Prior credit assessment of long-term SME projects with non-standard cash flows
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Author(s)Serhii OnikiienkoLink to ORCID Index: https://orcid.org/0000-0002-3450-6267
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Yevheniia PolishchukLink to ORCID Index: https://orcid.org/0000-0002-6133-910X
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Alla IvashchenkoLink to ORCID Index: https://orcid.org/0000-0002-4599-7137
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Anna KornyliukLink to ORCID Index: https://orcid.org/0000-0001-8713-0681
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Nazar DemchyshakLink to ORCID Index: https://orcid.org/0000-0001-6852-7405
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DOIhttp://dx.doi.org/10.21511/bbs.16(2).2021.14
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Article InfoVolume 16 2021 , Issue #2, pp. 148-158
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366 Downloads
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Creative Commons Attribution 4.0 International License
Over the past three decades, the relative bank loan demand has changed due to the arising small and medium-sized enterprises (SMEs). Therefore, banks in their operations face the problem of processing an ever-increasing number of loan applications.
The aim of this paper is to develop an auxiliary approach to assessing the prior creditworthiness of long-term SME projects with nonstandard cash flows.
This study reveals how the principles of value-based management can be incorporated into the process of borrower’s creditworthiness assessment to improve the process of screening loan applications. For this, the internal rate of return was used as a criterion for loan granting decision at the initial stage of loan underwriting.
An algorithm for the preliminary evaluation of loan applications is proposed and is based on the principle of maximizing the shareholder value of banks. This algorithm helps to define the credit terms taking into consideration the distribution of positive cash flows throughout the project’s expected economic life, calculate the possible real effective interest rate concerning the borrower’s nonstandard cash flow schedule, make a rough analysis on the economic efficiency of lending and state the necessary criterion to initiate the procedure of loan underwriting for the projects with nonstandard cash flow schedules.
The proposed estimation algorithm stemming from the IRR-approach for the cash flow analysis can also be initially used by a borrower as a tool for credit solvency self-testing via screening of periods with corresponding cash flows that can be used for loan servicing.
- Keywords
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JEL Classification (Paper profile tab)G21, E51, H81
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References19
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Tables5
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Figures2
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- Figure 1. CF schedules
- Figure 2. Preliminary evaluation of the loan application
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- Table 1. Comparison of loan and project terms
- Table 2. Selected scenarios for the realization of the algorithm
- Table 3. Cash flows planed by a potential borrower for loan servicing
- Table 4. Numerical illustration: Lending capacity assessment and loan terms calculation for the case when the potential borrower’s cash flows intended for loan servicing are not regular
- Table 5. Numerical illustration: Lending capacity assessment and loan terms calculation for rearranged cash flows
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- Brunzell, T., Liljeblom, E., & Vaihekoski, M. (2013). Determinants of capital budgeting methods and hurdle rates in Nordic firms. Accounting and Finance, 53(1), 85-110.
- Caner, S., & Karan, M. B. (2012). Screening Creditworthiness of SME’s: The Case of Small Business Assistance in Turkey. Multinational Finance Journal, 16(1/2), 1-20.
- Cusmano, L., & Koreen, M. (2015). New Approaches to SME and Entrepreneurship Financing: Broadening the Range of Instruments. OECD Publishing.
- Driver, C., & Temple, P. (2010). Why do hurdle rates differ from the cost of capital? Cambridge Journal of Economics, 34(3), 501-523.
- Dubyna, M., Zhavoronok, A., Kudlaieva, N., & Lopashchuk, I. (2021). Transformation of household credit behavior in the conditions of digitalization of the financial services market. Journal of Optimization in Industrial Engineering, 14(1), 97-102.
- Hanweck, G. A., & Ryu, L. H. (2005). The Sensitivity of Bank Net Interest Margins and Profitability to Credit, Interest-Rate, and Term-Structure Shocks Across Bank Product Specializations (FDIC Working Paper No. 2005-02).
- Hurjui, I., & Hurjui, M. C. (2008). Investment Projects: General Presentation, Definition, Classification, Characteristics, the Stages. The USV Annals of Economics and Public Administration, 8(1), 92-98.
- Jagtiani, J., & Lemieux, C. (2016). Small business lending after the financial crisis: A new competitive landscape for community banks. Economic Perspectives, 40(3), 1-30.
- Kastro, B., & Kulakov, N. Yu. (2016). Definition of the concepts of conventional and non-conventional projects. Business Informatics, 2(36), 16-23.
- Khovrak, I. V., & Petchenko, M. V. (2015). Estimating the level of financial safety in banking institutions. Actual Problems of Economics, 164(2), 347-354.
- Koller, T. (1994). What is value-based management? An excerpt from Valuation: Measuring and Managing the Value of Companies, second edition. The McKinsey Quarterly, 3, 87-101.
- Koteshov, D. (2019). The Current State of SME Lending. Lending Times.
- Mills, K. G., & McCarthy, B. (2014). The State of Small Business Lending: Credit Access During the Recovery and How Technology May Change the Game (Harvard Business School Working Paper No. 15-004).
- OECD. (2019). Financing SMEs and Entrepreneurs 2019: An OECD Scoreboard. Paris: OECD Publishing.
- Page, H. (2016). Seven key challenges in assessing SME credit risk. Whitepaper. Moody’s analytics.
- Pike, R. & Dobbins, R. (1988). Investment decision and financial strategy (pp. 40-44). Oxford: Philip Allan.
- Polishchuk, Y., Kornyliuk, A., Lopashchuk, I., & Pinchuk, A. (2020). SMEs debt financing in the EU: on the eve of the coronacrisis. Banks and Bank Systems, 15(3), 81-94.
- Wasiuzzaman, S., Nurdin, N., Abdullah, A. H., & Vinayan, G. (2019). Creditworthiness and access to finance: a study of SMEs in the Malaysian manufacturing industry. Management Research Review, 43(3), 293-310.
- Weth, M. A. (2002). The Pass-Through from Market Interest Rates to Bank Lending Rates in Germany (Discussion Paper No. 11/02). Deutsche Bundesbank, Economic Research Centre.
