Modelling credit risk using system dynamics: The case of licensed credit reference bureaus in Kenya

Yıl 2023, Cilt: 3 Sayı: 1, 47 - 56, 30.06.2023

Florence Kanyambu Lucy Waruguru

Öz

Credit reference bureaus (CRBs) have been operational in Kenya for many years owing to the large number of borrowers who fail to repay their loans. However, regulating how credit risk will be quantified by these CRBs is often based on standards and assumptions that are not practical to the real-world scenario. This study models credit risk to discover more effective and practical measures which relate to the borrowers and their operating environment. Data was collected from annual default reports from the Central bank of Kenya, CRBs and major financial institutions over a period of three years (2018, 2019, and 2020). The study also used focus group discussions to establish the key default factors and their baseline values. A sample of 29 participants was drawn from the population of CRB staff members who undertake the core functions of credit risk determination. Using the system dynamic modeling and simulation approach, the study identified faithful representations of default risk measurements. First, descriptive analysis was conducted using tabled summaries and bar charts and results identified customer income, issued loans and collateral amount as the most influential factors for credit risk. Explorative analysis applied causal loop diagrams (CLDs). Simulation analysis was then conducted after generating stock-and-flow diagrams and three important variables were identified, i.e., loan repayment, performing loans, and credit risk. The information gained from this study will benefit the government, the Central bank of Kenya (CBK), research scholars and other major financial institutions around the country.

Anahtar Kelimeler

Credit risk, Default, Kenya, Simulation, System dynamics modeling

Destekleyen Kurum

KCA University

Teşekkür

The authors wish to thank KCA University and the Kenya Education Networks for funding this research.

Kaynakça

• [1] BCBS. Principles for the Management of Credit Risk. Consultative paper, The Basel Committee on Banking Supervision, 2001.
• [2] CBK. Bank Supervision Annual Report. Available at https://www.centralbank.go.ke/uploads/399346751_2015%20Annual%20Report.pdf, 2015.
• [3] CBK. Bank Supervision Annual Report. Available at https://www.centralbank.go.ke/uploads/banking_sector_annual_reports/1174296311_2018%20Annual%20Report.pdf, 2018.
• [4] Kansiime, MK, Tambo, JA, Mugambi, I, Bundi, M, Kara, A, Owuor, C. COVID-19 implications on household income and food security in Kenya and Uganda: Findings from a rapid assessment. World development. 2021, p. 137, 105199.
• [5] Akkoc, S. 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 2012; 222: 168-178.
• [6] Moradi, S, Rafiei, FM. A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks. Financial Innovation 2019; 5(1): 1-27.
• [7] Munene, RN, Nyamache, T, Nzioki, PM. An Assessment of the Role of Credit Reference Bureau in Influencing Risk Identification in Mitigating Credit Default in Commercial Banks in Kenya, 2018.
• [8] Brown, M, Jappelli, T, Pagano, M. Information sharing and credit: Firm-level evidence from transition countries. Journal of Financial Intermediation 2009; 18(2): 151-172.
• [9] Emel, A, Oral, M, Reisman, A, Yolalan, R. A credit scoring approach for the commercial banking sector. Socio-Economic Planning Sciences 2003; 37(2): 103-123.
• [10] Kruppa, J, Schwarz, AG, Ziegler, A. Customer credit risk: individual probability estimates using machine learning. Expert Systems with Applications 2013; 40(13): 5125-5131.
• [11] Yu, L, Wang, S, Lai, KK, Zhou, L. BioInspired Credit Risk Analysis. Berlin, GERMANY: Springer, 2008.
• [12] Zanin, M, Papo, D, Sousa, P. A, Menasalvas, E, Nicchi, A, Kubik, E, Boccaletti, S. Combining complex networks and data mining: why and how. Physics Reports 2016; 635: 1-44.
• [13] Rahim, FHA, Hawari, NN, Abidin, NZ. Supply and demand of rice in Malaysia: A system dynamics approach. International Journal of Supply Chain and Management 2017; 6(4): 234-240
• [14] Afriyie, HO, Akotey, JO. Credit risk management and profitability of selected rural banks in Ghana. Ghana: Catholic University College of Ghana, vol. 7(4), 2012, pp. 176-181.
• [15] Onaolapo, AR. Analysis of credit risk management efficiency in Nigeria commercial banking sector, (2004-2009). Far East Journal of Marketing and Management 2012; 2(4): 39-52.
• [16] Abdipoor, S, Nasseri, A, Akbarpour, M, Parsian, D, Zamani, S. Integrating neural network and colonial competitive algorithm: a new approach for predicting bankruptcy in Tehran security exchange. Asian Economic and Financial Review 2013; 3(11): 1528-1539.
• [17] Maji, SG, Hazarika, P. Factors affecting credit risk of Indian banks: Application of dynamic panel data model. Research Bulletin 2016; 42(1): 85-96.
• [18] Otieno, S, Nyagol, M, Onditi, A. Relationship between credit risk management and financial performance. Empirical evidence from microfinance banks in Kenya. Research Journal of Finance and Accounting 2016; 7(6): 2222-2847.
• [19] Kithinji, AM. Credit risk management and profitability of commercial banks in Kenya. School of Business, Nairobi, KENYA: University of Nairobi, 2010.
• [20] Kaminskyi, A. System Dynamics Modelling of Credit Risk Management. 2018.
• [21] Sterman, DJ. Business Dynamics: Systems Thinking for a Complex World. Boston, Mass. USA: Irwin/ McGraw-Hill, 2000.
• [22] Reuter, J. Values and Worldviews. Diagnosing and Engaging with Complex Environmental Problems. 2013, pp. 62-64.
• [23] Csikosova, A, Janoskova, M, Culkova, K. Limitation of financial health prediction in companies from post-communist countries. Journal of Risk and Financial Management 2019; 12: 13-15.
• [24] Petrică, AC, Stancu, S, Tindeche, A. Limitation of ARIMA models in financial and monetary economics. Theoretical & Applied Economics 2016 23(4).
• [25] Nasiurma, D. K. Survey Sampling: Theory and methods. 2010. Nairobi, Kenya. University of Nairobi.
• [26] Duong, NT, Huong, TTT. The analysis of major credit risk factors-The case of the Vietnamese commercial banks. International Journal of Financial Research 2017; 8: 33-42.
• [27] Belás, J, Smrcka, L, Gavurova, B, Dvorsky, J. The impact of social and economic factors in the credit risk management of SME. Technological and Economic Development of Economy 2018; 24(3): 1215-1230.
• [28] Frei, C. A New Approach to Risk Attribution and Its Application in Credit Risk Analysis. Risks 2020; 8(2): 60-65.
• [29] Ngumo, KOS, Collins, KW. David, SH., Determinants of financial performance of microfinance banks in Kenya. arXiv preprint arXiv:2010.12569, 2020.
• [30] Misati, RN, Kamau, A, Tiriongo, S, Were, M. Credit risk and private sector loan growth under interest rate controls in Kenya. African Review of Economics and Finance 2021; 13: 83-112.