Access to Debt Finance in Sub-Saharan Africa: Redefining the Problem from Risk Perspective and Way Forward

Abstract

This article presents critical points of view relating to small and medium-sized enterprises (SMEs) access to debt finance in Sub-Saharan Africa (SSA). The prevailing view suggests that lack of credit information is a fundamental factor that contributes to the constraints faced by SMEs. We challenge this prevailing dominant view and argue that SMEs are constraints in accessing debt finance due to banks’ risk assessment strategies. At the same time, it suggests that banks modelling credit risk assessment for SMEs in SSA became extremely crucial for determining access to debt finance. This impacts on access to debt finance contrary to the popular claim that the opaque nature of SMEs is the major constraint limiting access to debt finance. In such a challenging context, SMEs are expected to either provide more information to banks to reduce the problem of information asymmetry as well as provide collateral and guarantees in the absence of reliable information to safeguard against the risk of default or maintain a long-term relationship with banks which is expected to result in an increase in debt financing. The authors emphasise the need to redefine the problem of access to debt finance so that financing obstacles for SMEs can be strengthened. The implications and alternative strategies for SMEs’ access to debt finance are discussed.

Keywords

Access to debt finance financing credit risk assessment SMEs Sub-Saharan Africa

Introduction

A great deal of research at the intersection of finance and entrepreneurship has focused on challenges facing small and medium-sized enterprises (SMEs) and particularly obstacles in SMEs access to finance (Abereijo & Fayomi, 2005; Agyapong et al., 2011; Beck, 2007; Beck et al., 2011; Behr & Güttler, 2007). While several attempts have been made to resolve this reoccurring phenomenon of access to finance (Bruns & Fletcher, 2008; Calabrese & Osmetti, 2013; Cassar et al., 2015; Huang et al., 2018; Krasniqi, 2010), the problem still persist in Sub-Saharan Africa (SSA) where an estimated 28.3% of SMEs are fully credit constrained and only between a third and a fifth of these SMEs have a bank loan or line of credit and recent studies have called for further scrutiny of the phenomenon (Moro et al., 2015; OECD, 2018; Runde et al., 2021; Zhu et al., 2019). In response to such a call, this article examines the issue of SMEs’ access to debt finance in SSA and provides evidence to suggest that the decision to lend to SMEs is significantly constrained by the perception of risk in SMEs’ lending. In turn, this is a consequence of banks’ risk assessment strategies used in evaluating such SMEs’ loans. Therefore, our central claim, which is similar to but extends to Palazuelos et al. (2018) and Moreira (2016), is that the constraint on SMEs’ access to finance is due to the perceived risks complicated by the banking system of SSA not being able to perform a fair and extensive actual credit risk assessment suitable to SMEs in SSA context. Debt financing of SMEs is a topic of significant interest amongst policymakers and researchers (Berger et al., 2006; De La Torre et al., 2008; Kersten et al., 2017). This interest is driven in part by the fact that debt finance from banks is recognised as a major source of external finance for SMEs (Cosh & Hughes, 1994; Petersen & Rajan, 1994) and also because of the important roles SMEs play in the economy.

SMEs are considered a major driver of innovation, employment, economic growth and development. However, recent empirical studies have suggested that the future of small business sector might be threatened due to financial constraints because finance is assessed as a major challenge confronting SMEs innovation, growth and development (Aghion et al., 2007; Beck et al., 2005; Olufunso Fatoki & Van Aardt Smit, 2011; Ramlee & Berma, 2013). This view is further shared by a number of studies which find that limited access to debt finance is considered a major factor that affects the growth and development of small businesses worldwide (Beck et al., 2005, 2006, 2008; Beck & Demirgüç-Kunt, 2006; Zhu et al., 2019). This is also an acute problem, especially in SSA where SMEs typically refer to limited access to finance as the major obstacle inhibiting growth of the sector (Abor & Biekpe, 2007; World Bank, 2006). The literature has identified finance as a major constraint to entrepreneurs in establishing and managing small firms in developing countries (Abereijo & Fayomi, 2005; Beck, 2007).

In fact, many of small businesses in developing countries rate access to finance as a major constraint as a result of high administrative costs, high collateral requirements and lack of experience with financial intermediaries (Onwuegbuchunam & Akujuobi, 2013). It is difficult for small businesses to access finance especially long-term loans from commercial banks for reasons ranging from the lack of collateral, difficulty in proving creditworthiness, inadequate credit history, high transaction costs, small cash flows and so on (Beck & Demirgüç-Kunt, 2006; IADB, 2004; Schiffer & Weder, 2001; Scholtens, 1999). In addition, information asymmetry has been highlighted as a huge constraint limiting access to finance for SMEs and cited as the root cause of SMEs credit risk (Altman et al., 2010; Altman & Sabato, 2007; Chen et al., 2010; Gong & Cullinane, 2018; Han et al., 2017; Li et al., 2016).

Our review shows that understanding credit risk exposure is a major factor in decision-making for banks involved in lending. This is because it influences the probability of banks’ survival and existence, suggesting that risk is highly prioritised by those banks who engaged in lending to SMEs (Huang et al., 2018; Oliyide, 2012; Palazuelos et al., 2018). So when banks decide to extend credit to SMEs, they are particularly concerned with the repayment ability of the SMEs. Stiglitz and Weiss (1981) observed early in the literature that banks are more interested in the risk of default inherent in a borrower when considering a loan application. This reflects in the bank’s decision whether to extend loans to SMEs and carefully determine the interest rate charged for the loan if eventually approved. This interest in the level of risk banks are exposed to is an effect of agency problem arising from information asymmetry (a situation where a borrower in a lending contract has more knowledge and relevant information, which is material to the lending contract but is less known or unknown to the lender). This hinders finance providers from making informed decision as it becomes difficult for the lender to distinguish between high- and low-risk businesses or good and bad borrowers. In effect, leading to the problems of adverse selection where unless the lender incurs significant transaction costs, bad/high-risk borrowers would be granted loans and creditworthy borrowers rejected by the lender, and/or moral hazard where borrowers conceal risky behaviour not specified in the credit contract from the creditor (Agyapong et al., 2011; Beck et al., 2011; Stiglitz & Weiss, 1981).

