The Impact of Egypt’s Debt Burden on Bank Credit Risk

66 Scientific Journal of Human and Machine Learning The Impact of Egypt’s Debt Burden on Bank Credit Risk Karim Farag Berlin School of Business and Innovation, Germany Karim.shehata@berlinsbi.com Rabia Luqman Berlin School of Business and Innovation, Germany Noah Mutai Berlin School of Business and Innovation, Germany Benjamin Bensam Berlin School of Business and Innovation, Germany Scientific Journal of Human and Machine Learning Introduction The January 25 Revolution caused Egypt’s GDP growth rate to decrease sharply to 1.8% in 2011–2012, according to a 2013 report from the Central Bank of Egypt (CBE). After this revolution, Egypt entered a new phase of political and economic transformation that led to the election of a new president and new policies for economic and political reform. As a result, Egypt’s economy observed some economic improvements during 2011-2018, reaching 5.6% GDP growth in 2018, but the COVID-19 epidemic slowed economic progress by 3.3% in 2021 (Farag et al., 2023). Keywords Credit Risk, Public and External Debt, and Macroeconomics in 2016, which caused the country’s inflation rate to rise from 11.06% in 2015 to 23.07% in 2016. In order to combat these inflationary pressures, the Central Bank of Egypt (CBE) raised the market interest rate, which came at the cost of a decline in the private sector investments, and the value of the Egyptian pound rose from 7.70 in 2015 to 17.80 in 2017. Source: Annual Report of the Central Bank of Egypt Abstract Egypt has seen a significant rise in its public and foreign debts throughout the past ten years, which has put an increasing financial strain on businesses and individuals and decreased living standards for the country’s residents. Additionally, the devaluation of the Egyptian pound placed more inflationary pressures; it raised the lending interest rate and reduced the contribution of the private sector to the growth of the economy, consequently leading to increased loan default rates for the banks, which jeopardized their solvency and market expansion due to increased credit risk exposure. Therefore, the study employed the Generalized Methods of Moments (GMM) to examine the impact of Egypt’s internal and external debt on bank non-performing loans during 2011-2023 by utilizing the retail and corporate non-performing loan ratios as proxies for bank credit risk. The results revealed that both debts significantly affect the risk of bank credit, and to reduce this risk, the government should reallocate funds from real estate and infrastructure projects to manufacturing and technology firms to boost economic growth. At the same time, new low-cost financing initiatives should be passed to incentivize the private sector to participate more in economic growth to achieve better financial and economic results. 67 Source: Annual Report of the Central Bank of Egypt According to the report of the Central Bank of Egypt (CBE) in 2017, Egypt sought a USD loan from the International Monetary Fund (IMF) to fund its projects and promote Egypt’s economic growth. The IMF agreed to this loan, subject to Egypt devaluing the Egyptian pound against foreign currencies. Egypt justified this condition by arguing that lower export prices will result from the devaluation of its currency, which will also increase export volume and draw more tourists to the country, boosting Egypt’s tourism sector. Ultimately, this will encourage more foreign investments in Egypt. In this respect, Egypt depreciated its currency According to the report of the CBE in 2023, Egypt’s external debt ballooned from 47.8 billion dollars in 2015 to 168 billion dollars in 2023 because of the USD loan from the IMF, putting further pressure on the value of the Egyptian pound, which eventually reached EGP 48 per USD. As a result, the rate of inflation rose sharply, hitting 33.7% in 2023. Egypt’s economic situation did not improve due to the devaluation; instead, the GDP growth rate in 2023 was 3.50%, which was consistent with a considerable rise in the country’s foreign debt. Egypt is currently experiencing dire economic conditions, a potential sign of impending credit and currency crises because of the financial and economic strains placed on individuals and businesses, which have reduced their standard of living and impaired their ability to repay debt. However, Egypt exerts much effort to attract Gulf investments to Egypt, such as UAE, KSA, and Qatar, to use 68 