“In God we trust. All others must bring data.”
–W. Edwards Deming, American Engineer and Management Consultant
When businesses need credit, they often apply to formal lenders like banks or non-banking financial companies (NBFCs). Before approving the application and disbursing a loan, lenders perform a very important activity: checking the creditworthiness of prospective borrowers and credit underwriting. In order to do this, they check the company’s credit history, score, and rank, as well as their loan repayment history, revenues, profits, and assets and liabilities.
A creditworthiness check is a crucial step in the loan disbursal process for lenders because it reveals the amount of risk that they may be taking on by lending to a particular borrower.
That said, it’s not easy for lenders to assess the creditworthiness of one particular group of business borrowers: small and medium enterprises (SMEs).
One of the main reasons banks and NBFCs struggle to access SME creditworthiness and perform SME risk assessment is data poverty. This article will explore the challenge and its solution from the perspective of a borrower (SME) and a lender (bank).
The Importance of Credit for SMEs
Being the backbone of economies, particularly in developing economies like India, SMEs must be able to access credit from formal lending channels. However, many of them struggle to do so, which explains why The World Bank estimates that 40% of formal micro, small and medium enterprises (MSMEs) in developing countries face a credit gap of $5.2 trillion every year.
The unmet financing need for these 65 million firms is equivalent to 1.4X the current level of global MSME lending. In general, a large number of SMEs in the country don’t have access to formal credit. And this is a huge problem for many SME-rich countries, especially India.
When SMEs cannot access capital, they often turn to informal credit sources such as moneylenders, family, or friends, which results in the borrower paying a high rate of interest and less liquidity in the business. In India alone, this informal credit market is worth a staggering $500 billion.
Without adequate credit, SMEs are unable to get the credit they need to manage day-to-day operations, expand their workforce, or pursue new growth opportunities. One reason for the lack of credit access is banks’ inability to accurately assess SME risk because they don’t have the data needed to conduct such assessments.
Data Poverty in Assessing SME Credit Risk: Causes and Impact
In developing countries like India, lenders are frequently unable to assess SMEs’ creditworthiness and determine their own risk in lending to these SMEs. This is due to the problem of data poverty. Institutional lenders simply don’t have enough information to assess SMEs’ credit profile and their own SME lending risk.
When banks ask for more information, many SME borrowers feel overwhelmed by the prospect of gathering this information and preparing relevant documentation. More often than not, many of them don’t collect this information in the first place since they don’t think that it is necessary to run a successful business. This is especially true in a country like India where so many SME owners may not have complete financial know-how and therefore do not possess enough knowledge about the financial aspects of running a business, such as:
- Accounting and book-keeping
- Cost management
- Budgeting
- Impact of inflation
- Credit options
- Insurance
- Debt management
- Working capital management
- Taxation
SMEs that are unaware of these financial nitty-gritties rarely (if ever) gather business-specific information about their:
- Sales numbers by product or product category
- Sales numbers by geography or line of business
- Comparisons of financial performance versus budget
- Operating expenses
- Amount of assets and liabilities (and changes to them)
- Cashflows
- Working capital changes
SMEs also argue that they have already provided sufficient information with the loan application. Here’s where friction emerges between the borrower and the lender. Even if the SME thinks that they don’t need to produce bespoke financial information, lenders disagree. They counter-argue that as external parties, they don’t know the business very well and therefore need more explicit or detailed information in order to:
- Assess the company’s health and financial condition
- Identify the drivers of its revenues and profits
- Determine if it is creditworthy
- Understand the bank’s own risk in lending to the SME
- Decide whether to approve the loan application or not
These mismatching perspectives result in a paucity of detailed, timely, and reliable financial information about the SME borrower. Without this data, banks and NBFCs are unable to identify and manage their credit risk.
This issue, along with many SMEs’ inabilities to provide collateral to reduce banks’ credit risk, prevents banks from making lending decisions that could potentially benefit both parties. Ultimately, this leads to more loan applications getting rejected and the SME credit gap getting wider and wider.
How Fintechs can Help Address the Data Poverty Problem in SME Lending
In the absence of financial information – not to mention collateral assets – Big Data could help banks with credit risk assessments of SMEs. This data could be a mix of traditional and proprietary information. The former includes information about the firm and its owners, such as owners’ gender and age, the company’s location, business type, etc. Proprietary information could include sensitive information specific to the firm, such as payments made or credit history.
All of this information can be used to assess an SME borrower’s financial health (capacity to repay), business stability, and behavioural characteristics (willingness to repay). However, traditional banks using legacy data collection methods often find it difficult to collect this information, and even more difficult to analyse it and make decisions.
Here’s where Fintechs and data-driven platforms come in. The best platforms are powered by cutting-edge Artificial Intelligence and Machine Learning technologies, which allows them to collect data from numerous sources like tradeline, credit inquiry, and internal records of the company and yield useful insights from this information.
Lenders can then use these insights to assess borrowers’ creditworthiness and conduct risk assessments. They can also update their assessments dynamically based on real-time data.
Access to useful data enables lenders to quickly complete SME risk assessments, process more loan applications faster, and build up their loan books. Some platforms can even predict loan defaults, thus helping banks to minimise their risk and lower the potential for costly mistakes.
The Key to Successful SME Risk Assessments: Digitisation + Fintech
SME risk assessment is not an easy process, even for large lenders with large teams. Most banks don’t think that the effort is worth it, resulting in more loan rejections and a widening SME credit gap.
Fortunately, fintech solutions can provide and analyse huge quantities of quantitative and qualitative SME data and help banks and NBFCs take better lending decisions.
Platforms like Yubi are aimed to addressing the credit gap problem in India’s SME sector. Yubi brings the power of digitisation to connect SME borrowers with lenders. Through this one-stop solution, SMEs can satisfy their debt requirements while lenders can add more high-quality borrowers to their loan books.
Click here to know more about Yubi.