Therefore, in an attempt to mitigate lending risk resulting from the absence of information from SMEs, banks adopt various credit screening techniques which may include request for audited financial statements, change in terms of contract, loan covenants, increase in interest rates, increased collateral requirements and credit rationing (Beck, 2007; Beck et al., 2011; Olufunso Fatoki & Van Aardt Smit, 2011; Stiglitz & Weiss, 1981). Each of these techniques illustrates some of the measures banks used due to risk perception in SMEs’ lending to guard against risk of default. However, we argue that some of these measures and credit risk assessment strategies are unfit for SMEs in the SSA context—suggesting the need to redefine the problem of access to finance and chart new way forward. One motivation for this line of reasoning is to show that bank’s credit risk assessment and strategies for SMEs in SSA should be distinct from SMEs in developed countries or for large enterprises. Previous studies that have focused on modelling credit risk particularly for SMEs have been conducted in developed countries. These studies do not take into consideration the features and finance ecosystems of SMEs in SSA which are markedly different from those of SMEs in developed countries (Altman & Sabato, 2007; Calabrese & Osmetti, 2013).

Therefore, our work extends the growing literature on SMEs’ access to finance by rethinking the phenomenon around bank’s credit risk assessment and strategies as partly responsible for low access to finance in SSA. Moreover, we also build on extensive research which has been conducted on the use of credit ratings and scores and other quantitative information based on publicly available information on financial ratios and other indicators from annual reports (Altman & Sabato, 2007; Calabrese & Osmetti, 2013; Fantazzini & Figini, 2009) by acknowledging that qualitative information based on subjective evaluations of the borrower also play crucial role when granting loans to SMEs (Figini & Giudici, 2011). As such, the study reviews the criteria used by banks to assess SME loans, its practicability and implications for SMEs in the SSA context. We note that an insight into the SMEs’ loan application and the decision-making process lenders adopt in granting loans to SMEs would help address the problem of access to finance for SMEs in the SSA context. The rest of the article is organised as follows: The second section provides an examination of access to debt finance for SMEs in SSA. The third section describes the SMEs credit risk influencing factors. The fourth section discusses the main findings and considers the implication and alternative strategies for SMEs credit risk assessment. The last section concludes the study and offers suggestion for future direction.

Access to Debt Finance for SMEs in SSA

The definition of the term SMEs is said to be idiosyncratic to an environment, this implies that the concept varies from one country or context to the other (Oliyide, 2012). However, exploring the diverse definition of SMEs both in Africa and worldwide, the term is generally defined along the parameters of turnover, number of employees and assets. For example, in Nigeria, small enterprises are those enterprises employing between 6 and 29 employees with fixed assets of $100,000 while medium enterprises are enterprises employ between 30 and 99 employees with fixed assets of up to $1 million (Mensah, 2004). A survey conducted in 2010 by the national micro, small and medium enterprise revealed that the total number of enterprises in Nigeria stood at 17,284,671 (micro 17,261,753, small 21,264 and medium 1,654) and the total number of persons employed by this sector as at December 2010 stood at 32,414,884. In other words, although there are about 18 million enterprises in Nigeria with small firms representing 99% of the total number of enterprises and like most other African developing countries employing the largest number of workers, the large size of the micro-sector in Nigeria could reflect the possibility that certain unfavourable economic and financial conditions forced small and medium enterprises to become micro-enterprises.

There is no doubt that SMEs operations are crucial to the growth, development and economy of SSA. It is estimated that more than 95% of enterprises across the world are SMEs and they account for more than 60% of private-sector employment (Ayyagari et al., 2011). In SSA, SMEs account for a greater proportion of businesses in Ghana, Nigeria and South Africa and their contributions to GDP and poverty reduction are substantial. The SMEs are facing numerous challenges in SSA because most countries within the sub-region have shallow financial markets and interest rates have lagged behind rising inflation. Under these circumstances, SMEs require substantial finance to invest in research and development, innovation, new product development and market expansion. In context, previous research has identified that SMEs in Africa’s lack of access to finance is due to two high-risk characteristics. First, the provision of finance for Africa is generally rated as riskier than for other regions. Second, the provision of finance for small firms is globally rated as riskier than for large firms (Collier, 2009). In fact, around one-quarter of firms (23–25%) in SSA are classified as fully credit-constrained (Thornsten & Cull, 2014). It was noted that SMEs are unable to obtain external debt financing despite actively seeking credit or were discouraged from seeking credit due to the unfavourable terms and conditions of a proposed loan. SMEs in SSA are 19% less likely to obtain a loan than in other regions of the developing world (Thornsten & Cull, 2014).

Our review of the finance and entrepreneurship literature shows that problems with access to finance or inadequate financial support have been identified as the main cause of small enterprise failure in SSA (Abereijo & Fayomi, 2005; Ihua, 2009; Okpara & Wynn, 2007). This has hindered small businesses in SSA from having significant contributions to national output compared to other nations (Eriki & Inegbenebor, 2009). This further explains why although small enterprises in SSA represent about 99% of firms, they only contribute a low percentage to GDP compared with empirical studies which show that small and medium enterprises contribute over 55% of GDP and over 65% of total employment in high-income countries, over 60% of GDP and over 70% of total employment in low-income countries, 70% of GDP and 95% of total employment in middle-income countries (National MSME Collaborative Survey, 2010). Specifically, recent research found that there are over 44 million SMEs in SSA which provide an estimated 80% of jobs on the continent (Runde et al., 2021). Our review also uncovers that almost all of the SMEs in SSA are micro-businesses and more than half of SMEs (51%) require more funding than they currently access (cf. Runde et al., 2021). In this view, we argue that lack of access to credit facilities hindered small business contributions to growth and development as well as limit SMEs ability to meet their expectation of accelerating job creation, facilitate technology transfer, increase production of goods and services and create further opportunities for entrepreneurs (Eriki & Inegbenebor, 2009; Runde et al., 2021).

