A RESEARCH ON USE OF CREDIT SCORING MODELS IN SMALL BUSINESS LENDING DECISIONS

Year 2018, , 934 - 953, 27.12.2018

Mustafa Çelik , Hüseyin Dalğar , Ömer Tekşen , Ahmet Furkan Sak

https://doi.org/10.30798/makuiibf.422139

Abstract

The aim of this research is to
disclose the criterias that are used in banks’ credit scoring systems  for small business lending decisions and to remove
information asymetry for small businesses in fund raising process from
financial intermediaries. In this context, interviews are conducted with bank
branch managers and credit portfolio managers in Burdur City that are selected
by purposeful sampling. Data that is gathered by interviews is analyzed through
descriptive analysis. The results are presented under the titles of  5C (Character, Capacity, Capital, Conditions
and Colleteral) of credit. According to results, suggestions for academy, small
business and regulators are presented. 

Keywords

Small Business, Deposit Banks, Credit Scoring, Interview

KOBİLERİN KREDİLENDİRİLME KARARLARINDA KREDİ SKORLAMA MODELLERİNİN KULLANIMI ÜZERİNE BİR ARAŞTIRMA

Abstract

Bu çalışmanın amacı, KOBİ’lerin
kredibilitesini tespit ederken bankalarca kullanılan kredi skorlama
modellerindeki kriterleri açıklamak ve finansal kurumlardan fon sağlama sürecinde
KOBİ’ler aleyhine olan bilgi asimetrisini ortadan kaldırmaktır. Bu kapsamda,
amaçsal örnekleme ile seçilen Burdur ilindeki 10 mevduat bankasının şube ve
portföy yöneticileri ile mülakat çalışmasına gidilmiştir. Mülakatlardan elde
edilen veriler betimsel analize tabi tutulmuş ve sonuçlar literatürde
kredilendirmenin 5K’sı olarak anılan karakter, kapasite, kapital, koşullar ve
karşılıklar başlıkları altında sunulmuştur. Sonuçlar ışığında, akademiye, KOBİ’lere
ve düzenleyici ve denetleyici kuruluşlara öneriler getirilmiştir. 

