This article contains:

For a financially sound company, the Credit Manager is perhaps the most important function. This importance is only increasing. Artificial intelligence, machine learning and big data are increasing the added value of the Credit Manager. Whereas previously the emphasis was on operational excellence, with the help of AI, ML and big data, the Credit Manager function is evolving towards inspiring business strategists who advise the management team and identify market trends and earning opportunities. 

A year ago, Gordon Moore died. In 1965, he predicted that the number of transistors on a chip would double every two years. This has become known as Moore's Law, the measure of exponential growth within the chip industry. In other words, a doubling of computing power every two years. With enormous (computing) power, modern technology is storming towards us.

This is well illustrated by the meteoric rise of artificial intelligence. From 'nowhere', it is suddenly everywhere. Everyone is talking about AI. The exponential growth of computing power has everything to do with this. More and more data are becoming available and the arrival of powerful processors and graphics cards makes it possible to train and use more complex AI models. Thanks to open source software, anyone can experiment with it and all kinds of tools are 'public'. The more massively we use them, the smarter they get.

AI and other new technologies are not lacking in the field of credit management either. In 2024, credit management is far from being fully automated, but developments are moving fast, which is not surprising given the many administrative and repetitive routine tasks in the field. Credit management software is getting smarter and smarter, and links to data providers, CRM and accounting systems are easily made.

  • AI algorithms can analyse large amounts of data to assess credit risks more accurately than human analysts.
  • Chatbots and virtual assistants can filter credit applications in advance and answer simple questions.
  • AI can be used to automatically send payment reminders, draft collection letters and contact defaulters.
  • Chatbots and virtual assistants can provide 24/7 customer support, answer simple questions and solve basic problems.
  • AI can identify fraudulent credit applications and transactions.
  • AI can automatically generate reports and dashboards with credit analysis. AI-algoritmen kunnen grote hoeveelheden data analyseren om kredietrisico's nauwkeuriger te beoordelen dan menselijke analisten.
Even if plenty of (complex) human work remains, the trend is evident: AI is a valuable tool that Credit Managers cannot ignore. Indeed, they will have to manage this far-reaching automation. It is therefore important that Credit Managers do not see the new technology as a threat, but as an enrichment of their profession. Collaboration with data analysts and other IT professionals is a natural part of the job. For today's Credit Manager, a digital mindset is essential. Knowing what is going on in the technology field and what tools there are to exploit.
"However dominant the new technology, the Credit Manager's input remains crucial," says Marcel Pheijffer, professor of Nyenrode Business University and Leiden University. He stresses how important human input remains. "AI can do a lot, is strongly developing and increasingly dominant in companies. Companies are going to lean on it. To manage this, it is crucial that the Credit Manager knows how to correctly interpret the outcomes of AI processes and translate them to stakeholders. Especially in the current tumultuous development phase of AI, it is necessary to thoroughly check both inputs, algorithms and outputs. As data quality improves, that check might be less extensive, but you will always need to keep checking."
As far as Pheijffer is concerned, there are two areas of concern in the application of AI both on the front and on the back end where the expertise of the Credit Manager is indispensable. "The integrity of the data must be assessed. What data do you put into the AI tool and what is the quality of that data? The analysis of AI does need to be reliable and that hinges on the integrity of the data. The quality of the input determines the quality of the output. Someone will have to monitor that. And by extension, what are the operations of AI; are the algorithms on which the calculations are based correct? That too is for the Credit Manager to monitor properly."

So on the front end, it's all about the data and the process; on the back end, the result has to be assessed with the right glasses on. "As a professional, you will always have to ask the question whether AI delivers what you need. Does the result match your own expectations and insights? Credit managers should not let anything be foisted on them; they will have to recognise themselves in the AI result. If that recognition is not there then you should not take it for granted. You have to trust it. One's own experience and expertise should always be used as a dipstick."

"Once Credit Managers have ensured that the data is in order and the algorithms are correct, they will have to translate the results into concrete proposals and actions based on their professional judgement. They have to make the technical language understandable to colleagues in the company who have to work with it. Wonderful what can be done with AI, but if it does not land in the company, it is of little use. It is up to the Credit Manager to deliver the results in such a way that they can be used effectively in practice. Communication skills become even more important than they already were."

Pheijffer notes that Credit Managers are professionally used to thinking and working from models. "The possibilities of AI are an extension of this. AI goes deeper, further. In a natural way, it enriches the value of credit management within the company."

Sales on credit are originally designed to boost a company's sales. In other words, a process for a company to achieve its strategic and commercial goals. AI enables credit management to add even more value. Less a stand-alone process, but more broadly integrated into business processes. In which the focus on the external environment also becomes increasingly important. The availability of increasingly relevant information prompts Credit Managers to share data on opportunities and financial risks with prospects, customers and market segments with the other departments within the company.

Credit Managers broaden their focus and seek connection with strategic agenda items to contribute to the success of the business. They provide input to establish the risk appetite appropriate to the company's goals. They provide the MT with solicited and unsolicited strategic advice. They speak the board's language and create insights on which management can base decisions.


Pheijffer: "From the point of view of the sales department, boundaries are often sought to exploit as many opportunities as possible. But this has to be done responsibly. There is always that field of tension between commercial growth ambitions on the one hand and risk appetite on the other. The Credit Manager plays a key role in this. The salesperson's foot is always close to the accelerator, while that of the Credit Manager also uses the brake if necessary.

What you see now with AI is that more specific knowledge is becoming available. This can provide additional leeway that can be utilized. The Credit Manager can therefore provide new scoring opportunities from AI's analysis.

By communicating and collaborating openly and effectively, Credit Managers create valuable synergy. On the one hand, thinking and acting strategically to support the financial stability and growth of the company; on the other, proactively looking for earning opportunities. From risk-thinking, this creates a link to yield-thinking.
Whereas in the past credit management was often an island unto itself, today Credit Managers build relationships. They have a lot of knowledge to share. New technology enriches the insights that emerge from corporate data. For instance, machine learning can identify patterns and trends in payment behaviour, which helps predict customer behaviour. This allows companies to act better and more proactively. When the predictability of possible payment delays, for example, becomes greater, it is possible to respond to them earlier (less reactive).
By networking and building relationships with customers, colleagues (from sales to marketing and customer service) and other stakeholders, they can monetise their expertise and added value. The Credit Manager is more than just a financial analyst. The Credit Manager of the future is also a people manager with commercial flair, who can convince both internal and external stakeholders of the importance of a sound credit policy.
We often hear that AI costs jobs, but at the same time it also puts professionals more in their power. As automation advances, the human behind the Credit Manager also comes to the fore more. There is more than just 'operational excellence'. A company's profitability and sometimes even viability depend to a large extent on the Credit Manager's result. A result that cannot be achieved only by automating a process to the maximum extent. In doing so, you lose sight of the human factor that is so important for customer satisfaction and thus for (recurring) turnover. The Credit Manager of the future The Credit Manager of the future has resources and means to serve each (large) customer in the best possible way and maintain the relationship. Something we cannot and need not count on with data and software.  
The Credit Manager is increasingly a connecting factor between the company and its customers, both at the beginning of the chain when credit needs to be extended and at the end when payments are not made on time. Human interaction is indispensable because credit management and lending essentially rely on trust, and that trust requires a human face.