inference. International Journal of Intelligent Systems, 2007, vol. 22, iss. 9, pp. 1021–1034.
  35. Nwulu N.I., Oroja S.G. A Comparison of Different Soft Computing Models for Credit Scoring. International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering, 2011, vol. 5, iss. 6, pp. 883–888.
  36. Mojisola Grace Asogbon, Oluwarotimi Williams Samuel. Comparative Analysis of Neural Network and Fuzzy Logic Techniques in Credit Risk Evaluation. International Journal of Intelligent Information Technologies (IJIIT), 2016, vol. 12, no. 1, pp. 47–62. doi: 10.4018/IJIIT.2016010103
  37. Akkoç S. An Empirical Comparison of Conventional Techniques, Neural Networks and the Three Stage Hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) Model for Credit Scoring Analysis: The case of Turkish credit card data. European Journal of Operational Research, 2012, vol. 222, iss. 1, pp. 168–178. URL: Link
  38. Gorzałczany M. B., Rudziński F. A Multi-Objective Genetic Optimization for Fast, Fuzzy Rule-Based Credit Classification with Balanced Accuracy and Interpretability. Applied Soft Computing, 2016, vol. 40, pp. 206–220.
  39. Schapire R.E. The Boosting Approach to Machine Learning: An overview. In: Nonlinear Estimation and Classification. New York, Springer, 2003, pp. 149–171.
  40. Korytkowski M., Rutkowski L., Scherer R. Fast Image Classification by Boosting Fuzzy Classifiers. Information Sciences, 2016, vol. 327, pp. 175–182. doi: Link
  41. Gibilisco B.M., Gowen M.A., Albert E.K. et al. Fuzzy Social Choice Theory. Springer, 2014, 186 p.
  42. Halgamuge S.K., Glesner M. Neural Networks in Designing Fuzzy Systems for Real World Applications. Fuzzy Sets and Systems, 1994, vol. 65, iss. 1, pp. 1–12.
  43. Carpenter G.A., Grossberg S., Markuzon N. et al. Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps. IEEE Transactions on Neural Networks, 1992, vol. 3, iss. 5, pp. 698–713.
  44. Nauck D., Kruse R. A Neuro-Fuzzy Method to Learn Fuzzy Classification Rules from Data. Fuzzy Sets and Systems, 1997, vol. 89, iss. 3, pp. 277–288.
  45. Tschichold-Gürman N. Generation and Improvement of Fuzzy Classifiers with Incremental Learning Using Fuzzy RuleNet. Proc. of the 1995 ACM Symposium on Applied Computing. ACM Nashville, Tennessee, USA, February 26–28, 1995, pp. 466–470.
  46. Pal S.K., Mitra S. Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing. New York, NY, USA, John Wiley & Sons, Inc., 1999, 375 p.
  47. Bunke H., Kandel A. (eds). Neuro-Fuzzy Pattern Recognition. World Scientific Publishing Company, Singapore, 2000.
  48. Melin P., Castillo O. A Review on Type-2 Fuzzy Logic Applications in Clustering, Classification and Pattern Recognition. Applied Soft Computing, 2014, vol. 21, pp. 568–577. URL: Link
  49. Kar S., Das S., Ghosh P.K. Applications of Neuro Fuzzy Systems: A Brief Review and Future Outline. Applied Soft Computing, 2014, vol. 15, pp. 243–259. URL: Link
  50. Angstenberger L. Dynamic Fuzzy Pattern Recognition with Applications to Finance and Engineering. New York, Springer Science+Business Media, 2001, 288 p. doi: 10.1007/978-94-017-1312-2
  51. Van Gestel T., Baesens B., Suykens J.A. et al. Bayesian Kernel Based Classification for Financial Distress Detection. European Journal of Operational Research, 2006, vol. 172, iss. 3, pp. 979–1003.
  52. Xiao W., Fei Q. A Study of Personal Credit Scoring Models on Support Vector Machine with Optimal Choice of Kernel Function Parameters. Systems Engineering: Theory & Practice, 2006, vol. 26, iss. 10, pp. 73–79.
  53. Lin C.F., Wang S.D. Fuzzy Support Vector Machines. IEEE Transactions on Neural Networks, 2002, vol. 13, iss. 2, pp. 464–471.
  54. Wang Y., Wang S., Lai K.K. A New Fuzzy Support Vector Machine to Evaluate Credit Risk. IEEE Transactions on Fuzzy Systems, 2005, vol. 13, iss. 6, pp. 820–831.

View all articles of issue

 

ISSN 2311-8709 (Online)
ISSN 2071-4688 (Print)

Journal current issue

Vol. 30, Iss. 3
March 2024

Archive