It is important to note that access to finance is essential at all stages of a firm’s life cycle as it helps it in fulfilling small enterprise roles of employment creation, poverty reduction and eradication, growth and development of any economy. Bank lending is recognised in the literature as the most common source of external finance for SMEs and most SMEs are heavily reliant on it for reasons ranging from size of funds being relatively small compared to the criteria for equity finance and the need to minimise intrusion into the business for fear of losing control or ownership. For SMEs in more developed countries, finance is noted to be relatively faster and easier to access because the financial markets are more developed, there is an enhanced risk management and information sharing system and resources are better allocated (Giovannini et al., 2013; Rajan & Zingales, 1996). On the contrary, in most countries of SSA constraints on access to finance are more pronounced and often highlighted among the main obstacles preventing SME development (Dinh et al., 2010; Stein et al., 2013). Factors such as asymmetric information, agency problems, high transaction costs, SME opacity, limited credit history and lack of collateral are often cited as causes that limit access to debt finance for SMEs in developing countries.

In sum, the literature suggests that a high proportion of SMEs still encounter substantial difficulties in accessing debt finance particularly as a result of higher interest rates and lack of assets to serve as collaterals to banks. Although banks claim credit is made available for SMEs and different resources are provided to increase access to finance for SMEs. But there is evidence to suggest that banks have become more risk-averse with evaluating SME credit thereby enhancing financing constraints of SMEs. In recognition of banks’ inability to reliably assess risks and potential benefits in lending to SMEs as a result of information deficiencies, a report by OECD (2018) maintains that the difficulties faced by SMEs in assessing debt finance and the higher cost typically associated with SMEs can be reduced in the presence of more reliable information about risks because it reduces the high-risks banks perceive when SMEs approach them for debt finance. This information can be provided by infrastructure for credit risk assessment such as credit bureaus and registries among others.

SMEs’ Credit Risk Influencing Factors

Agyapong et al. (2011) in their study of criteria for assessing SMEs’ loan in Ghana revealed that lenders perception of risk significantly shapes their lending decision as they dealt with SMEs. In deciding whether to lend to SMEs, the lenders ranked highest the following factors for consideration: purpose of loan, credit history, repayment schedule, type of business activity, size of loan relative to size of business and availability of security. Banks adopt a number of systems to identify risks and probability of default in their lending decision, this assessment influences their decision on whether to extend credit or not to grant credit and if debt is granted; the terms and condition attached to the lending relationship (Bruns & Fletcher, 2008; Moro et al., 2015). These systems include either the bank’s internal credit risk evaluation criteria or external credit rating and scores from credit bureaus. These are used to assess the creditworthiness of borrowers before a credit decision is made. Our review shows that three main factors; information, interest rate and collateral influence banks’ credit risk decision regarding SMEs’ debt financing in SSA. We elaborate upon these factors in the following subsections.

Role of Information in Lending

Banks critically take into consideration information from the financial statements in assessing the risk in a borrower as the analysis of this information enables them to determine the capability and repayment abilities of the borrower. Recent theoretical literature have stressed the role that information plays in a lending relationship (Altman et al., 2010; Altman & Sabato, 2007; Chen et al., 2010; Gong & Cullinane, 2018; Han et al., 2017; Li et al., 2016). They have outlined the role of information in accessing creditworthiness as banks are better able to evaluate a borrower and make informed lending decisions based on how much information it possesses about the borrower (Fredriksson & Moro, 2014; Moro et al., 2015). Zairani and Zaimah (2013) demonstrate that banks are unlikely to lend in the absence of verifiable information.

The lack of reliable information often makes it difficult for lenders to distinguish between creditworthy and unworthy borrower leading to the problem of adverse selection and moral hazards (Agyapong et al., 2011; Beck et al., 2011). Although using hard information or transaction-based lending default prediction models are easy to use in risk assessment in a practical sense because it has better predictability and less time is required for quantification of the risk, it is not applicable to many SMEs in developing countries for unavailability of required information (Andrikopoulos & Khorasgani, 2018). This technique is not desirable in the context of SMEs because it easily leads to the exclusion of small businesses from having access to debt finance (Behr et al., 2011), especially SMEs in developing countries that are characterised by the inability to provide robust information resulting in information asymmetry.

Role of Interest Rate and Determinants of Loan Pricing

In a situation where banks cannot evaluate the risks in lending to small enterprises due to information asymmetry, and consequent problems of adverse selection and moral hazards, they adopt different strategies to protect themselves from the potential risk of failure which may result in credit rationing (Abor & Biekpe, 2006). Banks tend to adopt increased interest rates to reflect the associated risk in lending. In SSA, local interest rates from banks are largely in double digits in the range of 20–25% in some SSA countries (Runde et al., 2021). In perspective, Krasniqi (2010) pointed out that when rates are increased, enterprises with lower tendencies to succeed would usually be willing to pay the increased interest rate thereby signalling high risks. On the other hand, when interest rates are increased by banks because of uncertainty surrounding borrowing small enterprises, creditworthy borrowing small enterprises would consider the increased rates too high and would choose not to borrow even though they have viable projects.

This reflects the work of George Akerlof’s ‘Market for Lemons’ which shows how information asymmetry can result in adverse selection and a situation where bad business drives out good business that now has to raise debt at the cost of the associated exaggerated risk. Akerlof’s work adopted the market for used cars as an example of the problem of uncertainty. So, supposing that there are two types of cars: the peaches (good used cars) and the lemons (defective used cars) but the buyer of a car does not know beforehand whether it is a peach or a lemon as a result of several not-always-traceable variables and other elements hidden from view as well as not easily accessible for inspection, the buyer’s best guess for a given used car is that the car is of average quality; accordingly, he/she will be willing to offer only the price of a car of known average quality. Consequently, the owner of a carefully maintained, never-abused, good used car will be unable to get a complementary price offer to make selling his car worthwhile, the owners of good cars will therefore not place their cars in the market or would withdraw the sales of their good cars which will resultantly reduce the average quality of cars on the market causing buyers to revise downwards their expectations of any given car whether good or bad because of perception of risk.

