Determinants of credit risk of Indonesian Sharīʿah rural banks

Introduction

Credit risk, which is often represented by nonperforming loans (NPLs) for conventional banks, is an important component in economic stability. According to Endut et al. (2013), NPLs were related to the Asian financial crisis of 2007 and the collapse of financial markets. As a result, NPLs became an important issue and continue to represent a challenge that financial institutions have to manage (Endut et al., 2013). Islamic banks also face the issue of credit risk, in particular, that of nonperforming financing (NPF). According to Al-Wesabi and Ahmad (2013), bad management of credit risk is the cause of failure of three quarters of Islamic banks. One of the reasons is due to limited knowledge about the dynamics of credit risk. Therefore, an adequate understanding of credit risk will lead to a more stable financial system (Adebola et al., 2011).

In Indonesia, credit risk in Islamic banks must be given special attention because the banks face a high level of NPF. Bank Indonesia (the central bank of Indonesia) requires the maximum level of NPL for the banking industry to be 5% (FSA, 2013). However, based on statistics issued by the Financial Services Authority (FSA), the NPF of Sharīʿah rural banks (SRBs) stood at 8.28% as at October 2019. SRBs are Islamic banks which provide services especially to the micro sector. There are 57.89 million business units in Indonesia, and the majority of them (99.9%) are micro, small and medium enterprises (MSMEs) (FSA, 2019). Many MSMEs do not have access to banking services (KNKS, 2019). The existence of SRBs is expected to strengthen the economic activity of the society, especially in rural areas where MSMEs face many difficulties in accessing business capital (Amelia and Fauziah, 2017). The FSA of Indonesia (2019) released the financing distribution and NPF ratio of SRBs.

The data show that the trend of total financing from 2010 to 2019 has increased and that this has been accompanied by an increase in NPF. The NPF of SRBs varied over the period 2010–2019, but all data show that NPF has been above 5%, which means that it exceeded the requirements of Bank Indonesia. However, from mid-2018 to the end of 2019, NPF appears to be under control. The trend of NPF in SRBs certainly raises the question of the extent to which these banks manage their financing risks.

NPL and NPF can be affected by several factors, both internal and external. Previous studies enumerated the internal factors influencing credit risk in the banking industry, specifically NPLs, such as financial ratio and bank characteristics (Suryanto, 2015; Effendi et al., 2017) and external factors such as interest rate, inflation rate and gross domestic product (GDP) (Adebola et al., 2011; Endut et al., 2013; Widarjono and Rudatin, 2021). Many studies also examined the internal and external factors influencing NPF, with varying results among studies (Firmansyah, 2014; Havidz and Setiawan, 2015; Supriani and Sudarsono, 2018).

The objective of this study is to analyze the internal and external factors that influence the credit risk of SRBs in Indonesia. Internal factors used in this study are capital adequacy ratio (CAR), financing to deposit ratio (FDR), return on assets (ROA), operating expense ratio (OER), financing to value (FTV) and profit and loss sharing (PLS) financing ratio. Meanwhile, the external factors consist of inflation, economic growth and interest rate.

The study of credit risk in the banking industry is important for the following reasons. First, credit risk is one of the criteria used to measure the financial performance of banks. Thus, high levels of NPLs at banks will have a negative impact on the banks’ income and the sustainability of their businesses (Haniifah, 2015). Second, high NPL ratios not only affect individual banks but also have an impact on the economic stability of a country. According to Rahman et al. (2017), bad management of NPLs “will lead to banking failure and countrywide financial vulnerability” (p. 181).

The study can be divided into several sections: the second section discusses the literature review on SRBs and credit risk; the third section presents the research methodology; the fourth section presents the data analysis and the findings; and finally, the fifth section concludes the paper and discusses the implications of the study.

Literature review

Research methodology

The study uses annual reports as secondary data of SRBs in Indonesia for the years 2010–2019. The annual reports were accessed from the banks’ websites. The study uses the analysis method of auto regressive distributed lag (ARDL). In the ARDL method, variables with different integration orders (level and first difference) can be used. The ARDL method can also assign the direction of the causality of the variables used in the model. One of the advantages of using ARDL is that this model can estimate the short-run and long-run effects of the variables simultaneously (Sukmana and Setianto, 2018).

