Document Type : Research Paper



This research study aims at using Data Mining and Fuzzy Logic
approaches to classify the credit scoring of banking system applicants
as to cover uncertainties and ambiguity connected with applicant
classes and also variables that affect their behavior.
The methodology, according to a standard Data Mining process, is to
collect and refine the client data, then those variables which are in
linguistic forms are converted to fuzzy variables under the supervision
of banking experts and final data are modeled using Fuzzy Decision
Tree, subsequently. The unfuzzy data are also modeled using the other
The results of the study suggest that as far as client distinction
accuracy is concerned Fuzzy Decision Tree produces better results
compared to Traditional Trees, Neural Networks, and statistical
procedures such as Logistic Regression and Bayesian Network.
However, it is not as accurate as Support Vector Machine and Genetic
Tree. On the other hand, Fuzzy Decision Tree technique has gained
better prediction than prediction performance of bank credit scoring


روئین تن، پونه، ) 3343 ( پایان نامه "بررسی عوامل موثر بر ریسک اعتباری مشتریان
حقوقی بانک )بررسی موردی بانک کشاورزی(" ، کارشناسی ارشد، دانشگاه شهید بهشتی.
2. سبزواری، حسن، ) 3343 ( "برآورد و مقایسه مدل امتیازدهی اعتباری لاجیت و روش
مطالعه موردی: مشتریان حقوقی بانک پارسیان(" ، پایان نامه کارشناسی ارشد، ( AHP
دانشگاه صنعتی شریف.
3. گودرزی، محمد رضا، ) 3343 ( "استفاده از منطق فازی در یادگیری درخت تصمیم فازی و
هرس کردن آن در محیط های مغشوش"، پایان نامه کارشناسی ارشد، دانشگاه علم و
3. مروج، مصطفی، ) 3343 (" افزودن قابلیت داده کاوی فازی به بانکهای اطلاعاتی رابطه ای
"، پایان نامه کارشناسی ارشد، دانشگاه صنعتی امیر کبیر.
4. مشکانی، علی؛ ناظمی، عبدالرضا، ) 3344 ( "داده کاوی کاربردی"، دانشگاه آزاد اسلامی
واحد نیشابور.
7. موسوی، ابتهاج، ) 3347 ( "بررسی عوامل موثر بر رتبه بندی اعتباری مشتریان" ، پایان نامه
کارشناسی ارشد، موسسه عالی بانکداری ایران.
7. Altman E.I, (1968) “Financial ratios discriminate analysis and the
prediction of corporate Bankruptcy”, The Journal of finance 23.
8. Banasik, J., Crook, J. and Thomas,L., (2003) “ Sample selection bias in
credit scoring models ”,Journal of the Operational Research Society,
vol.54, pp. 822–832.
9. Beaver W.H., (1967) “Financial ratios as Predictors of Failure”, Journal
Of Accounting Reserch.
10. Bird R.,(2003) “Fuzzy data analysis method for large volume of data”,
Project report in support of degree of master of engineering, University
of Bristol, Department of Engineering Mathematics.
11. Boggess W.B, (1967) “Screen-Test your credit Risks”, journal Of
Accounting Research 4.
12. Chapman. Pete & et al. (1999) “CRISP-DM 1.0: Step-by-step data
mining guide”,
13. Chen J.H., Ho S.Y., (2002) “Intelligent Multi-Objective Evolutionary
Algorithm for Editing Minimum Reference Set”, Proceedings of the 6th
998 مطالعات مدیریت صنعتی، سال نهم، شماره 52 ، تابستان 19
Pacific-Asia Conference on Knowledge Discovery and Data Mining,
Communications of the Institute of Information and Computing
Machinery, V. 5, No. 2, pp. 4-13.
14. Chiang I.J., and Hsu J.Y., (2002) “Fuzzy Classification Trees for Data
Analysis,” Fuzzy Sets and Systems, vol.130, no.1, pp.87-99.
15. Deakin E.B, (1972) “A Discriminate analysis of predictors of business
failure”, journal Of Accounting Research 10(1).
