By Marieke Saeij, CEO, Onguard

The Covid-19 pandemic is forcing finance teams into a critical new balancing act. As businesses adapt to the new economic conditions, finance departments find themselves having to balance long-term growth with the need to restart the flow of payments from current customers.

Growth depends very substantially on meeting the requirements of customers who need finance. Payments, on the other hand, rely on customers gaining release from payment freezes, which often requires on-going help.

The first half of this unique year saw digital transformation accelerate under the economic pressures of the pandemic as organisations sought rapid efficiency gains and strove to reinforce business continuity. With so many potential unknowns still affecting customers as we go well into the second half of 2020, finance teams must now focus on one critical area – future-proofing their credit management.

This is a critical area for any organisation. Finance and specifically, credit management, concerns the entire business and in tough times, is crucial to survival. A three-pronged approach is required to ensure growth by transforming credit management so it becomes fit for the future. It consists firstly of the implementation of a data-driven strategy, secondly on increasing automation and deployment of artificial intelligence (AI), and thirdly, on retaining the personal touch.

A business should start with its own data

The advantages of being a data-driven organisation are increasingly appreciated. It is why more than three-quarters (68 per cent) of finance professionals in the Onguard 2020 FinTech Barometer, said their organisation is already undergoing digital transformation.

Credit management founded on data insights can help to reduce the days sales outstanding (DSO) and allow credit managers to create a better understanding of risk profiles. Identifying payment patterns from the data produces better risk analyses and the ability to anticipate trends. The finance team is more rapidly alerted to the first signs that a customer will not pay, for example. Staff can then step in to resolve the situation, approaching the customer to discuss invoice payment. Data analysis will also predict a prospective customer’s expected growth, chance of bankruptcy or payment behaviour. This is not a capability many organisations currently have without laborious use of manual methods.

Once they have these insights, finance departments can better advise management at the strategic level, elevating their role within organisations. But finance professionals’ insights may also help other colleagues. One such example is sharing risk information with account managers, which will allow them to better calculate whether or not to approach a customer for upselling or new business.

Yet despite all the discussion of digital transformation, most organisations still only use a portion of their available business data. This is as true in credit management as any other area. According to the Barometer, only seven per cent of executives think their own organisation is already data-driven. It means the focus in credit management, as in other departments, must be on exploiting an organisation’s existing data riches because this is the most efficient and cost-effective route to becoming data-driven.

For maximum return on investment, businesses must use data from their own consumer base, such as customers’ payment behaviour. External data can be expensive, as pointed out last month (July) by McKinsey, but its use may strengthen an organisation’s own data resources, bringing a wider understanding of the market that makes for better decision-making. As it evolves, an organisation can combine internal and external sources to best suits its needs.

Marieke Saeij

The gains from this approach are tangible and come as enhanced sales, improved products, better finances and more targeted marketing, supplying a better service that boosts satisfaction levels and leads to improved relationships. Using in-house data will not hamper development. An organisation can combine both internal and external sources as it evolves.

Automation and AI

No discussion of future-proofing can take place without consideration of robotic process automation (RPA) and artificial intelligence (AI).

RPA automates the hugely repetitive manual tasks in credit management that involve collection and collation of masses of data. Not only is this very time-consuming, it diverts skilled employees from more valuable work.

AI, however, is the group of technologies with more far-reaching potential, making smart use of all available data. It links everything from CRM and ERP system data, to all the cogs in the order-to-cash process. This includes linking accounts receivables management with data about customer acceptance and e-invoicing. AI integrates these processes, transforming efficiency and delivering new insights through its analytical power. For finance departments it will also link with recognised parties that provide credit information, as well as payment service-providers and an automatic payment processing solution.

This, however, is only the starting point. AI’s predictive capabilities help minimise non-payment risk, support the forecasting of cashflow and advise on follow-up actions. This includes, for example, whether individual customers will respond better to phone calls, or when there is no alternative to commencement of collection proceedings.

Using individual insights based on consumer history, AI can even help identify the best time to contact specific customers, preventing unnecessary calls if the customer is known to be unavailable. This will this dramatically improve operational efficiency and if customers are approached in the right way, at the right time, will enhance relationships and bolster retention.

The personal touch

Although the future of credit management is going to depend very heavily on effective implementation of the right technology, the importance of personal relationships cannot be underestimated. A company that automates all contact with its customers will rapidly find credit management becomes unprofitable, because personal relationships remain so important.

As much as a finance department needs to embed a digital culture, it cannot just rely on the technology to take care of everything. All finance teams must adopt a hybrid approach that meshes the best data-driven tools with a heavy degree of personal involvement. This is not a matter of sentimentality – it is the most reliable means of ensuring optimal performance, profitability and customer satisfaction.

No two customers are the same and each needs to be taken on their own terms. Although data provides insight into overall payment patterns, it does not reflect every aspect of the relationship with the customer. A credit manager, for example, might know that it only takes a single call to trigger payment from a certain customer. AI has immense capabilities but still lacks the emotional intelligence to pick up on the nuances and subtleties of character that make a difference. This matters, because customers will soon switch providers when service-levels drop or if they start to feel they are just being treated as a number.

One of the ironies, however, is that if an organisation has the right credit management solution, it will understand more about the customer and have a firmer basis for effective person-to-person interaction. If you know more about a customer, saying the right things to obtain the right outcome becomes easier.

Conclusion

The future of credit management will be driven by data. The evidence is overwhelming. Data insights generate far better decision-making and outcomes, giving any organisation a substantial edge on its competitors. But they also embed another vital attribute in uncertain times – agility.

If a further wave of virus-outbreaks or trade disruptions pummels the world economy, organisations need to be as agile as possible, ready to meet the challenges with credit management that is already future-proof. That requires becoming data-driven and the adoption of fully-tested automation and AI.

Yet reliance on technology alone will not guarantee success. Organisations must continue to recognise the importance of human interaction with customers. In fraught times they may want to see a face or hear a reassuring voice, confirming what they see on a screen.

Alongside the implementation of solutions that deliver results quickly and cost-effectively, organisations need to embrace this hybrid approach that blends the best of conventional methods whilst preparing them for the data-driven future.