Boels uses Altares D&B's current data to perform credit checks

As an entrepreneur, do you find yourself in situations where you have a job to do, but not the right machines to carry it out? Then, chances are you will come to Boels to rent the tools you need. Whether the rented equipment is a saw worth a mere hundred euros or a very expensive excavator, Boels performs credit checks on all its new and existing customers. That's when it relies on the data provided by Altares Dun & Bradstreet.

Boels Rental

2.500
Products
6.900
Employees
800.000+
rental items

“From now on, we can act proactively when it comes to credit risk. With the help of the data from Altares Dun & Bradstreet, we can make the right decision in every situation.”

Björn Curvers - Credit Risk Manager

1. The Challenge

Credit checks performed by humans

Boels is a Dutch rental company that has expanded to seventeen other countries through the acquisition of several companies in recent years. Thousands of people and organisations rent equipment from Boels every day. Until recently, credit checks, in other words checks to see if lessees are creditworthy, were performed by people. “Not sustainable,” was the shared opinion at Boels. A large part of those checks therefore had to be automated.

2. The Solution

Automatic integration into the ERP system

Frank Donkers is Business Process Owner at Boels. Among other duties, he is involved in automation, and oversaw the integration of SOA People software into the SAP ERP system about a year and a half ago. “Altares Dun & Bradstreet provides the data to load credit score cards into our ERP system, enabling us to make sound decisions. Will or won't a new or existing customer be granted a limit with us? And how high? The data cloud ensures that the data we need is uniform. Everyone sees the same data everywhere.”

75%

automated credit checks

24/7

Expert service

18

operations in 18 countries

The automatic link with the Dun & Bradstreet data cloud within Boels' ERP system marked a significant expansion in the portfolio of Boels acquisitions. It enables them to use international data within a single interface, which is convenient now that the company is growing significantly beyond national borders. Previously, Boels purchased data from various partners and competitors of Altares Dun & Bradstreet. The real-time, accurate data has proven reliable, and therefore convinced Boels that it could give exclusivity to Altares Dun & Bradstreet in many, but not all, countries.

According to Björn Curvers, his department now spends much less time on routine work. Curvers is Credit Risk Manager at Boels. “In human work, 80 percent of the requests would get an easy 'yes' or 'no' answer. We wanted to automate those requests, and we largely succeeded. This allows our people to do quality work. Now that they have more time for their core tasks, the quality of their work has also increased. Due to the high reliability and an optimal process, we now only have 25% exceptions that need to be examined qualitatively.”

Paydex

“The Paydex score gives us insight into the payment history of individual companies. For example, if a self-employed person has a bad payment history, he/she may not pass the check, or will have to accept different (stricter) lease conditions. This score is dynamic, because it is constantly updated. This gives us a good snapshot of a customer's current financial health,” says Frank Donkers.

3. The result

Efficiency and time savings

The bottom of line is that the data from Altares Dun & Bradstreet Boels saves a lot of time while significantly boosting efficiency. “The report alerts us more quickly to possible payment issues,” adds Frank Donkers. “This enables us to manage the requests from such a customer more efficiently, adjust their limit, or ask for a down payment.”

“It gives us control”, says Björn Curvers. “If there are a hundred customers, we can use the data from Altares Dun & Bradstreet to decide which three to focus on.” Internal data and Artificial Intelligence designed by a third party complete our set of tools. We've set up a score card based on our own risk appetite: the resulting risk score tells us what to decide and is in many cases automated. “From now on, we can react proactively, instead of saying afterwards: something went wrong here. With this collaboration we really took a step forward. Now, we can see what's coming and intervene in time.”

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