Data analyses of this study include descriptive statistics, unit root test (using Augmented Dickey-Fuller (ADF) test and Philips-Perron (PP) test), model estimation in the short-run and long-run, lag determination, cointegration test, diagnostic test and stability test. The ARDL model of this study is as follows:

NPF = Nonperforming financing (per cent);

INF = Inflation using Consumer Price Index (CPI) (per cent);

GDP = Economic growth using Industrial Production Index (IPI) (per cent);

IRate = Interest rate (per cent);

CAR = Capital adequacy ratio (per cent);

FDR = Financing deposits ratio (per cent);

ROA = Return on assets (per cent);

OER = Operating expense ratio (per cent);

FTV = Financing to value (dummy), where 0 = before the policy and 1 = after the policy;

PLS = Profit and loss sharing financing ratio (per cent); and

e = Error term.

Results and discussion

The sample in this research consists of SRBs which are studied over the period January 2010–September 2019. There were 165 SRBs in Indonesia in 2019. Based on the availability of data, this study analyses 164 SRBs (99.4%) using monthly data. The results of the descriptive statistics are described in Table 1.

Table 1 shows that NPF has an average value of 8.59, standard deviation is 1.57, maximum value is 11.80 and minimum value is 6.15. The value of NPF in SRBs is found to be high during the observation period. Even the minimum value of NPF exceeded the expected limit of 5% instituted under Bank Indonesia’s NPF policy.

According to the findings, the NPF level fluctuated during 2010–2019. The highest NPF is 11.80 as at Q2–2018, and the lowest NPF is 6.15 as at Q1–2013. These high NPFs should be managed properly so as not to decrease the profits obtained by the bank.

Stationary testing is carried out to determine which data is integrated in the same or different orders. The results of stationary testing show that there are several variables at the stationary level, whereas other variables are stationary at the first difference level or not integrated in the same order. The stationary test uses an Augmented Dickey–Fuller (ADF) test and Philips–Perron (PP) test.

Table 2 shows that NPF, INF, GDP, IRate, FDR, OER and FTV have ADF probability value greater than the alpha value of 10%. It means that the data is not stationary at the level. Furthermore, the stationary test for the first difference was carried out, and the results showed that the ADF probability values for all variables were smaller than the alpha value of 1%, which indicates that the data are stationary in the first difference. Meanwhile the PP test at the level shows that NPF, INF, IRate, ROA, OER, FTV and PLS values are greater than the alpha value of 10%, meaning that the data are not stationary at the level. The PP test on the first difference shows that all variables are stationary at the alpha value of 1%.

The results of stationarity testing show that there is no integration between the variables so that the selection of the ARDL method is the right method for examining the relationship between NPF and macro-microeconomic conditions that affect SRBs.

In the ARDL regression, an estimation of the relationship between the dependent and independent variables is carried out. Based on the estimation results, an error correction mechanism (ECM) regression is carried out to balance the short-term economic relations with variables that have a long-term balance or economic relationship. The estimation results for the short run are described in Table 3, whereas Table 4 presents the long-run results.

Table 3 shows that the NPF variable in lag 1 negatively affects the following month’s NPF. This means that every 1% increase in NPF in the previous month will decrease the NPF of the current month by 0.24% at 1% significance level. This indicates that the NPF decrease is contributed by the previous month’s NPF.

Inflation negatively influences NPF in the short run at a 1% significance level. It means that an increase in inflation of 1% will reduce NPF by 0.24%. This implies that higher inflation rates will result in lower NPF. The result is consistent with the study by Effendi et al. (2017) and Supriani and Sudarsono (2018). High prices of goods and services because of inflation may reduce the intention of the society to save. Therefore, Islamic banks may have lower third party funds and will thus be more careful in giving out financing (Widiastuty, 2017); consequently, Islamic banks will have lower NPF too.

Otherwise, INF(−1) and INF(−3) had a significant positive effect at the 1% and 5% significance levels. If there is a 1% increase in inflation, NPF will increase by 0.35% in the first month and 0.30% in the third month. The positive effect is in line with the theory that when there is inflation, the high prices of goods will reduce people’s purchasing power if their income is constant and affect the ability of customers to pay off their financing. Widarjono and Rudatin (2021) explain that inflation can worsen economic conditions by decreasing the purchasing power of consumers, therefore increasing “poor” financing.