Therefore, the side effect of the cost of dishonesty in the market not only lies in the amount a buyer is cheated on but also includes the loss incurred from driving legitimate business out of existence. According to Akerlof (1970), when making business decisions, there is an inherent difficulty in distinguishing between good and bad quality in an uncertain situation; therefore, considering that small enterprises are perceived to be highly risky, the level of risk associated to the riskiest small enterprise tends to be applied to all other small business which reflects in how the loans extended to them is priced. In sum, high interest rates usually deter SMEs from accessing finance in SSA.

The Role of Collateral in a Loan Contract

Collateral is also said to play an important role in the lending behaviour of many banks. In the face of information asymmetry, collateral enables banks to militate against the event of project failure by limiting the potential of losses with the provided assets which can be used as substitutes for the loan loss thereby shifting risk from lenders back to the borrower (Elsas & Krahnen, 2000; Manove et al., 2001; Storey, 1994, p. 210). Although based on conventional wisdom, the request for collateral corresponds with how risky a loan or borrower is predicted, it is viewed as an instrument to lower risk exposure of a bank (Berger & Udell, 1990; Cowling & Westhead, 1996). Some models interpret collateral to be a signalling device which allows banks to militate against the problem of adverse selection common with financing debt under a situation of information asymmetry (Bester, 1985; Besanko & Thakor, 1987a, 1987b; Chan & Kanatas, 1985).

According to Bester (1985) and Besanko and Thakor (1987a) cited in Elsas and Krahnen (2000), under a signalling model a negative correlation is predicted to exist between risk and collateral as high levels of collateral is generally linked to low-risk borrowers as they offer large amounts of collateral to signal their quality why high-risk borrowers prefer no collateral at all attached to their loans. This argument follows logic that a borrower that is low risk has more reasons to pledge collateral than a high-risk borrower because the borrower has less likelihood of failure and loss of the pledged collateral than a high-risk borrower. The provision of collateral also helps prevent borrowers from behaving in an opportunistic manner because they have investment at stake which might be lost if the project fails, so it makes them commit to the success of the project (Abor & Biekpe, 2006; Storey, 1994, p. 210).

Furthermore, the provision of something of value by the borrower in the form of collateral serves as a signal to the bank that the small enterprise borrower believes the project would succeed (Abor & Biekpe, 2006). Other aspects of collateral existing studies have examined relates with how the request for collateral in a lending relationship depends on the length of the relationship between lender and borrower. This is because rather than request for collateral to mitigate against the problems associated with information asymmetry, this may be resolved through relationship lending where the lender through continuous contact with the borrower over time will learn about the borrowers’ credit behaviour (Behr et al., 2011; Berger & Frame, 2007; Ferri et al., 2019; Moro & Fink, 2013). The findings of Berger and Udell (1995) analysis of the relationship between collateral and length of relationship imply decreasing collateral requirements where bank–borrower relationship has brought about information intensity. Therefore, the provision of the bank’s required collateral helps banks to screen out high risks from low-risk borrowers (Krasniqi, 2010). However, where the enterprises are characterised by a lack of collateral, the financing constraint that small enterprises encounter would still persist as is the case with SMEs in sub-Sahara Africa.

Discussion

From a theoretical point of view, this article reaches three major findings. First, we find that past experiences of loan loss or uncertainty regarding debt are extended to SMEs because of information asymmetry makes banks to be risk-averse; this affects bank’s willingness to grant credit to SMEs and in such situations where debt is extended, it is done employing various strategies to either cover for the risk perceived or to shift the risk to the borrower. This finding gives the idea of an appropriate credit assessment strategy in the absence of bank-required information. This is because, on one hand, the tendency to place greater attention to risk by banks has affected how a loan is assessed and the risk control measures put in place. While these risk control measures allow the avoidance of credit losses (i.e. type 11 errors) similar to Dietsch and Petey (2002), type 1 errors (i.e. a situation where creditworthy borrowers are denied credit) become difficult to overcome as the lenders are particular on risk reduction (Deakins & Hussain, 1994; Nilsson & Öhman, 2012). This strategy of mitigating or shifting risk currently adopted by banks eliminates type 1 error; however, type 2 error is created where creditworthy borrowers would have been denied access to debt for their lack of collateral or information or acceptance of high loan pricing.

Second, regarding the characteristics of SMEs in developing countries, we find that the problem of information asymmetry is not necessarily because SMEs have refused to share information about their businesses or provide collateral as a signal of their low risk and creditworthiness, but the main issue is that SMEs in developing countries are very opaque in nature. As such, it is very common to find that these enterprises are unable to provide lending requirements imposed on them by banks and are essentially exempted from the possibility of accessing debt based on those requirements (Bruns et al., 2008; Palazuelos et al., 2018). Third, banks do not have factual evidence of an assessment of risk of default in SMEs considering their opaque characteristics in SSA, and they also do not have a basis for assuming a risk of default is inherent in the lending. Altman and Sabato (2013) maintain that from a credit risk perspective, risk is perceived to be higher in SMEs compared to larger enterprises. This has been partly attributed to the fact that the default prediction model developed for large enterprises when applied to SMEs shows a poorer performance resulting from the lower prediction power of the model. This is because SMEs have different characteristics from large enterprises.

Furthermore, the finding is consistent with a similar study by Uchida et al. (2008) where they investigated the importance of bank size and relationship in Japanese SMEs in relation to the availability of audited financial statements. Uchida et al. (2008) maintain that the identification of whether the relevant SMEs studied have audited financial statements would help determine whether an SME can receive loans from a bank whose lending technology is transaction/financial statement lending-based and also permit a more penetrating interpretation of the gap, while the work of Uchilda et al. (2008) examined SMEs of developed countries who often can provide audited accounts. Our research, nevertheless, extends recent literature at the intersection of SMEs’ access to finance and entrepreneurship in SSA showing that most SMEs especially micro-businesses do not have audited accounts and that banks (lenders) need creative approach to evaluate SMEs’ loan approval (cf. Moyo & Sibindi, 2022; Runde et al., 2021). Therefore, it is very important for banks to make accurate judgement and assessment of risks or probability of default in lending to SMEs. This is not just because of the losses that can result from underestimating risks when debt is extended to SMEs but also because of loss of opportunities and gains that may result from not extending credit to SMEs due to wrong risk assessment. Thus, the importance of an appropriate credit risk assessment for SMEs in SSA should not be underestimated.

