[Editor’s note: This is a blog post from Helene Page at Moody’s Analytics. Moody’s is a Bronze Sponsor at LendIt USA 2017 which will take place on March 6-7, 2017 in New York City.]
Assessing the credit risk of small and medium size enterprises (SMEs) is one of the most challenging tasks in banking. The difficulties stem from fragmented financial data, the strength of risk models, length of the process, and broader issues such as the tension between sales and credit. The competitive lending environment, regulatory requirements, different geographies, and positions in the economic and credit cycles also have an impact. This blog post discusses how these challenges might be addressed from a Moody’s Analytics perspective, which has been developed from our extensive experience in assessing credit risk.
What’s in a Name?
SMEs encompass a broad range of company shapes and sizes, and other variants such as the industry within which the company operates. Nevertheless, there are some common factors, such as:
- Private ownership
- Less than $50 million in revenue and fewer than 250 employees
- Higher default rates than larger enterprises
These three factors drive a host of other shared characteristics, including the ability to take a longer term view on business and investment plans, inconsistency in the quality of financial information, and reliance on key individuals who are also likely to be owners.
Meeting the Challenges
There are seven key challenges related to assessing SME credit risk, including challenges stemming from the wider business infrastructure.
1. Financial information. A common complaint, especially of credit executives, is that SME financial information does not provide sufficient detail to understand the drivers of the business. Examples include:
- How sales break down by product, geography, or line of business
- Performance compared with budget
- The absence of any cash flow statement
Borrowers often argue that the information they produce is sufficient for them to run their business successfully, and that they do not have sufficient resources to produce bespoke financial information. However, if a good relationship exists and the request is reasonable, borrowers are usually open to amending the information they provide. Moreover, technological advances can address some of these challenges, as many providers offer the ability to link a borrower’s financial accounting software directly with the bank’s records. This automated delivery of financial data from borrower to lender can help resolve the resource, detail, and timeliness issues.
2. Predicting cash flow. Before cash flow can be predicted, it must be derived from either historical or forecasted income and balance sheet statements. After data has been extracted, it is not an unduly difficult task using a financial analysis package. However, understanding the expected trend of the borrower’s business, and ensuring that the characteristics of any new capital structure are included in forecast cash flow modeling, can only be undertaken with appropriate information from the borrower.
3. Rating models. Models are only as good as the quality and richness of the data that drives them. It is therefore important to have a proven and valid benchmark – reliable market information from historical SME company default data. Thus, a database of historical financial statements and default rates on a global scale and of substantial size is required. For example, Moody’s Analytics CRDTM is the largest database of private company financial information and default rates in the world. It feeds into models based on ratios which have been proven to be highly predictive of defaults, and which therefore provide an effective early warning against credit deterioration.
4. Process efficiency and infrastructure. The time it takes for a lender to process a loan application and disburse funds is commonly known as “time to cash.” Many factors can impede time to cash, including market environment, decision makers, and system infrastructure. Where the process is too manual and there are too many systems, this inefficiency can cause slower response times. While installed systems and hardened data silos can make streamlining lending processes challenging, new outsourced or cloud platforms offer an attractive alternative since they require no new hardware, no additional IT staff, and are automatically updated and backed up.
5. Data, reporting, and audit. The ability to extract meaningful data to understand key performance indicators and for audit requirements depends on several things. First, a focus on exactly what data is the most meaningful is required. Next, a data structure or system that captures the right data in a user-friendly way is essential. And finally, strict discipline and well-defined processes are necessary to ensure that the data is accurately captured and maintained.
6. Problem loan management. Lenders who have effective processes for the early detection of distressed loans are most likely to minimize losses. Bankers should:
- Build good relationships with borrowers
- Have early warning triggers such as covenants in place
- Have a specialist team to manage distressed credits
7. Business model sustainability. Alignment between the market strategy of the business and sectors that have sufficiently strong creditworthiness is important to mitigate against losses when a particular sector starts to underperform. In addition to alignment with strong sectors, fundamentals such as making sure that the overall business strategy is viable in an increasingly competitive marketplace go without saying. But there are two more factors that come into play:
- Weak financial performance can lead to pressure to take greater risks and go beyond established risk parameters
- Many lenders do not fully understand the profitability of the deals they are underwriting
A strong risk culture, with low tolerance for behavior outside acceptable risk parameters, is essential to address the first point. To address the second point, robust models that feed into appropriate deal-pricing tools are necessary.
Keys to Success
SME risk assessment is a multi-faceted process with many challenges. Key to success in addressing these challenges are processes that enhance the analysis of quantitative and qualitative data, the quality of decision making, and the relationships between borrowers and lenders.
Helene Page is a senior engagement advisor with Moody’s Analytics, based in London. Contact her at Helene.Page@moodys.com.