Credit Scoring Modelling For Corporate Banking Institutions
DOI:
https://doi.org/10.58229/jims.v2i1.125Keywords:
Financial Ratio, Credit Rating, Corporate Banking, Credit ScoringAbstract
This research aims to build a credit scoring modeling simulation of bank corporate loans. The credit scoring model is used in assessing creditworthiness in credit decisions. This model determines whether or not a company is eligible for the corporate credit facility it proposes. Observations were made of 100 companies included in the list of Kompas100 Index formers on the Indonesia Stock Exchange (IDX) that have the potential to apply for loans/credits to Bank Financial Institutions (IKB) in optimizing the corporate capital structure through bank debt facilities in the period 2022. Analysis was conducted on five financial aspects consisting of 14 research variables, including (i) liquidity aspects, including current ratio and quick ratio variables; (ii) solvency aspects, including debt asset ratio and equity ratio variables; (iii) profitability aspects including return on net assets, operating profit ratio, price to earnings ratio variables, (iv) activity aspects including total asset turnover, accounts receivable turnover, inventory turnover, current assets turnover, and (v) growth aspects including operating income growth rate, total assets growth rate, and operating profit growth rate variables. The analysis tool uses Logistic Regression through an assessment conducted on the company's credit rating as a proxy for the dependent variable, worth one if the credit application is feasible and worth 0 if the credit application is not feasible with a cut-off point of 0.5. The results show that credit scoring modeling for corporate credit is significantly formed from liquidity (CR) and solvency (DER) aspects. Out of 61 companies classified as not eligible for credit facilities, 58 companies were classified correctly, and out of 39 companies classified as eligible, 29 companies were classified correctly. The overall percentage shows 68.0, meaning that the logistic regression model has an accuracy of 68%.
References
Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23(4), 589-609. http://dx.doi.org/10.1111/j.1540-6261.1968.tb00843.x
Araghi, K., Maryam. (2013). Comparing Logit, Probit and Multiple Discriminant Analysis Models in Predicting Bankruptcy of Companies. Journal of Finance & Accounting 5 (1), 48-59.
Conan, J., & Holder, M. (1979). Variables explicatives de performances et contrôle de gestion dans les PMI. Thèse de Doctorat en sciences de gestion, Université de Paris IX.
Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accounting Research, spring 1972, 167-179. http://dx.doi.org/10.2307/2490225
Helder, V., et al. (2022). Application of the VNS Heuristic for Feature Selection in Credit Scoring Problems. Machine Learning with Application 9. https://doi.org/10.1016/j.mlwa.2022.100349
Hlongwane, et al. (2014). Censored Regression Techniques for Credit Scoring: A Case Study for the Commercial Bank of Zimbabwe (Bulawayo). International Journal of Economics and Finance 6(10). http://dx.doi.org/10.5539/ijef.v6n10p227.
Hu Y, and Su J. (2022). Research ini Credit Risk Evaluation of Commercial Banks Based on Artificial Neural Network Model. Procedia Computer Science 199. https://creativecommons.org/licenses/by-nc-nd/4.0.
Jacobson T., and Roszbach K. (2003). Bank lending policy, credit scoring and value-at-risk. Journal of Banking & Finance 27(4), 615-633. https://doi.org/10.1016/S0378-4266(01)00254-0.
Kanno M. (2020). Credit rating migration risk and interconnectedness in a corporate lending network. Research in International Business and Finance 54. https://doi.org/10.1016/j.ribaf.2020.101282.
Khemais, et al. (2016). Credit Scoring and Default Risk Prediction: A Comparative Study between Discriminant Analysis & Logistic Regression. International Journal of Economics and Finance 8. http://dx.doi.org/10.5539/ijef.v8n4p39.
Lainez, L. (2021). Regulating Digital Credit Scoring in Vietnam. Perspective 20. Microsoft Word - ISEAS_Perspective_2021_20.docx (think-asia.org).
Lessmann, S., et al. (2015). Benchmarking state-ofthe-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research, 247(1), 124–136.
Mihalovič, M. (2016). Performance Comparison of Multiple Discriminant Analysis and Logit Models in Bankruptcy Prediction. Economics and Sociology 9 (4), 101-118. DOI: 10.14254/2071-789X.2016/9-4/6.
Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, Spring, 109-131. http://dx.doi.org/10.2307/2490395
Pindyck, R., Daniel L. (1998). Econometric Models and Economic Forecasts Fourth Edition, Mc Graw Hill, New York.
Saunders, A., & Allen, L. (2002). Credit Risk Measurement: New Approaches to Value at Risk and Other Paradigms. New York: John Wiley and Sons. Retrieved From http://www.untag-smd.ac.id/files/Perpustakaan_Digital_1/CREDIT%20RISK%20Credit%20risk%20measu rement,%20New%20approaches%20to%20value%20at%20risk%20and%20other%20paradigms.pdf
Shih, et-al. (2022). A Framework of Global Credit-Scoring Modeling Using Outlier Detection and Machine Learning in a P2P Lending Platform. Mathematics 10, 2282. https://doi.org/10.3390/ math10132282.
Siddiqi N. (2017). Intelligent Credit Scoring: Building and Implementing Better Credit Risk Scorecards. Wiley. ISBN 9781119279150.
Simatupang M, et al. (2023). Corporate Credit Scoring Model of Banking Sector in Indonesia. Heliyon. http://dx.doi.org/10.2139/ssrn.4312660.
Tabak B., et al. (2016). Financial stability and bank supervision. Finance Research Letters 18, 322-327. https://doi.org/10.1016/j.frl.2016.05.008.
Thomas, et al. (2002). Credit scoring and its applications: SIAM monographs on mathematical modeling and computation. Society for Industrial and Applied Mathematics. ISBN 9780898714838.
Thomas, L., et al. (2002). Credit Scoring and Its Applications. Philadelphia: Society for Industrial and Applied Mathematics. http://dx.doi.org/10.1137/1.9780898718317
Tzong, H. (2009). A cross model study of corporate financial distress prediction in Taiwan: Multiple discriminant analysis, logit, probit and neural networks models. Mathematics, Neurocomputing 72, 3507–3516. DOI:10.1016/j.neucom.2009.02.018.
Van J, et al. (2012). Credit scoring for microfinance: is it worth it? International Journal of Finance & Economics 17(2), 103-123. https://doi.org/10.1002/ijfe.444.
Wojewodzki M, et al. (2020). Credit rating, banks' capital structure and speed of adjustment: A cross-country analysis. Journal of International Financial Markets, Institutions and Money 69. https://doi.org/10.1016/j.intfin.2020.101260.
Yakymova, et al. (2019). The Use of Discriminant Analysis in The Assessment of Municipal Company’s Financial Health. Economic and Sosiology, 12 (2), 64-78. doi: 10.14254/2071-789X.2019/12-2/4.
Young D, et al. (2004). The Past, Present, and Probable Future for Community Banks. Journal of Financial Services Research, 25, 85-133. http://dx.doi.org/10.1023/B:FINA.0000020656.65653.79
Yu, W., Xu, W., and Zhu, L. (2017). A Modified Hosmer-Lemeshow Test for Large Data Sets. Communications in Statistics-Theory and Methods Vol. 46, Issue 23, https://doi.org/10.1080/03610926.2017.1285922.
Book
Hosmer, D., Lemeshow, S., and Sturdivant, R. (2013). Applied Logistic Regression. John Wiley & Sons, Inc. ISBN: 9780470582473. DOI: 10.1002/97811185.
Internet Source
Laporan Perekonomian Indonesia 2022: Sinergi dan Inovasi Memperkuat Ketahanan dan Kebangkitan Menuju Indonesia Maju. https://www.bi.go.id/id/publikasi/laporan/Documents/LPI_2022.pdf. Diakses pada 25 Februari 2023.
Laporan Profil Industri Perbankan. https://www.ojk.go.id/id/kanal/perbankan/data-dan-statistik/laporan-profil-industri-perbankan/Documents/LPIP%20TW%20III%202022.pdf. Diakses pada 25 Februari 2023.
Peraturan Menteri Keuangan Nomor 169/PMK.010/2015 Tahun 2015 tentang Penentuan Besarnya Perbandingan Antara Utang Dan Modal Perusahaan Untuk Keperluan Penghitungan Pajak Penghasilan. PMK No. 169/PMK.010/2015 (bpk.go.id). Diakses pada 15 September 2023.