their hard currency to repay the IMF loan. Further, Egypt sells state-owned assets also to pay off part of its IMF loan. Therefore, this study aims to investigate the effect of Egypt’s debt and exchange rate on bank credit risks to clarify how Egypt’s debt burden and currency devaluation affect banks’ non-performing loans. Literature Review The studies of Louzis et al. (2012), Amit Gosh, (2015), and Naili & Lahrichi (2022) find that increases in a country’s debt raise the treasury securities, which reduces bank reserves and increases lending interest rates. As a result, additional financial pressure on businesses impairs their capacity to repay loans and eventually boosts the non-performing loan (NPL) percentage in banks. Moreover, in the Eurozone, Makri and Bellas (2014), and Ofria and Mucciardi (2022) find that public debt has a positive significant effect on non-performing loans, stating that treasury securities are always used by banks as secondary reserves in their portfolios to meet their liquidity needs, but during recessions, governments issue more treasury securities, forcing banks to rebalance their asset classes at the expense of lower funds or reserves allocated to the private sector. This raises lending interest rates, weakens the ability of businesses to repay the loans, and ultimately increases the credit risk level in banks. Further, Giammanco, et al. (2022) reveal that Asia’s public debt is positively related to the quality of bank portfolios, having more internal debts raises the credit risk exposure in banks. On the other hand, Kauko (2012) contends that an increase in the government’s external debt during the period of bank deregulation makes the problem of non-performing loans (NPLs) worse because it lowers the value of the Scientific Journal of Human and Machine Learning Scientific Journal of Human and Machine Learning domestic currency, which raises the cost of production in the private sector and weakens the ability of the borrowers to repay. This, in turn, increases exposure to credit risk and deteriorates bank solvency. In this regard, in 2018, the report of the Central Bank of Egypt revealed that Egypt issued excessive amounts of credit along with heavy indebtedness to other countries suggesting that Egypt’s banking industry is on the verge of a credit crisis that might endanger the survival of banks and their ability to thrive within their respective economies. Furthermore, According to Maltritz and Molchanov (2014) and Nikolaidou and Vogiazas (2017), a significant increase in foreign debt will cause greater volatility and swings in the home currency’s exchange rate, which will cause the currency to depreciate severely and thus raise the NPLs in banks. Moreover, Farag et al. (2023) claimed that the government always finances its investments with revenues from taxes, but when governments experience budget deficits due to higher expenditure than revenue, they are compelled to issue Treasury securities to close the budget gap, which raises the national debt. In other words, a more significant budget deficit will result in the issuance of more treasury securities, increasing the national debt size. However, in such a scenario, the government still owes citizens, and this debt can be paid for by increasing tax rates or printing more money. the macroeconomic variables on the credit risk of Egypt’s banks from 2011 to 2020. The non-performing loan (NPL) ratio was used as a proxy for credit risk. The findings demonstrate a negative relationship between GDP and NPL ratio because greater GDP is associated with improved living standards for people and businesses, improving their ability to repay loans, and lowering the NPL ratio in banks. Additionally, the results showed a significant association between the interest rate and the NPL ratio, meaning that rising interest rates translates into greater borrowing costs for borrowers, increasing their financial risk and, ultimately, driving up the NPL percentage in banks. Furthermore, the results indicate a significant relationship between the exchange rate and the non-performing loan (NPL) ratio, suggesting that rises in the foreign exchange rate aggravate the NPL ratio issue, particularly for nations that rely more heavily on imports than exports. Moreover, Farag et al. (2023) employ the GMM to examine the impact of macro- and microeconomic variables on the corporate and retail credit risk in banks of Egypt during 2011-2020, and the findings reveal that the