Implication of Assessment Strategies on SME Access to Finance

If the financial intermediation role which involves channelling funds from the surplus sector to the deficit sector in the economy of every nation is successfully performed by banks, as noted by Oliyide (2012), it will be instrumental to SMEs discharging its roles in economic growth and development. However, bank’s decision to grant loans to businesses and the determination of the interest rates is based on the evaluation of the business information to determine borrower’s ability to repay (Cassar et al., 2015). Nonetheless, using the evidence of the assessment and eligibility criteria that banks adopt to review SMEs’ loans, this study in agreement with Lefilleur (2008, 2009) argued that banks are biased against SMEs, which results from the perceived risks associated with small firms which makes banks very overcautious in SMEs credit review and often reluctant to extend credit to them. Lefilleur (2009) further argued that the problems of information asymmetry and SMEs inability to provide required security are the reason risk in SMEs’ lending is overestimated.

The inability of SMEs that are informationally opaque to provide security for their loans result into first, an increase in transaction cost derived from risk assessment and supervision; second, inaccurate and overestimated risk assessment by banks. These two factors make banks avoid extending credit to SMEs and in the event, they extend, it is at a very high interest rate (Lefilleur, 2009). In SSA, while there have been many models developed and adopted by practitioners for large enterprises to predict probability of default, none have been developed specifically for SMEs in developing countries putting into consideration its unique characteristics. We are not suggesting the need for a generalised or one-size-fits-all approach to credit lending to SMEs. Instead, we are acknowledging that there are unique characteristics of SMEs in SSA that make them vulnerable in accessing finance from banks and call for redefining the problem. Our review also found that the SMEs ecosystem which affects SMEs in SSA countries differ substantially from that of developed countries and credit risk assessment should equally take this feature into consideration. In sum, our overarching claim is that there is no one-size-fits approach to SMEs’ access to finance in SSA but that each bank requires unique credit risk tool kits when dealing with the specific issue that each SME exhibits in SSA.

Alternative Strategies for SME Credit Assessment

Although Erdogan (2018) argues that the lack of proper accounting records and reliable financial statements to reflect the financial situation of small enterprise borrowers is a constraining factor limiting access to finance from banks, in a developing country situation where a lack of reliable information is an inherent characteristic of small enterprises. However, the assessment of an enterprise’s true financial position cannot be limited to statements provided by the enterprises. Furthermore, with the understanding that it is important for banks to quantitatively analyse the financial condition of a borrower (Erdogan, 2018), we found that lenders can achieve the same aim by adopting strategies to obtain additional reliable information that properly suits the borrower’s characteristics in making informed lending decisions (Moro et al., 2015). For example, since small enterprises primarily use short-tenured loans to finance their operating capital, the bank can as an alternative to relying on accounting records or proper financial statements assess the cash conversion cycle and inventory holding period of the borrowing small enterprise. The relationship between the borrowing enterprise trade receivables and payable collection and maturity period alongside the proposed loan application can be considered to ensure the enterprises do not encounter difficulty in the repayment of the loan.

Also, since credit track record is usually a criterion considered in assessing the risk of lending to a small enterprise borrower (Bruns & Fletcher, 2008) and with the understanding that most small enterprises in developing countries do not always have a formal credit track record to justify that creditworthiness, as an alternative, the bank can consider the borrowers indebtedness to its suppliers for example and assess whether the borrowing small enterprise have experienced difficulties in the repayment of its trade payables within specified periods. This also applies to longer-term loans which are tailored towards investment and capital projects of small enterprises adopting alternative mechanics to assess the viability of the project and its repayment capabilities. On the contrary, to deal with this potential problem of default, banks have mostly concentrated on type 2 errors. They have developed measures to mitigate the chances of a loss and also attempted to shift risk from the banks to the borrowers (Bruns & Fletcher, 2008). This explains the use of formal guidelines and assessment criteria for credit decision-making by banks. This adoption limits the influence of branch managers and relationship officers who have a direct relationship with the borrower and who in the absence of formal information provision can informally signal and rate risk in the borrower. So, although the relationship officers and bank branch managers attempt to determine the repayment capabilities of the borrowing small enterprises and help to support the credit decision process, the explicit assessment criteria set by the bank are the essential factors that sum up risk in the credit decision process.

Conclusion

This article is an additional step forward in research into the role perceived risks play in the lending relationship of SMEs. It shows that it is very important for banks to make accurate judgements and the assessment of risks or probability of default in lending in lending to SMEs, not just because of the losses that can result from underestimating risks when debt is extended to credit unworthy borrowers but also because of losses of opportunities and gains that may result from not extending credit to worthy borrowers for wrong risk assessment reasons; hence, the importance of an appropriate credit risk assessment for small and medium enterprises in developing countries should not be underestimated. Therefore, an understanding of the peculiar characteristics of a small enterprise in an ecosystem is beneficial in helping to design a risk assessment theoretical framework to address the problem of limited access to debt finance of small enterprise in developing countries. More so, our suggestion will entail more bank involvement and less algorithmic decision-making in credit risk assessment of SMEs. The study suggests that bank’s risk-averse policy to supply loans to small businesses, which results from the highly perceived risky nature of small enterprises, is responsible for the financial constraints confronting small businesses. This research therefore concludes that the risk assessment and management strategies adopted by banks considering the peculiarity of SMEs in developing business environments impact the supply of credit and therefore merit particular attention.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

ORCID iDs

Olapeju Comfort Ogunmokun

Oluwasoye P. Mafimisebi

Demola Obembe

References

Abereijo

I. O.

, & Fayomi

A. O.

(2005). Innovative approach to SME financing in Nigeria: A review of small and medium industries equity investment scheme (SMIEIS). Journal of Social Science , 11(3), 219–227. Retrieved, 11 February 2020, from https://www.kpmg.co.za