CAR does not have an effect on NPF in the short run. This means that the size of the capital owned by Islamic banks does not affect NPF. Capital owned by banks is not always channeled in the form of financing or to cover losses resulting from financing risks. Banks may instead use the capital to invest (Suryanto, 2015). This study supports the results obtained by Havidz and Setiawan (2015) and Suryanto (2015).

However, different results can be observed for CAR(−1), CAR(−2) and CAR(−3) that show a significant negative effect on NPF. This means that if there is an increase in CAR by 1%, it will reduce NPF by 0.117% in the first month, 0.096% in the second month and 0.118% in the third month. A higher CAR means that SRBs have to maintain a larger amount of reserve funds, thus limiting their ability to provide financing in the long term and minimizing their credit risk as well. This result supports the finding of Muhammad et al. (2020) that high CAR can be used to absorb losses and thus minimize financing risk.

PLS has a significant positive effect on NPF in the short run. According to Ozili (2017), the more involved the banking industry is in its role as a financial intermediary, the higher will the risk of NPLs be. The positive relationship between NPF and PLS indicates that SRBs are not successfully mitigating the risk of PLS financing as doing so may involve large monitoring costs. This raises the issue of instability of the Islamic banking system (Fatoni and Sidiq, 2019).

In addition, with a coefficient of 0.696%, PLS(−1) has a significant negative effect. With monthly data, PLS(−1) is interpreted as the lag of the PLS variable in the first month. This means that an increase in PLS financing at lag 1 (in the previous month) will reduce NPF in the current month by 0.696% at the 1% significance level.

The use of the cointegration analysis method aims to analyze the long-term relationship between the explanatory variables and the dependent variable, especially in models containing non-stationary variables. This study uses the cointegration test method from Pesaran et al. (1996), namely the bound test. If the statistical F-value is greater than the first difference value at the 5% significance level, then the variables have a long-term cointegration and pass the test (Table 5). From the results of the bound test, it is found that the F-statistical value (2.786) is greater than the F-critical value (2.08) at the 5% significance level, meaning that the variables in this study have long-term cointegration.

Model testing is carried out to ensure the suitability of the model by conducting diagnostic and stability tests. Diagnostic tests are performed by testing the correlation test and heteroscedasticity test. Table 6 indicates that the probability values of autocorrelation and heteroscedasticity test are greater than 0.05%, meaning that the model is free from autocorrelation and heteroscedasticity problems.

A stability test is conducted to see the stability of the model by using the CUSUM test. The result of the CUSUM test shows that the CUSUM line is on the line of the 5% significance which means that the model is stable.

Discussion of long run results

The statistical analysis of each variable in the long run shows the variation result. It is found that inflation does not influence financing risk. The data shows that in the long run, inflation declined in the period 2013–2016, i.e. inflation was high in 2013 and 2014 but then went down in 2015 and 2016. This means that inflation may not have a significant impact on the activities of Indonesians, including the financing activities of SRBs.

This study finds that economic growth does not have an influence on NPF in SRBs in the long run. According to Firmansyah (2014), a high or low GDP will influence the ability of people to repay their obligations and thus will have an impact on NPF. Nonetheless, the bank’s financing approval process, which examines the capability and capacity of customers to make repayments, will also influence smooth installment repayments. Therefore, economic conditions may not influence the bank’s returns from financing if borrowers with the capacity to repay are selected. Hasna et al. (2019) state that when inflation is rising, it will impact on the rising yield of State Sharia Securities (SBSN) and thus, the financing of Islamic banks may be directed to the financing of SBSN.

In addition, the financing of SRBs was dominated by SMEs, which have an important role in supporting the economy. The economic crisis of 1998 shows that SMEs can survive crises better than larger companies (KNKS, 2019). However, high levels of NPF can occur because of mismanagement of financing distribution to economic sectors that are not booming, so that it does not affect economic growth.

