In Nigeria, small and medium-sized enterprises (SMEs) play a vital role in driving economic activity, employment, and supply chains. However, many SMEs face cash flow crises, which can be detrimental to both lenders and borrowers. According to Winston Osuchukwu, Founder and Chief Executive Officer of Mathesis Analytics Inc., traditional lending systems often fail to detect early warning signs of financial distress in SMEs. These systems, designed for point-in-time lending decisions, are not equipped to continuously monitor the financial behavior of borrowers.
Osuchukwu identified four key early warning signals that conventional models frequently miss. One of these signals is slower vendor payment velocity. When an SME delays payments to its suppliers, it may be an indication of weakening working capital. Although a single delayed payment may not be a cause for concern, a pattern of progressively delayed payments can be a different story. For instance, a business that historically settles its primary vendor on the 25th of every month may begin paying on the 28th, and over time, the payment could move into the following month.
Another early warning signal is the illusion of liquidity. A healthy account balance on the day a loan repayment falls due can create a misleading picture of a borrower’s financial health. Legacy credit systems that rely heavily on account-balance snapshots may see the required funds sitting in an account and conclude that the borrower has sufficient repayment capacity. However, this may not be the case if the borrower has borrowed from another lender or delayed payments to creditors to accumulate cash in its primary account.
Changes in the composition of business expenditure can also provide clues about emerging financial stress. A sudden increase in payments to a shrinking group of vendors, greater dependence on short-term financing, or a rise in transfers out of the primary operating account may suggest that working capital is coming under pressure. However, such patterns must be interpreted carefully, as not every unusual spending pattern is evidence of distress. For example, seasonal businesses may experience a dramatic concentration of expenditure during a short harvest period.
The deterioration of a business’s cash buffer is another indication of impending trouble. An SME may continue to pay its loans on time while its financial cushion steadily disappears. For example, a company that historically maintained enough cash to cover several weeks of operating expenses may begin ending each repayment cycle with progressively less money. Traditional systems are often designed to verify whether sufficient funds exist when a repayment is due, but they may not continuously monitor what happens to the borrower’s liquidity before and after that payment.
Modern cash-flow analysis is creating the possibility of a different approach, one in which lenders monitor the financial behavior of businesses continuously and intervene before distress develops into default. This approach involves using intelligent credit systems that can identify deteriorating payment patterns, provide a consolidated view of the movement of funds, and interpret changes in cash-flow behavior. By doing so, lenders can take proactive measures to mitigate potential risks and prevent defaults.
The use of advanced cash-flow analysis and intelligent credit systems can help lenders to better manage credit risk and prevent SME defaults. By continuously monitoring the financial behavior of borrowers, lenders can identify early warning signs of financial distress and take proactive measures to mitigate potential risks. This approach can help to reduce the costs associated with SME defaults and promote a healthier financial environment for both lenders and borrowers.
Key points
- Banks often miss subtle changes in SME cash flows before default.
- Traditional lending systems have limitations in detecting early warning signs of financial distress.
- Modern cash-flow analysis can help lenders monitor SME financial behavior continuously and prevent defaults.
