Development of Company’s Creditworthiness Analysis Model Based on the Characteristics of the National Market

  • Miloš Ilić Erste Bank ad Novi Sad, Serbia
  • Dušan Saković MK Group doo Belgrad
  • Mile Stanišić University Singidunum
Keywords: analytical indicators, financial statements, loan placement, Creditworthiness,

Abstract


The main issue for all creditors in the Republic of Serbia is how to effectively lend to companies, with adequate increase of rate of return. Namely, domestic creditors are using traditional credit analysis methods, which are very slow and very expensive, or on the other hand using one of the world most developed models for assessing company's creditworthiness, which are still not adapted to the characteristics and performance of domestic companies. It is emphasized from a professional and practical standpoint, that the models for assessing company's creditworthiness developed for individual market, give significantly better results than other “standardized models“, and this represents the basic assumption of this research. Financial statements of domestic companies, together with their analytical indicators, in best way reflect their characteristics. As such, they are the basis for developing a model for assessing companies’ creditworthiness, which is the main goal of the research. The hypothesis of the paper is “Respecting the characteristics of domestic companies, it is possible to develop a creditworthiness analysis model for the Republic of Serbia, that will be more efficient than generally known models”. The result of the research is a model that can be practically applied, which contributes to increasing efficiency in decision making, and which should represent “condition sine qua non” of each lender in Serbia.


Author Biography

Miloš Ilić, Erste Bank ad Novi Sad, Serbia

Miloš Ilić

Head of Client Service Unit

Retail Operations Department

Bank Operations Division

Erste Bank a.d. Novi Sad

Bulevar Oslobođenja 5
21 000 Novi Sad
Tel: +381 (0) 21 480 9547

Mob: +381 (0) 60 8747 767

E-mail: milos.ilic@erstebank.rs
www.erstebank.rs

References

Altman, E.I., & Sabato, G. (2007). Modelling Credit Risk for SMEs: Evidence from the U.S. Market. Abacus, 43(3), 332-357. doi:10.1111/j.1467-6281.2007.00234.x

Baesens, B. (2003). Developing intelligent systems for credit scoring using machine learning techniques. Leuven: K. U. Leuven. (Unpublished PhD thesis).

Dahiya, S., Handa,S.S., Singh, N.P. (2015). Credit Scoring Using Ensemle of Various Classifiers on Reduced Feature Set, Industrija, 43(4), 163-174. Retrieved from: https://scindeks-clanci.ceon.rs/data/pdf/0350-0373/2015/0350-03731504163D.pdf

Fernandes, G.B., & Artes, R. (2016). Spatial dependence in credit risk and its improvement in credit scoring. European Journal of Operational Research, 249(2), 517-524. doi:10.1016/j.ejor.2015.07.013

Caracota, R.C., Dimitru, M., & Dinu, M.R. (2010). Building a Small and Medium Enterprises. Theoretical and Applied Economics, 9(550), 117-128, retrieved from: http://www.ectap.ro/theoretical-and-applied-economics-number-9-2010/r66/.

Husein, M.F., & Pambekti, G.T. (2015). Precision of the models of Altman, Springate, Zmijewski, and Grover for predicting the financial distress. Journal of Economics, Business and Accountancy Ventura, 17(3), 405. doi:10.14414/jebav.v17i3.362

Falbo, P. (1991). Credit-scoring by enlarged discriminant models. International Journal of Management Science, 19(4), 275-289. doi:10.1016/0305-0483(91)90045-u

Feng, D., Gourieroux, C., & Jasiak, J. (2008). The ordered qualitative model for credit rating transitions. Journal of Empirical Finance, 15(1), 111-130. doi:10.1016/j.jempfin.2006.12.003

Gehrlein, W.V., & Wagner, B.J. (1997). A two-stage least cost credit scoring model. Annals of Operations Research, 74, 159-171. doi:10.1023/a:1018914219633

Gupta, J., Wilson, N., Gregoriou, A., & Healy, J. (2014). The effect of internationalisation on modelling credit risk for SMEs: Evidence from UK market. Journal of International Financial Markets, Institutions and Money, 31, 397-413. doi:10.1016/j.intfin.2014.05.001

Hayden, E. (2003). Are Credit Scoring Models Sensitive With Respect to Default Definitions? Evidence from the Austrian Market. SSRN Electronic Journal, Paper presented at the EFMA 2003 Helsinki Meetings. doi:10.2139/ssrn.407709

Ilić, M., & Saković, D. (2017). Quantitative indicators in function of company’s creditworthiness assessment. Economic Outlook, 19(2), 1-16. retrieved from: http://www.ekonomskipogledi.pr.ac.rs/.

Lessmann, S., Baesens, B., Seow, H., & Thomas, L.C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research, 247(1), 124-136. doi:10.1016/j.ejor.2015.05.030

Orgler, Y.E. (1970). A Credit Scoring Model for Commercial Loans. Journal of Money, Credit and Banking, 2(4), 435. retrieved from: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flinks.jstor.org%2Fsici%3Fsici%3D0022-2879%2528197011%25292%253A4%253C435%253AACSMFC%253E2.0.CO%253B2-7%26origin%3Dbc;h=repec:mcb:jmoncb:v:2:y:1970:i:4:p:435-45. doi:10.2307/1991095

Sembiring, T.M. (2015). Bankruptcy Prediction Analysis of Manufacturing Companies Listed in Indonesia Stock Exchange. International Journal of Economics and Financial Issues, 5(1), 354-359. Retrieved from: http://www.econjournals.com/.

Smaranda, C. (2014). Scoring Functions and Bankruptcy Prediction Models – Case Study for Romanian Companies. Procedia Economics and Finance, 10, 217-226. doi:10.1016/s2212-5671(14)00296-2

Steenackers, A., & Goovaerts, M.J. (1989). A credit scoring model for personal loans. Insurance: Mathematics and Economics, 8(1), 31-34. doi:10.1016/0167-6687(89)90044-9

Vukadinović, P., Cerović, S., Matović V., Stevanović, G. (2018). Financial position and Credit Rating of Companies in circular Economy in Serbia, Industrija, 46(2), 77-98, retrieved from: https://scindeks-clanci.ceon.rs/data/pdf/0350-0373/2018/0350-03731802077V.pdf

Published
2019/08/13
Section
Original Scientific Paper