Abor

, & Biekpe

(2006). Small business reliance on bank financing in Ghana. Emerging Markets Finance and Trade , 43(4), 93–102. https://doi.org/10.2753/REE1540-496X430405

Aghion

, Fally

, & Scarpetta

(2007). Credit constraints as a barrier to the entry and post-entry growth of firms. Economic Policy , 22(52), 732–779. https://doi.org/10.1111/j.1468-0327.2007.00190.x

Agyapong

, Agyapong

G. K. Q.

, & Darfor

(2011). Criteria for assessing small and medium enterprises’ borrowers in Ghana. International Business Research , 4. https://doi.org/10.5539/ibr.v4n4p132

Akerlof

G. A.

(1970). The market for ‘Lemons’: Quality uncertainty and the market mechanism. The Quarterly Journal of Economics . Retrieved, 4 May 2019, from https://www.perishablepundit.com/docs/market-for-lemons.pdf

Altman

E. I.

, & Sabato

(2007). Modelling credit risk for SMEs: Evidence from the US market. Abacus , 43(3), 332–357. https://doi.org/10.1111/j.1467-6281.2007.00234.x

Altman

E. I.

, & Sabato

(2013). Modeling credit risk for SMEs: Evidence from the US market (pp. 251–279). https://doi.org/10.1142/9789814417501_0009

Altman

E. I.

, Sabato

, & Wilson

(2010). The value of non-financial information in SME risk management. The Journal of Credit Risk , 6(2), 95–127. https://doi.org/10.21314/jcr.2010.110

Andrikopoulos

, & Khorasgani

(2018). Predicting unlisted SMEs’ default: Incorporating market information on accounting-based models for improved accuracy. British Accounting Review , 50(5), 559–573. https://doi.org/10.1016/j.bar.2018.02.003

10.

Ayyagari

, Demirgiic-Kunt

, & Maksimovic

(2011). Small vs. young firms across the world—Contribution to employment, job creation, and growth [Policy Research Working Paper 5631], The World Bank Development Research Group.

11.

Beck

(2007). Financing constraints of SMEs in developing countries: Evidence, determinants and solutions . The World Bank. https://core.ac.uk/download/pdf/6635804.pdf

12.

Beck

, & Demirgüç-Kunt

(2006). Small and medium-size enterprises: Access to finance as a growth constraint. Journal of Banking and Finance , 30(11), 2931–2943. https://doi.org/10.1016/j.jbankfin.2006.05.009

13.

Beck

, Demirgüç-Kunt

, Laeven

, & Maksimovic

(2006). The determinants of financing obstacles. Journal of International Money and Finance , 25(6), 932–952. https://doi.org/10.1016/j.jimonfin.2006.07.005

14.

Beck

, Demirgüç-Kunt

, & Maksimovic

(2005). Financial and legal constraints to growth: Does firm size matter? Journal of Finance , 137–177. https://doi.org/10.1111/j.1540-6261.2005.00727.x

15.

Beck

, Demirgüç-Kunt

, & Martinez

M. S.

(2008). Bank financing for SMEs around the world [The World Bank: Policy Research Working Paper, n. 4785 (November)], pp. 1–43. https://doi.org/10.1596/1813-9450-4785

16.

Beck

, Demirgüç-Kunt

, & Pería

M. S. M.

(2011) Bank financing for SMEs: Evidence across countries and bank ownership types. Journal of Financial Services Research , 39(1–2), 35–54. https://doi.org/10.1007/s10693-010-0085-4

17.

Behr

, & Güttler

(2007). Credit risk assessment and relationship lending: An empirical analysis of German small and medium-sized enterprises. Journal of Small Business Management , 45(2), 194–213. https://doi.org/10.1111/j.1540-627X.2007.00209.x

18.

Behr

, Entzian

, & Güttler

(2011). How do lending relationships affect access to credit and loan conditions in microlending? Journal of Banking and Finance , 35(8), 2169–2178. https://doi.org/10.1016/j.jbankfin.2011.01.005

19.

Berger

A. N.

, & Frame

W. S.

(2007). Small business credit scoring and credit availability. Journal of Small Business Management , 45(1), 5–22. https://doi.org/10.1111/j.1540-627X.2007.00195.x

20.

Berger

A. N.

, & Udell

G. F.

(1990). Collateral, loan quality and bank risk. Journal of Monetary Economics , 25(1), 21–42. https://doi.org/10.1016/0304-3932(90)90042-3

21.

Berger

A. N.

, & Udell

G. F.

(1995). Relationship lending and lines of credit in small firm finance. Journal of Business . Retrieved, 15 January 2020, from https://www-jstor-org-443.web.bisu.edu.cn/action/showPublication?journalCode=jbusiness

22.

Berger

A. N.

, & Udell

G. F.

(2006). A more complete conceptual framework for SME finance. Journal of Banking & Finance , 30, 2945–2966. https://doi.org/10.1016/j.jbankfin.2006.05.008

23.

Besanko

, & Thakor

A. V.

(1987a). Collateral and rationing: Sorting equilibria in monopolistic and competitive credit markets. International Economic Review , 28(3), 671. https://doi.org/10.2307/2526573

24.

Besanko

, & Thakor

A. V.

(1987b). Competitive equilibrium in the credit market under asymmetric information. Journal of Economic Theory , 42(1), 167–182. https://doi.org/10.1016/0022-0531(87)90108-6

25.

Bester

(1985). Screening vs. rationing in credit markets with imperfect information. The American Economic Review , 75(4), 850–855.

26.

Bruns

, & Fletcher

(2008). ‘Banks’ risk assessment of Swedish SMEs. Venture Capital , 10(2), 171–194. https://doi.org/10.1080/13691060801946089

27.

Calabrese

, & Osmetti

S. A.

(2013). Modelling small and medium enterprise loan defaults as rare events: The generalized extreme value regression model. Journal of Applied Statistics , 40(6), 1172–1188. https://doi.org/10.1080/02664763.2013.784894

28.

