Balancing the Impacts of Insolvencies: Why Visibility Over Payment Behaviors & Trends Is Critical

US bankruptcy filings surged by 18% last year, according to data from bankruptcy data provider Epiq AACER. Higher interest rates, rising material costs, and historically high levels of household debt contributed to a challenging year for many businesses. While some types of companies have fared better than others, the reality is that insolvencies can have a domino effect, impacting the flow of cash across sectors.

For instance, in the UK, recent statistics from the Insolvency Service paint a concerning picture for the construction industry: in November of last year alone, construction firms accounted for 17% of all insolvencies in England and Wales. With 4,370 construction firms becoming insolvent, the field is facing significant challenges. Bad news for construction, but what does this mean for companies in interconnecting industries or other parts of the sector?

Take manufacturing as an example. Manufacturers are integral parts of supply chains that span various industries, including construction. When an industry like construction experiences financial turmoil, the effects reverberate throughout the entire chain—from manufacturers to transport and logistics companies to retailers. Similarly, if a retailer fails, the logistics company may incur bad debt, affecting its ability to pay the manufacturer.

Any disruption along supply chains can have cascading effects which emphasizes the need for enhanced financial resilience across the board.

Proactive risk management and contingency planning are essential for businesses to navigate turbulent economic waters successfully. When facing the possibility of a customer—or even one of your customers’ clients—heading for insolvency, businesses can take proactive steps to mitigate risks.

One of these is closely monitoring the customer's payment behaviors and other financial health indicators. Early detection of warning signs, such as delayed payments or erratic purchasing patterns, is critical and can prompt businesses to reassess credit terms or adjust inventory levels accordingly.

In the complex web of supply chains though, detecting warning signs or red flags manually is a daunting task. Customer payment behaviors may subtly change over time. An organization that used to pay on day 30 may start paying on day 32 and then on day 33. They could then make an unexpected change to their payment method. These nuances can easily go unnoticed amidst a large volume of transactions—particularly if you have a small team that only has the capacity to look at whether an invoice has been paid or not.

This is why, traditionally, businesses have been forced into a reactive approach, only addressing issues after they escalate. BlackLine’s recent research revealed this as a common issue for finance and accounting professionals: manual processes consume excessive amounts of time, leaving no room for the bigger picture. Almost two-thirds (64%) of respondents to our recent survey stressed that the overwhelming volume of manual day-to-day work leaves little or no time for proper planning and analysis. At the same time, more than two-thirds (68%) stated that manual work leaves their organization vulnerable to errors that could undermine business decision-making.

Fortunately, a digital platform can revolutionize this process by automatically analyzing payment behavior. Using software for the manual data crunching means people can focus on what they’re good at: evaluating and understanding discrepancies and trends and using these insights for strategic decision-making.

With today’s machine learning capabilities, businesses can gain unparalleled visibility into their full invoice-to-cash process. This helps organizations easily see gaps between their cash forecasts and actuals and, importantly, understand why these gaps exist. The more data you feed into your model, the more accurate your forecasts become, which not only enhances preparedness for economic downturns but also fosters agility in responding to market fluctuations.

What’s more, looking forward, technology and its capabilities will only improve. In fact, the vast majority of C-suite and F&A leaders believe that advances in cloud computing (80%), generative AI (78%), and new kinds of AI (76%) will be essential for improving business resiliency in the face of future disruption.

Many people are optimistic about the potential for AI-driven models to offer unparalleled analysis of vast historical datasets and to generate forward-looking, predictive, and actionable insights. For example, better AI models could help organizations predict with near-complete accuracy when all their payments are going to come in. Accurate predictions around shortfall would help businesses understand when they genuinely need to borrow money, which, of course, comes at a cost.

Similarly, generative AI, while subject to a lot of hype, also has the potential to speed up analysis and decision-making. The beauty of generative AI is that you don’t need to be an expert coder to use it—it gives users the ability to query data in natural language. Think how much faster it would be to feed an AI model a huge financial data set and ask it simple prompts to show you the trends you need.

The volatility in industries like construction underscores the importance of financial resilience across supply chains. Manufacturers, as integral components of these chains, must prioritize visibility and preparedness to mitigate risks effectively.

By leveraging technology-driven solutions like digital platforms and AI-driven analytics, businesses can enhance their financial resilience and thrive in an ever-changing landscape. Now more than ever, proactive financial management is the key to navigating economic instability with confidence. BlackLine’s Consolidation & Financial Analytics tool can elevate efficiency, control, and transparency across your F&A processes.