16. Deakin E.B, (1989) “Rational Economics Behavior and lobbying on
Accounting Issues: Evidence from the Oil and Gas Industry”, the
Accounting Review 66(1).
17. Durand D, (1941) “Risk element in consumer installment lending”,
National bureau of economic research, New york,
18. Edward F.R , Mishkin F.S.,(1995) “the decline of traditional banking:
implication for financial stability and regulatory policy”, Federal reserve
bank of New York policy Review, pp.27-45.
19. Fisher R.A, (1936) “The Use-of multiple measurement in Taxonomic
problem”, Annals of Eugenics.
20. Gehrke J., Ramakrishnan R., Ganti V., RainForest (1998) “A
Framework for Fast Decision Tree Construction of Large Datasets”,
Proc. 24th International Conference on Very Large Data Bases (VLDB).
21. Kiss, France, (2003) “credit scoring process from a knowledge
management prospective”, Budapest University of Technology And
Economics, pp:96-108.
22. Janikow C.Z., (1998) “Fuzzy Decision Trees: Issues and Methods,”
IEEE Trans. on Systems, Man, and Cybernetics, vol.28, no.1, pp.1-14.
23. Meier A.Savary C., Schindler G., Veryha Y, (2001) “Database Schema
with Fuzzy Classification and Classification Query Languag”, Proc. Of
the International Congress on Computational Intelligence-Mehods and
Applications. Bangor U.K.
24. Mikut R., Jens J., (2004) “Interpretability in Data-based Learning of
Fuzzy Systems”, Fuzzy Set and Systems, Elsevier.
25. Moon, C. G. and Stotsky, J. G., (1993) “Testing the Differences
Between the Determinants of Moody's and Standard & Poor's Ratings:
An Application of Smooth Simulated Maximum Likelihood
Estimation”, Journal of Applied Econometrics, Vol. 8, No. 1, pp. 51-69.
26. Morgan, Guaranty, (1994) “Risk Metrics Technical Document” ,2nd
Edition,New York: Morgan Guaranty.
27. Olaru C. and Wehenkel L.,(2003) “A Complete Fuzzy Decision Tree
Technique,” Fuzzy Sets and Systems, vol.138, no.2, pp.221-254.
28. Shafer J. ,Agrawal R., Mehta M.,(1996) “SPRINT: A Scalable Parallel
Classifier for Data Mining”, Proc. 22nd International Conference on
طبقه بندی متقاضیان تسهیلات اعتباری بانکی با... 998
Very Large Data Bases (VLDB).
29. Sahraoui H., Boukadoum M., Lounis H., (2000) “Using Fuzzy
Threshold Values for Predicting Class Libraby Interface Eolution”, In
Proceedings of the 4th International ECOOP workshop on Quantitative
Approaches in Object-Oriented Software Engineering, Nice (France).
30. Sattler K., Dunemann O.,(2001) “SQL Database Primitives for Decision
Tree Classifiers”, In Proc. of the 10th ACM CIKM Int. Conf. on
Information and Knowledge Management, November 5-10, Atlanta,
Georgia, USA.
31. Weber R., (1992) “Automatic Knowledge Acquisition for Fuzzy Control
Applications”, Proc. lnternat. Symp. on Fuzzy Systems (lizuka, Japan,12
15 July 1992), pp. 9-12.
32. Weber R., (1992) “Fuzzy-ID3: A Class of Methods for Automatic
Knowledge Acquisition”, Proc. 2nd Internat. Con[i on Fuzzy Logic &
Neural Networks, pp.265-268.
33. William F.Treacy &Mark S.Carey, (1998) “Credit risk rating at large,” Federal Reserve Bulletin, Board of Governors of Federal
reserve System (U.S).
34. Yang, Lui, (2001) “New Issues In Credit Scoring Applications”,
George-August ,University Gottingen, Institute For Wirtschafts
informatics, pp.3 .
35. Zimmerman H.J.,(1992) “Fuzzy set theory and its application”,Kluwer
Academic Publishers, London.