Cassar

, Ittner

C. D.

, & Cavalluzzo

K. S.

(2015). Alternative information sources and information asymmetry reduction: Evidence from small business debt. Journal of Accounting and Economics , 59(2–3), 242–263. https://doi.org/10.1016/j.jacceco.2014.08.003

29.

Chan

Y.-S.

, & Kanatas

(1985). Asymmetric valuations and the role of collateral in loan agreements. Journal of Money, Credit and Banking , 17(1), 84. https://doi.org/10.2307/1992508

30.

Chen

, Wang

, & Wu

D. D.

(2010). Credit risk measurement and early warning of SMEs: An empirical study of listed SMEs in China. Decision Support Systems , 49(3), 301–310. https://doi.org/10.1016/J.DSS.2010.03.005

31.

Collier

(2009, May). Rethinking finance for Africa’s small firms in ‘SME Financing in SSA’. Proparco Magazine on Private Sector and Development (No. 1).

32.

Cosh

A. D.

, & Hughes

(1994). Size financial structure and profitability: UK companies in the 1980s. In Hughes

& Storey

D. J.

(Eds.), Finance and small firms . Routledge.

33.

Cowling

, & Westhead

(1996). Bank lending decisions and small firms: Does size matter? International Journal of Entrepreneurial Behaviour & Research , 2(2), 52–68. https://doi.org/10.1108/13552559610119331

34.

De La Torre

, Martínez Pería

M. S.

, & Schmukler

S. L.

(2008). Bank involvement with SMEs: Beyond relationship lending . https://econ.worldbank.org

35.

Deakins

, & Hussain

(1994). Risk assessment with asymmetric information. International Journal of Bank Marketing , 12(1), 24–31. https://doi.org/10.1108/02652329410049571

36.

Dietsch

, & Petey

(2002). Are SMEs sensitive to systematic risk? A study of probabilities of default and assets correlations in French and German SMEs . https://www.fbv.kit.edu/symposium/9th/papers/Die_Pet.pdf

37.

Dinh

H. T.

, Mavridis

D. A.

, & Nguyen

H. B.

(2010). The binding constraint on firms’ growth in developing countries . Retrieved, 3 September 2019, from https://econ.worldbank.org

38.

Elsas

, & Krahnen

J. P.

(2000). Collateral, default risk, and relationship lending: An empirical study on financial contracting . Retrieved, 11 November 2019, from https://hdl.handle.net/10419/78076www.econstor.eu

39.

Erdogan

A. I.

(2018). Factors affecting SME access to bank financing: An interview study with Turkish bankers. Small Enterprise Research , 25(1), 23–35. https://doi.org/10.1080/13215906.2018.1428911

40.

Eriki

O. P.

, & Inegbenebor

(2009). Capacity of SMEs in Nigeria to access institutional equity finance. Journal of Business Management , 2(1), 1–5.

41.

Fantazzini

, & Figini

(2009). Random survival forests models for SME credit risk measurement. Methodology and Computing in Applied Probability , 11, 29–45. https://doi.org/10.1007/s11009-008-9078-2

42.

Ferri

, Murro

, Peruzzi

, & Rotondi

(2019). Bank lending technologies and credit availability in Europe: What can we learn from the crisis? Journal of International Money and Finance. Elsevier Ltd , 95, 128–148. https://doi.org/10.1016/j.jimonfin.2019.04.003

43.

Figini

, & Giudici

(2011). Statistical merging of rating models. Journal of the Operational Research Society , 62(6), 1067–1074. https://doi.org/10.1057/jors.2010.41

44.

Fredriksson

, & Moro

(2014). Bank ‘SMEs relationships and banks’ risk-adjusted profitability. Journal of Banking Finance , 41, 67–77. https://doi.org/10.1016/j.jbankfin.2013.12.026

45.

Giovannini

, Iacopetta

, & Minetti

(2013). Financial markets, banks, and growth: Disentangling the links. Revue de l’Ofce , 131(5), 105–147. https://doi.org/10.3917/REOF.131.0105

46.

Gong

, & Cullinane

(2018). Finance and risk management for international logistics and the supply chain . Retrieved, 17 September 2024, from https://books.google.com/books?hl=en&lr=&id=4aRqDwAAQBAJ&oi=fnd&pg=PP1&dq=Gong+and+Cullinane+(2018),+&ots=VoerJXpK8d&sig=d2wdTdbejWW6674nJaeCuvcuFEs

47.

Han

, Zhang

, & Greene

F. J.

(2017). Bank market concentration, relationship banking, and small business liquidity. International Small Business Journal , 35(4), 365–384.

48.

Huang

, Liu

, & Ren

(2018). Enterprise credit risk evaluation based on neural network algorithm. Cognitive Systems Research , 52, 317–324. https://doi.org/10.1016/j.cogsys.2018.07.023

49.

IADB. (2004). Inter-American Development Bank annual report . Retrieved, 17 September 2024, from https://publications.iadb.org/en/publications/english/viewer/Inter-American-Development-Bank-Annual-Report-2004.pdf

50.

Ihua

U. B.

(2009). SMEs key failure-factors: A comparison between the United Kingdom and Nigeria. Journal of Social Science , 18(3), 199–207.

51.

Kersten

, Harms

, Liket

, & Maas

(2017). Small firms, large impact? A systematic review of the SME finance literature. World Development , 97, 330–348. https://doi.org/10.1016/j.worlddev.2017.04.012

52.

Krasniqi

B. A.

(2010). Are small firms really credit constrained? Empirical evidence from Kosova. International Entrepreneurship and Management Journal , 6(4), 459–479. https://doi.org/10.1007/s11365-010-0135-2

53.

Lefilleur

(2008). Comment améliorer l’accès au financement pour les pme d’afrique subsaharienne? Afrique Contemporaine , 227(3), 153–174. https://doi.org/10.3917/afco.227.0153

54.

, Karim

, & Munir

(2016). The determinants of leasing decisions: An empirical analysis from Chinese listed SMEs. Managerial Finance , 42(8), 763–780. https://doi.org/10.1108/MF-06-2015-0166/FULL/HTML

55.

