Share it with your network!
Help your friends to new knowledge
In uncertain economic times, payment defaults can be common. To counteract this, the global life-science group Bayer has automated its credit management processes and, with the help of Dun & Bradstreet, significantly increased its efficiency and the quality of risk assessment.
High inflation, rising interest rates, unstable supply chains, geopolitical upheaval – we are living in turbulent times. One crisis follows another. What holds true today may well be called into question tomorrow. This makes it essential for companies to strengthen their resilience and ensure their own liquidity. A decisive success factor is keeping a constant eye on the creditworthiness of customers.
To this end, the global life-science group Bayer has established a largely automated credit management process. The team, headed by Andreas Wenzel, Global Credit & Customer Finance Manager at Bayer AG, is pursuing a “smart data purchase” approach for the process. What exactly does this mean? “We send an automated request for different data from Dun & Bradstreet depending on the credit limit. For customers with a low credit limit, we use master data and payment behaviour. For a comprehensive credit check of major customers, we use special risk data,” says Wenzel.
All information is transferred directly to the SAP Enterprise Resource Planning (ERP) system via an Application Programming Interface (API), compiled in an individual scorecard with internal information, and evaluated. In the dashboard, credit managers at Bayer see the most important key figures for a customer aggregated at a glance (including credit scores and finances).
The internal and external data on each customer are categorised and evaluated in the scorecard. The rule set applied here is based on statistical analyses and comprehensive data on the Bayer customer portfolio. The focus is on determining company characteristics (key figures, trends, industry) that are typically associated with payment problems. In practical terms this means that the system uses the underlying data and rule sets to calculate the percentage probability of payment defaults and sorts the customers into different risk classes on this basis.
“As both our own internal data and external data from Dun & Bradstreet go into our scorecard, our risk assessment is far more reliable. This makes a huge contribution to the high quality of our risk assessment,” says Wenzel.
What’s more, the Data Blocks from Dun & Bradstreet make it possible to call up only the data that Bayer really needs for the respective risk assessment. The data elements, which are topic-based and logically linked, can be compiled as required and, in addition to master data, also provide information on aspects such as the financial strength and payment behaviour of a customer, indications of corporate affiliations and locations as well as relevant stock market news and industry developments.
“In times of economic uncertainty, it is more important than ever to keep a regular check on the creditworthiness and payment behaviour of customers,” says Wenzel.
Process automation creates the basis for permanent customer monitoring – and thus for efficient and sound credit management.
“Many companies only check the credit rating of their customers once a year, whereas we do it continuously. As a result, we can identify potential payment defaults far more reliably and, more importantly, much earlier.”
This is how it works: If critical data situations come to light during the continuous check by the scorecard, the relevant employees are immediately informed via a message on the dashboard. This can happen, for example, if a customer slips into a worse risk category because they have paid invoices too late or weak financial figures have been published. In these cases, the credit manager responsible takes over and initiates appropriate measures. If necessary, the sales department is contacted, the credit limit is reduced, payment terms are adjusted, or the delivery may even be stopped.
Since the individual scorecard was introduced, the credit managers at Bayer have been focusing on personally monitoring high-turnover customers with poor creditworthiness. Customers with a low default risk, on the other hand, are handled automatically. This aims to reduce the number of blocked orders and specifically drive growth at Bayer. “All in all, we have been able to considerably improve our efficiency in credit management by automating the processes,” says Wenzel.
At Bayer, additional risk data from Dun & Bradstreet is included in the creditworthiness check of high-risk customers. This is meticulously checked and thoroughly evaluated by the credit managers. “The structures of large companies and corporations are highly complex and require greater attention. Balance sheets need to be closely scrutinised, stock market news read, affiliations sorted through,” explains Wenzel. In his opinion, this is the only way to make watertight credit decisions worth millions of euros.
That's why a comprehensive analysis of the portfolio was first on the agenda at the beginning of the automation process. Which customers’ credit checks can be automated? What threshold values are needed and what needs to be considered when calibrating the scorecard? "In clarifying these and other questions, we relied entirely on the technical expertise of Dun & Bradstreet – and we did very well,” sums up Wenzel.
In the meantime, the project team has established the automated credit management internationally. This enables the Bayer Group to make reliable credit decisions even in highly volatile times and to reduce debtor risks in a targeted manner.