corporate NPL ratio is more sensitive to the changes of the macro- and microeconomic variables compared to the retail ones which shows the importance of classifying the credit risk into retail and corporate to generate more accurate results and estimates. Shehata (2019) employs the regression to examine the impact of macroeconomics on the NPL ratios by conducting a comparison between the listed and non-listed banks of Egypt during 2010-2017, and the results find that the NPL ratio of the listed banks is more sensitive to the macroeconomic variables compared to the non-listed ones. Further, ElGaliy (2022) employs vector Autoregression (VAR) to study the impact of According to the analysis of the previous literature review section, the paper finds a very limited number of papers that have studied the effect of public and external debts on bank credit risk in terms of corporate and retail, clarifying and confirming the importance of this paper in providing better insights to the practitioners and academic scholars, which will eventually bridge gap in the literature of credit risk determinants and consequently 69 enhance the credit risk management performance to gather better financial and economic results and also to provide better comprehension to the regulators on how to manage their debts relative to the growth of the economies. Methodology The paper aims to study the impact of Egypt’s debt burden on bank credit risk by conducting an empirical study that selected 14 banks operating in Egypt out of 35 due to data availability covering the period of 2011-2023. The study uses the Generalized Methods of Moments (GMM) to test the hypotheses as it is more robust, unbiased, and reliable than other models and can also handle the endogeneity issue. Further, the paper selected the corporate, retail, total non-performing loan, and Capital Adequacy (CAR) ratios to be proxies for the credit risk in Egypt, while the national debt, external debt, exchange rate, interest rate, inflation rate, and economic growth rate as the independent variables. Accordingly, as shown below, the paper will create four models to estimate the future value of credit risk and provide better insights to academics, bankers, and policymakers on the intensity of the impact of debt on banks’ credit risk in Egypt. In addition, the results will be added to the literature database of credit risk determinants to better comprehend the relationship between debt and credit risk. 70 Scientific Journal of Human and Machine Learning Scientific Journal of Human and Machine Learning to total outstanding loans, which shows high exposure to credit risk that threatens banks’ survival and growth in the credit market. Table of Variables and Measurements Symbols Variables Measurements RNPL Retail non-performing loan ratio Retail impairment / tTotal retail loans CNPL Corporate non-performing loan ratio Corporate impairment / Total corporate loans TNPL Total non-performing loan ratio Total impairments / Total loans CAR Capital Adequacy Ratio Equity Tier 1 and 2 / Weighted average assets IDEBT Internal Debt Debt in % of GDP EDEBT External Debt Log of the external debt in dollars EXR Exchange rate EGP/USD INT Corporate Annual Weighted Interest Corporate Annual lending interest rate INF Inflation rate Headline inflation rate GDP Gross Domestic Product Real GDP growth rate Results and Data Analysis The paper described the collected data in terms of observation, mean, standard deviation, maximum, and minimum, as shown in Table (1), and the results show that the number of observations is 182 since it selected 14 banks covering the period of 2011-2023. Additionally, the mean of the CAR is 18%, which indicates that the banks of Egypt have, on average, 18% of equity to risk-weighted assets, and it meets the capital required ratio of the CBE of 12.5%. Furthermore, this percentage is substantially beyond the required ratio, indicating that the The IDEBT’s mean of 88.72% shows that Egypt has a large debt burden in relation to GDP growth, reaching a maximum of 103% of its GDP. Egypt’s external debt has increased significantly over the past ten years, from $33.70 billion in 2011 to $168 billion in 2023, when compared to the IDEBT. This significant increase in external debt suggests that Egypt is more vulnerable to default, particularly when its sources of USD income are unstable. Furthermore, it puts further strain on economic expansion, devalues currencies, and lowers credit ratings, discouraging