This study finds that interest rate negatively influences NPF in the long term. When the bank rate increases, conventional banks tend to increase their deposit interest rates as well as lending rates to maintain their profitability. This can encourage third-party funds to move to conventional banks rather than to Islamic banks/SRBs, if profit rates shared by Islamic banks/SRBs are not raised accordingly. This may lead SRBs to decrease their financing, thus resulting in a lower NPF.

This result is different from that of Adebola et al. (2011), who state that interest rate has a significant positive effect over the long term, whereas Haniifah (2015) shows no significant positive effect between interest rate and NPF. SRBs do not apply interest rates in their operational activities. However, there is competition between conventional and Islamic banks (Trinugroho et al., 2021). When interest rates rise, SRBs tend to reduce expansion in the distribution of financing because the cost of third party funds becomes expensive to compensate for conventional banks. SRBs are more interested in placing their funds in SBSN which provide good returns compared to channeling funds in the form of financing. SBSN use a yield or equivalent rate. When SBSN yields increase, financing in SBSN by Islamic banks also increases (Hasna et al., 2019). SRBs can reduce the risk of financing when interest rates increase by investing in SBSN, and therefore, the small financing distribution will impact on a smaller NPF.

CAR has a significant and positive relationship with NPF in the long run. SRBs’ financing from 2010 to 2019 was mostly disbursed in the form of productive loans (working capital and investment financing) which reached 60% of total financing (FSA, 2020). The placement of risk-weighted assets in SRBs is high. High NPF rates on commercial loan financing require high CAR reserves. Effendi et al. (2017) and Supriani and Sudarsono (2018) also show a significant positive influence between CAR and NPF in Indonesian Islamic banks. CAR is a form of SRBs’ capital capacity. SRBs management may feel confident when there is an improvement of their CAR and attempt to increase the volume of financing without necessarily considering the repayment ability of customers. Consequently, it increases the potential for NPF to increase because of less optimal screening and monitoring processes for prospective customers.

Table 4 also shows that FDR does not have an influence on the NPF of SRBs in the long run. When financing is managed properly by assessing each customer’s risk level, a large or small amount of financing will not influence NPF. This result is in line with Havidz and Setiawan (2015), Dwihandayani (2016) and Muhammad et al. (2020). Furthermore, as noted earlier, the NPF for SMEs’ financing is higher than for non-SMEs although total financing provided to non-SMEs is higher than that to SMEs. Hence, it is important for SRBs to manage the level of financing disbursed for both SMEs and non-SMEs to minimize the NPF level. According to Havidz and Setiawan (2015), the influence of FDR is not significant because financing risks are rather influenced by portfolio management financing, i.e. good or bad management. Muhammad et al. (2020) state that there are two possibilities when FDR has no effect on NPF. First, SRBs get profit not only from financing, but also investment in SBSN or the financial market. Second, SRBs provide more low-risk financing (such as murābaḥah) than PLS financing.

The statistical result shows that profitability or ROA has a significant positive influence on NPF in the long run. Profitability is very important to attract investors to invest their funds in the company. A high level of financing that is distributed is expected to result in a high profit, although the possibility of having a high percentage of NPLs as a consequence can occur. SRBs face competition not only with conventional banks but also with Islamic commercial banks, and it may encourage them to increase their level of financing to generate higher profits, and this may result in high NPF also. The result is in accordance with the findings of Setiawan and Putri (2013).

OER has been found to have no effect on NPF, which indicates that the efficiency or inefficiency of SRBs does not influence NPF in the long run. This can be explained in two ways. First, according to data from FSA (2019), murābaḥah financing of SRBs dominated their financing distribution by 75.6%. The profit from murābaḥah financing can be calculated with certainty based on the value of the margin on the price of the goods. Therefore, SRBs can rely on the performance of murābaḥah financing in determining a high or low NPF and may not look at the efficiency of SRBs’ activities. Second, the management of SRBs may not prioritise the reduction of NPF levels. The efficiency of Islamic banks is lower than conventional banks (Chamberlain and Khokhar, 2020; Trinugroho et al., 2021). Therefore, NPF settlement in Islamic banks can be done through litigation and non-litigation processes. Litigation resolution will be more expensive than non-litigation because it requires more resources and time. If OER is unable to significantly reduce the NPF level, management tends to seek non-litigation solutions that are less costly but have a risk of a protracted settlement.