Keywords

KOBİ, Mevduat Bankaları, Kredi Skorlama, Görüşme

References

• AKKOÇ, S., (2012). “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, 222(1), 168-178.
• ALTMAN, E. I. (1980). “Commercial Bank Lending: Process, Credit Scoring, And Costs Of Errors in Lending”, Journal of Financial and Quantitative Analysis, 15(4), 813-832.
• ARTİS, M., GUİLLEN, M., MARTİNEZ, J.M. (1994). “A Model for Credit Scoring: An Application of Discriminant Analysis” Questiio, 18(3), 385-395.
• AVERY, R.B., CALEM, P.S., CANNER, G.B. (2004). “Consumer Credit Scoring: Do Situational Circumstances Matter?”, Journal of Banking & Finance, 28, 835-856.
• AVERY, R.B., BREVOORT, K.P., CANNER, G.B. (2009).” Credit Scoring and Its Effects on the Availability and Affordability of Credit”, The Journal of Consumer Affairs, 43(3), 516-537.
• AZİZ, A.R. (2014). “Consumer Loan Credit Scoring Model for Pakistani Commercial Banks: An Application of Discriminant Analysis”, Market Forces, 9(2), 21-30.
• BAKLOUTİ, I. (2014). “A Psychological Approach to Microfinance Credit Scoring via a Classification and Regression Tree”, Intelligent Systems in Accounting, Finance and Management, 21(4), 193-208.
• BERGER, A.N., FRAME, W.S., MİLLER, N.H. (2005). “Credit Scoring and the Availability, Price, and Risk of Small Business Credit”, Journal of Money, Credit and Banking, 37(2), 191-222.
• BERGER, A.N., COWAN, A.M., FRAME, W.S. (2011). “The Surprising Use of Credit Scoring in Small Business Lending by Community Banks and the Attendant Effects on Credit Availability, Risk, and Profitability”, Journal of Financial Services Research, 39, 1-17.
• BLANCO, A., MEJİAS, R.P., LARA,J., RAYO, S. (2013). “Credit Scoring Models for the Microfinance Industry Using Neural Networks: Evidence from Peru”, Expert Systems with Applications, 40(1), 356-364.
• BOLGÜN, K.E, AKÇAY, M.B. (2005). Risk Yönetimi: Gelişmekte Olan Türk Finans Piyasasında Entegre Risk Ölçüm ve Yönetim Uygulamaları (2. Baskı), İstanbul: Scala Yayıncılık.
• BÜYÜKÖZTÜRK, Ş., ÇAKMAK, E.K., AKGÜN, Ö.E., KARADENİZ, Ş., DEMİREL, F. (2013). Bilimsel Araştırma Yöntemleri (15. Baskı), Ankara: Pegem Akademi. CHARLES, W. (2014, Şubat 19). FICO Score Vs FAKO Score: What’s The Difference?, 05.03.2018 tarihinde https://www.doctorofcredit.com/fico-score-vs-fako-score-whats-the-difference/ sitesinden alınmıştır.
• CROUCHY, M., GALAİ, D., MARK, R. (2000). ““A Comparative Analysis Of Current Credit Risk Models”, Journal of Banking and Finance, 24, 59-117.
• DETWEİLER, G. (2012,Eylül 30). How Do Credit Scores Work in Other Countries?, 06.03.2018 tarihinde https://www.huffingtonpost.com/creditcom/how-do-credit-scores-work_b_1723362.html sitesinden alınmıştır.
• DEYOUNG, R., GLENNON, D., NİGRO, P. (2008). “Borrower–Lender Distance, Credit Scoring, and Loan Performance: Evidence from Informational-Opaque Small Business Borrowers”, Journal of Financial Intermediation, 17, 113-143.
• DİAZ , D., GEMMİLL, D. (2011) “A Systematic Comparison of Two Approaches To Measuring Credit Risk: CreditMetrics versus CreditRisk+”, Working Paper Series, 17.04.2018 tarihinde http://www.actuaries.org/events/congresses/Cancun/ica2002_subject/credit_risk/credit_x_diazledezma.pdf adresinden alınmıştır.
• EİSENBEİS, R.A. (1978). “Problems in Applying Discriminant Analysis in Credit Scoring Models”, Journal of Banking & Finance, 2(3), 205-219.
• FAİR ISAAC CORPORATİON. (2018). “Understanding FICO Scores”, FICO Bilgi Formu. 05.03.2018 tarihinde https://www.myfico.com/Downloads/Files/myFICO_UYFS_Booklet.pdf adresinden alınmıştır.
• FICO (2018), “Solution Architecture The FICO® Score”, 17.03.2018 tarihinde http://www.fico.com/en/products/fico-score#marketecture adresinden alınmıştır.
• FİNDEKS. (2018). “Findeks Kredi Notu’nun Bileşenleri”, 16.03.2018 tarihinde https://www.findeks.com/urunler/findeks-kredi-notu adresinden alınmıştır.
• GHODSELAHİ, A. (2011). “A Hybrid Support Vector Machine Ensemble Model for Credit Scoring”, International Journal of Computer Applications, 17(5), 1-5.
• GLASSMAN, C.A., WİLKİNS, H.M. (1997). “Credit Scoring: Probabilities and Pitfalls”, Journal of Retail Banking Services, 19(2), 53-56.
• HANSELL, S. (1995, 18 Nisan). “Need a Loan? Ask the Computer; 'Credit Scoring' Changes Small-Business Lending”, New York Times, D1.
• HARDY, W.E., ADRİAN, J.L.(1985). “A Linear Programming Alternative to Discriminant Analysis in Credit Scoring”, Agribusiness, 1(4), 285-292.
• HARRİS, T. (2015). “Credit Scoring Using the Clustered Support Vector Machine”, Expert Systems with Applications, 42(2), 741-750.
• IRBY, L. (2018). “FICO & FAKO Credit Scores: Understanding the Differences”, 15.03.2018 tarihinde https://www.thebalance.com/fico-and-fako-credit-scores-960497 adresinden alınmıştır.