investors from taking capital flights to Egypt. Consequently, the exchange rate of the EGP/USD has notably devaluated 71 from EGP 5.9 in 2011 to EGP 48 per dollar in 2024. In addition, the rate of inflation grew dramatically from 4.66% in 2011 to 33.70% in 2023. In order to stop this inflation, the CBE increased lending rates in the credit market, reaching 19.80%. This discouraged privatesector lending and led to a decline in GDP, which reached 3.50% in 2022 as opposed to 6.6% in 2022. Furthermore, when compared to other macroeconomic variables, the standard deviation of the inflation rate and exchange rate has the highest STDEV, indicating greater instability in Egypt’s economic conditions due to the country’s high levels of price volatility and currency volatility. In this respect, such an increase in public and external debts, along with spikes in credit risk, would exacerbate the problem leading to a very near credit crisis in Egypt. Table (1) Descriptive Statistics Obs. MEAN STDEV MAX MIN CAR 182 18.00% 5.39% 44.80% 8.62% CNPL 182 6.17% 7.62% 73.50% 0.01% banks expect high credit risk exposure in the near future. RNPL 182 2.71% 2.98% 24.27% 0.00% TNPL 182 7.31% 10.53% 56.85% 0.30% The average CNPL and RNPL are 6.17% and 2.71%, respectively, indicating that, in comparison to retail credit risk, corporate credit risk bears a larger share of the overall credit risk. Further, the CNPL’s standard deviation is 7.62% while the RNPL’s is 2.98%, indicating that the credit risk associated with corporations is more volatile than that of the retail sector. Moreover, the mean of the TNPL is 7.31%, which means that the banks of Egypt have an average of 7.31% of NPLs IDEBT 182 88.72% 6.59% 103.00% 76.20% EDEBT 182 1.889982 0.244741 2.225309 1.52763 EXR 182 14.09 7.49% 30.94 5.9 INT 182 13.75% 3.36% 19.80% 9.50% INF 182 13.66% 8.41% 33.70% 4.66% GDP 182 3.83% 1.37% 6.60% 1.80% 72 GMM Results Using CNPL, RNPL, TNPL, and CAR as its four proxies for credit risk, the study develops four models to identify the variables that influence credit risk and project how the risk will move in the future. Before doing so, the unit-root test is used to determine whether the data is stationary. It discovers that some of the variables are non-stationary; as a result, it applies the first lag and discovers that all the variables become stationary. Additionally, in Table 2, the Arellano-Bond autocorrelation test found that both P-values are greater than 0.05, indicating no evidence of first-order serial correlation in the first differenced error. Additionally, the Sargan-Hansen tests have P-values greater than 0.05, which indicates that the instruments are uncorrelated with the error term and can handle the endogeneity issue. Accepting this null hypothesis implies that the instruments are valid and that the overidentifying restrictions are not violated. Therefore, the results increase the robustness of the four models. Table (2) shows the results of the GMM for the CNPL and RNPL; GMM findings found that EDEBT, EXR, and INF are statistically significant and can affect the CNPL value, while the IDEBT, INT, and GDP are insignificant. In other words, the external debt has a significant negative effect on the CNPL, claiming that a dollar increase in EDEBT reduces the CNPL by -1.229675. These results are supported by the findings of Vogiazas & Nikolaidou (2011). The paper justifies such results by stating that an increase in external debt places more devaluation on the Egyptian pound, leading to more inflationary pressures, enforcing the CBE to raise the interest rates substantially to combat such challenges and consequently lowering the demand for loans Scientific Journal of Human and Machine Learning by the private sector reducing the CNPL in the banks. Moreover, there is a positive association between the exchange rate and CNPL, stating that a one percent increase in the exchange rate results in a 0.097709 increase in CNPL, which is supported by Castro (2013), Laxmi Koju & Wang (2018), Kjosevski, et al. (2019), and Gulati et al. (2019). This suggests that more devaluation of the EGP raises CNPL, as increased pressure on production costs weakens corporate net worth and increases loan default rates. Additionally, the results find that the INF has a negative significant effect on the CNPL, which is consistent with the results of Poudel (2013), Olson & Zoubi (2014), Koju & Wang (2018), Farooq et al. (2019), Naili & Lahrichi (2022) that argue that notable