Manove

, Padilla

A. J.

, & Pagano

(2001). Collateral versus project screening: A model of lazy banks. Journal of Economics , 32(4), 726–744.

56.

Mensah

(2004). A review of SME financing schemes in Ghana . https://semcapitalgh.com/downloads/research/SME_Financing_Schemes_in_Ghana.pdf

57.

Moreira

D. F.

(2016). The microeconomic impact on growth of SMEs when the access to finance widens: Evidence from internet & high-tech industry. Social and Behavioral Sciences , 220, 278–287.

58.

Moro

, & Fink

(2013). Loan managers’ trust and credit access for SMEs. Journal of Banking & Finance , 37(3), 927–936. https://doi.org/10.1016/j.jbankfin.2012.10.023

59.

Moro

, Fink

, & Maresch

(2015). Reduction in information asymmetry and credit access for small and medium-sized enterprises. Journal of Financial Research , 38(1), 121–143. https://doi.org/10.1111/jfir.12054

60.

Moyo

, & Sibindi

A. B.

(2022). Does bank competition affect credit access in Sub-Saharan Africa? Evidence from World Bank informal firms surveys. Journal of African Business , 23(1), 180–198. https://doi.org/10.1080/15228916.2020.1826857

61.

National MSME Collaborative Survey. (2010). Survey report on the micro, small and medium enterprises in Nigeria . National Bureau of Statistic.

62.

Nilsson

, & Öhman

(2012). Better safe than sorry: Defensive loan assessment behaviour in a changing bank environment. Qualitative Research in Accounting & Management , 9(2), 146–167. https://doi.org/10.1108/ 11766091211240360

63.

OECD. (2018). Enhancing SME access to diversified financing instruments. Plenary session 2 . Retrieved, 3 September 2019, from https://www.oecd.org/cfe/smes/ministerial/documents/2018-SME-Ministerial-Conference-Plenary-Session-2.pdf

64.

Okpara

J. O.

, & Wynn

(2007). Determinants of small business growth constraints in a sub-Saharan African economy. SAM Advanced Management Journal , 72(2), 24–35.

65.

Oliyide

(2012). Law, credit risk management and bank lending to SMEs in Nigeria. Commonwealth Law Bulletin , 38, 673–695.

66.

Olufunso Fatoki

, & Van Aardt Smit

(2011). Constraints to credit access by new SMEs in South Africa: A supply-side analysis. African Journal of Business Management , 5(4), 1413–1425. https://doi.org/10.5897/AJBM10.1335

67.

Onwuegbuchunam

D. E.

, & Akujuobi

A. B. C.

(2013). SMEs financing and development in Nigeria’s shipping sector: A case study. Advances in Management & Applied Economics . Scienpress Ltd.

68.

Palazuelos

, Herrero Crespo

Á.

, & Montoya Del Corte

(2018). Effect of perceived default risk and accounting information quality on the decision to grant credit to SMEs. Risk Management , 20(2), 121–141. https://doi.org/10.1057/s41283-017-0030-z

69.

Petersen

M. A.

, & Rajan

R. G.

(1994). The benefits of lending relationships: Evidence from small business data. The Journal of Finance , 49(1), 3–37. https://doi.org/10.1111/j.1540-6261.1994.tb04418.x

70.

Rajan

R. G.

, & Zingales

(1996). Financial dependence and growth . Retrieved, 3 September 2019, from https://www.nber.org/papers/w5758.pdf

71.

Ramlee

, & Berma

(2013). Financing gap in Malaysian small-medium enterprises: A supply-side perspective. South African Journal of Economic and Management Sciences , 16(5). https://www.scielo.org.za/scielo.php?script=sci_arttext&pid=S2222-34362013000500012

72.

Runde

D. F.

, Savoy

C. M.

, & Staguhn

(2021). Supporting small and medium enterprises in sub-Saharan Africa through blended finance. CSIS briefs . CSIS.

73.

Schiffer

, & Weder

(2001). Firm size and the business environment, firm size and the business environment . The World Bank. https://doi.org/10.1596/978-0-8213-5003-4

74.

Scholtens

(1999). Analytical issues in external financing alternatives for SBEs. Small Business Economics , 12(2), 137–148. Retrieved, 12 February 2020, from https://search-proquest-com.proxy.library.dmu.ac.uk/docview/220955215?pq-origsite=360link

75.

Stein

, Ardic

O. P.

, & Hommes

(2013). Closing the credit gap for formal and informal micro, small, and medium enterprises. International Finance Corporation. Retrieved, 3 September 2019, from https://openknowledge.worldbank.org/handle/10986/21728

76.

Stiglitz

J. E.

, & Weiss

(1981). Credit rationing in markets with rationing credit information imperfect. The American Economic Review , 71(3), 393–410. https://doi.org/10.2307/1802787

77.

Storey

D. J.

(1994). New firm growth and bank financing. Small Business Economics , 6(2), 139–150. https://doi.org/10.1007/BF01065186/METRICS

78.

Thornsten

, & Cull

(2014). SME finance in Africa. Journal of African Economies , 23(5), 583–613.

79.

Uchida

, Udell

G. F.

, & Watanabe

(2008). Bank size and lending relationships in Japan. Journal of the Japanese and International Economies , 22(2), 242–267. https://doi.org/10.1016/J.JJIE.2007.07.003

80.

World Bank. (2006). World development report 2006: Equity and development (English) . World Bank Group. https://documents.worldbank.org/curated/en/435331468127174418/World-development-report-2006-equity- and-development

81.

Zairani

, & Zaimah

Z. A.

(2013). Difficulties in securing funding from banks: Success factors for small and medium enterprises (SMEs). Journal of Advanced Management Science , 4(1). https://doi.org/10.12720/joams.1.4.354-357

82.

Zhu

, Zhou

, Xie

, Wang

G.-J.

, & Nguyen

T. V.

(2019). Forecasting SMEs’ credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach. International Journal of Production Economics , 211, 22–33. https://doi.org/10.1016/j.ijpe.2019.01.032