• İÇ, Y. T., YURDAKUL, M. (2010). “Development of a Quick Credibility Scoring Decision Support System Using Fuzzy TOPSIS”, Expert Systems with Applications, 37(1), 567-574.
• JENSEN, H.L.(1992). “Using Neural Networks for Credit Scoring”, Managerial Finance, 18(6), 15-26
• KAO, L.J., CHİU, C.C., CHİU, F.Y. (2012). “A Bayesian Latent Variable Model with Classification and Regression Tree Approach for Behavior and Credit Scoring”, Knowledge-Based Systems, 36, 245-252.
• KAVCIOĞLU, Ş. (2011). “Ticari Bankacılıkta Kredi Riskinin ve Kredi Riski Ölçüm Modellerinin Değerlendirilmesi”, Finansal Araştırmalar ve Çalışmalar Dergisi, 3(5), 11-19.
• KESHAWARZ, H. G., AYATİ, G.H. (2008). “A Comparison Between Logit Model And Classification Regression Trees (CART) in Customer Credit Scoring Systems”, The Economic Research, 7(4), 71-97.
• KHASHMAN, A. (2011). “Credit Risk Evaluation Using Neural Networks: Emotional Versus Conventional Models”, Applied Soft Computing, 11(8), 5477-5484.
• KHEMAİS, Z., NESRİNE, D., MOHAMED, M. (2016). “Credit Scoring and Default Risk Prediction: A Comparative Study between Discriminant Analysis & Logistic Regression”, International Journal of Economics and Finance, 8(4), 39-53.
• KREDİ KAYIT BÜROSU (2018). “Findeks Kredi Notu”. 17.03.2018 tarihinde https://www.kkb.com.tr/urunler/findeks-kredi-notu adresinden alınmıştır.
• LAUFER, S., AND PACİOREK, A. (2016). “The Effects of Mortgage Credit Availability: Evidence from Minimum Credit Score Lending Rules,” Finance and Economics Discussion Series 2016-098. Washington: Board of Governors of the Federal Reserve System.
• LİM, M.K., SOHN, S.Y. (2007). “Cluster-Based Dynamic Scoring Model”. Expert Systems with Applications, 32(2), 427–431.
• MAKUCH, W. M. (2001). “Handbook of Credit Scoring”, Mays, E. (ed.), Scoring Applications (3-20), Chicago: Glenlake Publishing.
• MARQUES, A.I., GARCİA, V. SANCHEZ, J.S. (2013). “A Literature Review on the Application of Evolutionary Computing to Credit Scoring”, Journal of the Operational Research Society, 64, 1384-1399.
• MESTER, L.J. (1997). “What’s the Point of Credit Scoring?”, Federal Reserve Bank Of Philadelphia Business Review, Şubat-1997, 3-16.
• NİLİ, M., SABZEVARİ, H.(2008). “Credit Scoring Models For A Private Bank: A Comparison Between The Logit And Ahp Methods”, Industrial Engineering & Management Sharif, 24(43), 105-117.
• PATTON, M.Q.(2002). Qualitative Research and Evaluation Methods (3rd Ed.), Thousand Oaks, California: Sage.
• ROBSON, C. (2002). Real World Research : A Resource for Social Scientists And Practitioner-Researchers (2nd ed.). Madden, Mass., Oxford: Blackwell Publishers.
• SOARES, M.C. (2006). “Modelling of An Indicator for Credıit Scoring of Non-Financial Corporations – A Preliminary Research Based On Discriminant Analysis”, Banco de Portugal Financial Stability Report 2006.
• SOYDANER, D., KOCADAĞLI, O. (2015). “Artificial Neural Networks with Gradient Learning Algorithm for Credit Scoring”, İstanbul Üniversitesi İşletme Fakültesi Dergisi, 44(2), 3-12.
• T.C. RESMİ GAZETE, 04.11.2012 Tarih ve 28457 Sayılı, “Küçük ve Orta Büyüklükteki İşletmelerin Tanımı, Nitelikleri ve Sınıflandırılması Hakkında Yönetmelikte Değişiklik Yapılmasına Dair Yönetmelik”.
• THOMAS, L. CROOK, J. EDELMAN, D. (2017). Credit Scoring and Its Applications. Philedelphia: SIAM.
• TORUN, T. (2013). “Bankalarda Kredi Yönetimi”, Erdem, E.(ed.), Kredi Değerlendirme Süreci,Mali Tahlil ve Kredi Hesaplamaları (51-97), Eskişehir: Anadolu Üniversitesi Web-Ofset.
• UYAR, K. İLHAN, Ü. (2009). “Fuzzy Type 2 Inference System for Credit Scoring”, Proceedings of the 11th WSEAS International Conference on Automatic Control, Modelling and Simulation, 518-523.
• VURUCU, M., ARI, M. U. (2014). A’dan Z’ye Bankacılık Yasal Mevzuat, Ürün ve Hizmetler, Uygulamalar. Ankara: Seçkin Kitabevi.
• WENDİ, W. (2014). “An Application of Logit Model to Credit Scoring and its Implications to Financial Market”. 06.03.2018 tarihinde http://scholarbank.nus.edu.sg/handle/10635/78936 adresinden alınmıştır.
• XU, X., ZHOU, C., WANG, Z. (2009). “Credit Scoring Algorithm Based on Link Analysis Ranking with Support Vector Machine”, Expert Systems with Applications, 2(2), 2625-2632.
• YILDIRIM, A., ŞİMŞEK, H. (2003). Sosyal Bilimlerde Nitel Araştırma Yöntemleri. Ankara: Seçkin Yayınları .
• ZHOU X., ZHANG D., JİANG Y. (2008). A New Credit Scoring Method Based on Rough Sets and Decision Tree. In: Washio T., Suzuki E., Ting K.M., Inokuchi A. (ed.) Advances in Knowledge Discovery and Data Mining. PAKDD 2008. Lecture Notes in Computer Science, vol 5012. Springer, Berlin, Heidelberg.