increases in the inflation rate force banks to raise their interest rates substantially, reducing the demand on loans and lowering the CNPL and production in the private sector. As a result, such increases in EDEBT, EXT, and INF will worsen bank financial problems and diminish their ability to act as financial intermediaries. As a result, this will reduce economic development and put bank assets at risk of insolvency, which would plunge Egypt into a serious credit crisis. On the other side, IDEBT is insignificant and is not supported by the results of Gosh (2015) and Naili & Lahrichi (2022); they argue that public debt positively impacts credit risk because rising public debt means governments are issuing more treasury securities, which lowers bank reserves, raises lending interest rates placing more financial pressure on companies, weakens their ability to repay debt, and ultimately increases the corporate NPL ratio in banks. Table 2 also shows the GMM results for RNPL, which show that the IDEBT, INT, and GDP have a significant negative effect on Scientific Journal of Human and Machine Learning 73 the RNPL. The results of the IDEBT and INT are not supported by the findings of Gosh (2015) and Naili & Lahrichi (2022), stating that increases in public debt raise the retail credit risk because more IDEBT means lower bank reserves and higher interest rates raise the costs of loans and weakening the financial positions of the individuals and thus having higher RNPL. However, the justification behind such a negative relationship between the IDEBT and RNPL is that rises in Egypt’s state debt will cause bank reserves to decline, lending interest rates to rise, and a decline in retail loan demand, which will lead to a decrease in RNPL. In this respect, increases in IDEBT will lower the RNPL by -2.925600. Furthermore, the GDP has a negative effect on the RNPL, which is consistent with the results of Castro (2013), Gosh (2015), Chaibi & Ftiti (2015), Mpofu & Nikolaidou (2018), and Naili & Lahrichi (2022) suggesting that rising GDP is a sign of improving people’s financial health, which enhances their ability to make payments and lowers the retail NPL ratio in banks. On the other hand, EDEBT, EXR, and INF are statistically insignificant, and their hypotheses are rejected, showing that their variations cannot affect the value of the RNPL. Table (2) GMM Results of CNPL and RNPL Variables Estimate (CNPL) P-value Estimate MAX (RNPL) P-value 18.00% 5.39% 44.80% IDEBT -1.780934 0.1026488 -2.925600 0.0179887 EDEBT -1.229675 0.0146390 -0.543162 0.4050615 EXR 0.097709 0.0014764 0.112332 2.234e-06 INT -0.026333 0.3234394 -0.105914 0.0053576 INF -0.017813 0.0823696 0.022870 0.3249892 GDP -0.098613 0.2648083 -0.313116 0.0005109 Arellano-Bond test 0.66749 0.45162 Sargan-Hansen test 0.4169 0.79492 74 Scientific Journal of Human and Machine Learning Table 3 shows the findings of the TNPL and CAR. The inflation rate is statistically significant and has a positive association with the TNPL, supported by the results of (Zribi & Boujelbène, 2011), Thiagarajan, et al. (2011), Koju et al. (2018), Kjosevski et al (2019), Trung (2021) and Alnabulsi, et al. (2022), arguing that increases in inflation weaken and deteriorate people’s ability to repay loans, which in turn raises the retail NPL ratio in banks, while the IDEBT, EDEBT, EXR, INT, and GDP are insignificant. In this regard, the results of the TNPL compared to the CNPL and RNPL confirm the significance of separating and classifying credit risk into corporate and retail to provide more accurate and reliable estimates for the factors influencing credit risk in banks. Moreover, the results of CAR find that the GDP has a negative significant effect on the CAR, claiming that when the GDP grows, banks anticipate a lower exposure to credit risk; this results in a decrease in the amount of equity needed to absorb losses, which lowers the CAR. Table (3) GMM Results of TNPL and CAR Variables Estimate (TNPL) P-value Estimate MAX (CAR) P-value 18.00% 5.39% 44.80% IDEBT 0.4627813 0.604611 -1.8332779 4.038e-10 EDEBT 0.4584147 0.285817 -0.2433459 0.1933846 EXR -0.0146848 0.420253 0.0473045 2.596e-15 INT 0.0260654 0.115479 0.0033775 0.6348628 INF -0.0198364 0.005404 0.0004184 0.7957667 GDP -0.0055704 0.917543 -0.0681150 0.0006529 Arellano-Bond test 0.50724 0.056662 Sargan-Hansen test 0.6671 0.68739 Scientific Journal of Human and Machine Learning 75 Conclusion responsibilities in financial intermediation. The GMM