However, banks need to evaluate their activities that can cause inefficiency because it will influence the banks’ performance. Suryanto (2015) explains that bank efficiency is related to the quality level of management and the effectiveness of the products and services offered. Better quality management will enhance efficiency in the operational activities of the bank.

This paper finds that FTV does not affect NPF in the long term. FTV is related to contracts of sale and purchase such as murābaḥah, istiṣnāʿ, mushārakah mutanāqiṣah and ijārah muntahiyah bittamlik. Sutanto (2012) noted that the purpose of the LTV policy is to relocate between consumption and investment loans to ensure a balance between the two types of financing. In September 2019, murābaḥah financing distributed by SRBs amounted to 75.6% of total financing, followed by mushārakah (9.62%) and multi-purpose financing (9.57%) (KNKS, 2019). However, the composition of financing based on types of usage consisted of working capital (37.75%), investment (14.93%) and consumption (47.32%). Financing for consumption purposes still dominated the financing of SRBs. It may cause FTV not to influence NPF in these banks.

In the long run, PLS has a negative effect on NPF. It means that an increase in PLS will result in a lower NPF. There are several possible reasons why that happened: first, SRB financing is dominated by non-PLS financing, so there is a possibility that NPF will occur because of a problem in non-PLS financing. Data as at September 2019 shows that SRB financing is dominated by murābaḥah financing (75.6%), whereas the NPF level continues to increase. So when PLS is low but NPF is high, NPF may arise from the higher portion of non-PLS financing. This is in accordance with the argument by Widarjono and Rudatin (2021) that the increase in NPF can be influenced by the distribution of concentrated financing in Islamic banks. Second, generally, risk of PLS financing is higher than other types of financing. When SRBs have higher PLS financing, the management may apply high risk mitigation to prevent NPF, and therefore, it will impact on lower NPF.

Conclusion

Managing financing risks at the level of the Islamic banking industry is important because it is one of the key financial performance measurements. NPF must be managed well to maintain the sustainability of Islamic banks. This study attempts to examine the influence of internal and external factors on NPF in SRBs. Internal variables examined are: FDR, CAR, ROA, OER, FTV and PLS financing ratio. External variables comprise inflation, economic growth and interest rate.

The findings indicate that there are four variables that have a lag in the short run, namely, NPF, inflation, CAR and PLS. Furthermore, the long run results show that CAR and ROA influence NPF positively, whereas inflation and PLS have a negative influence on NPF. The rest of the variables, including economic growth, interest rate, FDR, FTV and OER do not have an influence on NPF in SRBs.

The results show that internal factors tend to dominate the NPF level of SRBs in Indonesia. This is understandable because SRBs are local banking institutions and face a different environment compared to Islamic commercial banks, which operate on the national banking scale. Therefore, the ability of SRBs’ management to understand business complexity and risk management may influence their capability in monitoring and controlling potential financing problems. The different results found for the short run and long run for certain variables require the banks to adopt the right strategy in accordance with these estimates.

SRBs must also meet the central bank’s NPLs/NPF requirements that have been issued for the banking industry. They need to more carefully screen prospective customers and the guarantees they can provide in the early stage of financing approval to reduce the risk of default. SRBs also need to develop the necessary competency in managerial and risk management to improve their understanding of business risks and risk mitigation with regard to SMEs’ customers. SRBs tend to attract more SMEs’ customers as the latter are considered less bankable when they seek financing with commercial banks; thus, they turn to SRBs to fulfill their financing needs because it is easier to meet these banks’ conditions. Thus, to manage the credit risk arising from the financing of SMEs, SRBs should adopt the right risk management mechanism.

The findings of this study would be helpful to the regulator for it to determine policies that accommodate the characteristics of SRBs because excessive NPF can disrupt the continuity of the banks and affect the economic growth of a nation. Regulations can focus on the improvement of the corporate governance structure in SRBs to increase the competency and experience of management in controlling, monitoring and mitigating financing risk especially for SMEs’ customers.

It is noted that several of the findings of this study are different from those reported in previous studies. This might be because of the difference in research subjects with different characteristics. This study examines the case of SRBs, whereas previous studies mostly discussed Islamic banks. Future research can try to analyze those differences in greater depth.