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Futureproofing Your Credit Management Now

Futureproofing Your Credit Management Now 1

By Marieke Saeij, CEO, Onguard

The pandemic has forced a shift in day-to-day operations for the majority of businesses. In particular, finance teams have found themselves attempting to balance long-term growth with the need for resumption of payments from current customers.

Growth depends largely on answering the funding requirements of customers who need finance, while payments rely on customers emerging from payment freezes, often requiring ongoing help. The first half of the year saw digital transformation accelerate under the economic pressures of the pandemic as organisations sough to achieve rapid efficiency gains and underpin business continuity. With so many potential unknowns continuing to affect customers, finance teams must now focus on one critical area – future-proofing their credit management.

This is a critical initiative. Finance and specifically, credit management, concerns the entire organisation and in tough times, will be crucial to survival.

A three-pronged approach is required to ensure growth by transforming credit management 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.

Future-proofing with your 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.

Start with your own and move to third-party data when you need to

Businesses should start by using data from their own consumer base, such as their customers’ payment behaviour. This is not only more cost-effective, but risk profiles based on an organisation’s own customers can reveal more about future customers than data from other companies. The risk profile scores based on internal data will therefore have greater predictive value.

External data can be expensive, as pointed out last month (July) by McKinsey, but its use can strengthen an organisation’s own data resources, bringing a wider understanding of the market that makes for better decision-making. An organisation can combine internal and external sources as it evolves to best suits its needs.

The gains from this hybrid 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.

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 and divert 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.

Marieke Saeij

Marieke Saeij

Using individual insights based on consumer history, AI can even help identify the best time to contact specific customers. 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 will hinge on effective implementation of the right technology, the importance of personal relationships must not be neglected. A future in which all contact with customers is automated will soon become unprofitable in credit management, where personal relationships are all-important.

It must be recognised that 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 the totality of the relationship with the customer. A credit manager, for example, might know that a single call is all it takes to trigger payment from a certain customer. Yet as much as AI will achieve, it still lacks the emotional intelligence to pick up on these kinds of nuances and subtle differences in 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 outcome you want is easier. This means finance professionals need to adopt a hybrid approach that combines the best data-driven tools with a heavy degree of personal involvement. This is the most reliable way of ensuring optimal performance, profitability and customer satisfaction.

Conclusion

There is nothing more fundamental to business than getting paid, but times are changing and data-driven credit management is undoubtedly the future. There can hardly be any argument about it. Basing decisions on data insights generates far better outcomes, delivers a substantial edge on competitors and injects agility into a team.

If another global wave of virus-outbreaks or other sudden disruptions strike 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 proven automation and AI. Yet reliance on technology alone will not guarantee success. Organisations must continue to recognise the importance of human interaction with customers, who may want to see a face or hear a voice when times are tough.

Alongside the implementation of solutions that deliver results quickly and cost-effectively, organisations need a hybrid approach, that uses the best of the conventional world and adapts it to the data-driven future.

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