Late payments have haunted the small business sector for years. With strict deadlines on outgoing payments, from wages to supplier bills, small businesses often face long waits to be paid themselves – throwing the viability of their business into question.
Action to strengthen the Prompt Payment Code was declared by the government in January 2021. But, just a year on from this announcement, 440,000 small firms could be forced out of business by the UK’s late payment “crisis”, according to the Federation of Small Businesses (FSB).
Following yet another year of lockdowns and restrictions, many small and medium-sized enterprises (SMEs) are relying on a surge in consumer demand in the New Year to bump up their bottom line. As margins continue to tighten, it’s more important than ever that small businesses have a comprehensive view of any potential risks in advance.
Leveraging data and using predictive analytics are small steps that can have a considerable impact on helping SMEs mitigate risk while navigating a turbulent market. In this article, I will discuss how SMEs can weather the coming years with the help of technology to reduce late payments.
What’s the impact of late payments on SMEs?
Our research found that over half (54%) of SMEs said late payments critically impact their business, and 51% reported that late payments affect the productivity of their business.
Astonishingly, over a fifth (22%) has needed to use personal savings or assets to cover the shortfall of late payments – a percentage that has increased from 2019, when only 13% of SMEs needed to use their savings.
In short, late payments can have significant repercussions for SMEs. And the situation continues to plague the sector, with a third of small businesses (30%) reporting an increase in late invoice payments in the last three months, according to the FSB.
When 51% of SMEs say their solvency is reliant on suppliers making payments on time, it’s clear the issue of late payments has become a “crisis” that threatens the jobs of more than 13 million people in the UK.
There are a few ways SMEs can stay protected financially, and one of the most impactful is to leverage existing data and use predictive analytics to make confident informed business decisions.
In fact, when it comes to data, our study revealed that over half (52%) of European businesses don’t think they will survive without relevant, up-to-date, and compliant business data, showing how important it is to have an eye on the metrics. So, for businesses, it’s easy to see why this can be the first stumbling block to understanding what’s critical information and what’s not.
The ability to navigate an overwhelming amount of data in order to make decisions is fundamental to the success of businesses today. Deploying technology that can process and leverage data, and then generate precise and perceptive analysis is essential to take advantage of opportunities and mitigate risk.
Not only does predictive analysis of data enable businesses to gain a detailed understanding of any previous payment behavior, it can also anticipate future performance to circumvent the potential impact of late payments on cash flow.
SMEs don’t have to go it alone, technology can help
One of the biggest barriers to accessing data and using it to support predictive business analysis is the use of manual payment processes. Whilst processing payments is essential to understanding cash flow, manual operations can be resource-heavy and labor-intensive, as well as potentially vulnerable to human error.
But there are ways to enhance manual processes. Intelligent receivables management, for example, uses data to analyze and predict, transforming accounts receivable processes with automated workflows, AI-driven analytics, and powerful risk-based strategies.
Intelligent receivables management enables curated data to pinpoint when payments are expected – and most importantly when they are at risk of being unpaid.
With this analysis, those in charge of finance at SMEs are well equipped to tackle the situation by tracking the “time to cash” (from shipping the goods to receiving the money in the bank). Tools that enable predictive analytics to enhance the business’ understanding of the financial health of customers – for example, whether they’re financially capable of making the payment quickly.
But it’s not just about harnessing data for payment visibility, predictive analysis can also assist the collections process through automation.
When data is segmented, differentiated, and prioritized – essentially, harnessed to its full potential – it enables those in charge of finance to establish exactly how much is owed and how a situation should be addressed.
So, when dealing with collections, data is key to quickly uncover the ‘receivables trinity’: the size of the debt, the age of the debt, and how risky it has become.
This knowledge, powered by automating data processes, will ensure business efforts can be focused on making the most of any financial situation and the time available – a key priority for SMEs in the battle against late payments.
Visibility, analytics, and automation are key to beating late payments
In essence, a data-first approach supported by predictive analysis is the only true way to bridge the gap from credit to cash, reducing – and hopefully eliminating – debt created by late payments. Ultimately, for SMEs to get on the front foot, a full view of all existing customer and supplier relationships is necessary to assess any potential impact and identify new opportunities – ensuring continuity as the UK economy continues its road to recovery.
By Tim Vine, Head of International Finance & Risk Solutions at Dun & Bradstreet