results indicate that both external and internal debt significantly impacts the corporate and retail non-performing loan ratios. In other words, most countries exhibit a positive relationship between credit risk and internal and external debt, meaning that banks are more exposed to credit risk when they have more debt. However, in Egypt, it turns out that rising levels of both internal and external debt lessen exposure to credit risk. However, this is not a good sign, as the fall in NPL ratios was mostly caused by a reduction in the quantity of loans extended to the private sector. In this respect, increasing debt accumulation occurs at the expense of the overall decline of private-sector production in anticipation of an impending recession. Therefore, if Egypt’s debt burden continued to rise, this would reduce its production to the point of forcing Egypt into a severe recession, jeopardizing its capacity to remain solvent and expand economically. A major devaluation of the Egyptian currency relative to other currencies, with a rise in lending interest rates in the credit market, has led to a decrease in private sector investments and production, decelerating the economy’s growth. In light of this, the paper makes the following recommendations for the government: it should gradually cut back on spending on real estate and infrastructure projects, use the money borrowed to improve secondary and higher education systems, and encourage the private sector to produce and export goods overseas to secure some reliable sources of hard currencies that can strengthen the value of the EGP relative to the USD. It also suggests that the CBE launch innovative, low-cost financing programs for businesses in the private sector that can raise Egypt’s manufacturing, export, and technology capabilities. Additionally, the foreign debt, inflation rate, and currency rate have the most significant effect on the corporate NPL ratio, whereas the GDP, interest rate, and internal debt have a significant impact on the retail NPL ratio. In this manner, the bankers can utilize these findings to project the future values of CNPL and RNPL. On the one hand, a decrease in the value of the EGP in relation to the USD would raise the CNPL level, whilst an increase in the INF and EDEBT would lower it. Conversely, an increase in IDEBT, INT, and GDP would result in a decrease in the RNPL ratio inside Egypt’s banks. As a result, the bankers will thus be better able to predict how retail and corporate credit risk will move in the future, which will improve their ability to evaluate credit risk and manage it to produce better financial and economic outcomes, strengthening their It also suggests pegging the currency rate as soon as possible to improve Egypt’s foreign direct investment and stabilize the country’s economic conditions by luring greater capital outflows from other countries. Additionally, it is advisable to encourage leasing out some state-owned land and real estate for a usufruct to secure a stable source of hard currency, avoiding selling them to safeguard public properties. Moreover, companies should be stimulated to improve their R&D divisions by giving them additional funding to produce innovative goods that can compete globally and raise Egypt’s export level. In this respect, the government should be careful while managing its debts and look for securing many different sources of foreign income to stabilize the value of the Egyptian currency and to enhance the country’s solvency levels to have more financial health conditions in support of the growth of the economies. 76 References Thiagarajan, S., Ayyappan, S. & Ramachandran, A., 2011. Credit Risk Determinants of Public and Private Sector Banks in India. European Journal of Economics, Finance and Administrative Sciences, 34(34), pp. 147-153. Vogiazas, S. D. & Nikolaidou, E., 2011. Investigating the determinants of nonperforming loans in the Romanian banking system: An empirical study with reference to the Greek crisis.. Economics Research International. Zribi, N. & Boujelbène, Y., 2011. The factors influencing bank credit risk: The case of Tunisia. Journal of Accounting and Taxation, 3(4), pp. 70-78. Louzis, D. P., Vouldis, A. T. & Metaxas, V. L., 2012. Macroeconomic and bank-specific determinants of non-performing loans in Greece: A comparative study of mortgage, business and consumer loan portfolios. Journal of Banking & Finance, 36(4), pp. 1012-1027. Kauko, K., 2012. External deficits and non-performing loans in the recent financial crisis. Economics Letters, 115(2), pp. 196-199. Poudel, R. P. S., 2013. Macroeconomic determinants of credit risk in Nepalese banking industry. s.l., In Proceedings of 21st International Business Research Conference. Castro, V., 2013. Macroeconomic determinants of the credit risk in the banking system: The case of the GIPSI. Economic Modelling, Volume 31, pp. 672-683. Maltritz, D. & Molchanov, A., 2014. Country credit risk determinants with model uncertainty. International Review of Economics & Finance, Volume 29, pp. 224-234. Nikolaidou, E. & Vogiazas, S. D., 2014. Credit Risk Determinants for the Bulgarian Banking system. International Advances in Economic Research, Volume 20, pp. 87-102. Makri, V., Tsagkanos, A. and Bellas, A., 2014. Determinants of non-performing loans: The case of Eurozone. Panoeconomicus, 61(2), pp.193-206. Olson, D. & Zoubi, T. A., 2014. The determinants of loan Scientific Journal of Human and Machine Learning Scientific Journal of Human and Machine Learning loss and allowances for MENA banks Simultaneous equation and three-stage approaches. Journal of Islamic Accounting and Business Research, 5(1), pp. 98-120. Shehata, K. M. F., 2019. The Macroeconomic Determinants of The Non-Performing Loans: Comparative Research Between Non-Listed & Listed Commercial Banks Of The Stock Exchange Of Egypt. Ghosh, A., 2015. Banking-industry specific and regional economic determinants of non-performing loans: Evidence from US states. Journal of financial stability, Volume 20, pp. 93-104. Wang, R. & Luo, H. R., 2020. Oil prices and bank credit risk in MENA countries after the 2008 financial crisis. International Journal of Islamic and Middle Eastern Finance and Management, 13(2), pp. 219-247. Chaibi, H. & Ftiti, Z., 2015. Credit risk determinants: Evidence from a cross-country study. Research in International Business and Finance, Volume 33, pp. 1-16. Trung, N. K. Q., 2021. The relationship between internal control and credit risk – The case of commercial banks in Vietnam. Cogent Business & Management, 8(1), p. 1908760. Nikolaidou, E. & Vogiazas, S., 2017. Credit risk determinants in Sub-Saharan banking systems: Evidence from five countries and lessons learnt from Central East and South East European countries. Review of Development Finance, 7(1), pp. 52-63. Ofria, F. & Mucciardi, M., 2022. Government failures and non-performing loans in European countries: a spatial approach. Journal of Economic Studies, 49(5), pp. 876-887. Koju, L., Abbas, G. & Wang, S., 2018. Do macroeconomic determinants of non-performing loans vary with the income levels of countries? Journal of Systems Science and Information, 6(6), pp. 512-531. Wan, J., 2018. Non-performing loans and housing prices in China. International Review of Economics and Finance, Volume 57, pp. 26-42. Laxmi Koju, G. A. & Wang, S., 2018. Do Macroeconomic Determinants of Non-Performing Loans Vary with the Income Levels of Countries? Journal of Systems Science and Information, 6(6), pp. 512-531. Mpofu, T. R. & Nikolaidou, E., 2018. Determinants of credit risk in the banking system in Sub-Saharan Africa. Review of Development Finance, 8(2), pp. 141-153. Gulati, R., Goswami, A. & Kumar, S., 2019. What drives credit risk in the Indian banking industry? An empirical investigation. Economic Systems, 43(1), pp. 42-62. Kjosevski, J., Petkovski, M. & Naumovska, E., 2019. Bank-specific and macroeconomic determinants of non-performing loans in the Republic of Macedonia: Comparative analysis of enterprise and household NPLs. Economic research-Ekonomska istraživanja, 32(1), pp. 1185-1203. Alnabulsi, K., Kozarević, E. & Hakimi, A., 2022. Assessing the determinants of non-performing loans under financial crisis and health crisis: evidence from the MENA banks. Cogent Economics & Finance, 10(1), p. 2124665. Naili, M. & Lahrichi, Y., 2022. Banks’ credit risk, systematic determinants and specific factors: recent evidence from emerging markets. Heliyon, 8(2), p. e08960. ElGaliy, N., 2022. Macroeconomic Shocks and Credit Risk Stress Testing: Evidence from The Egyptian Banking Sector. p. 60. Giammanco, M. D., Gitto, L. & Ofria, F., 2022. Government failures and non-performing loans in Asian countries. Journal of Economic Studies. Farag, K., Kassem, T. and Ramzy, Y., 2023. The Crucial Macroeconomic and Microeconomic Determinants of Retail and Corporate Credit Risks. International Journal of Finance, Insurance and Risk Management, 13(2), pp.30-41. 77