AI model for credit risk reduces workload and speeds up customer response times at Boels Rental
Accepting new customers for rental on credit was a bottleneck, which created an accumulating workload for employees
The AI model supports an immediate decision in more than 60% of the rental requests, leading to substantially shortened review times, faster response to the customer and a reduced workload for the team
Seven weeks for project design, development, testing and implementation
At a glance:
Boels is one of the largest equipment rental companies in Europe. This includes both equipment rentals and specialist rentals. On a daily basis, Boels receives approximately 150-200 requests from new customers who wish to rent on credit. Each new request had to be reviewed manually by an employee.
The credit review process proved to be a bottleneck in accepting new customers for rental on credit. Customer requests accumulated, resulting in a higher workload for employees and longer response times towards the customer than desired. Boels took steps to automate the review process as much as possible and was looking for ways to automatically filter out (part) of the cases that can be immediately accepted or rejected. The goal was to reduce labor efforts, improve decision quality and up the speed of servicing customers.
“The original expectations have been exceeded, as in more than 60% of the cases the model can directly support a decision. Our employees are very happy with this, as customer requests are handled substantially faster than before. Also, it is beneficial for our team as we can focus on other tasks with the same capacity.”
Bjorn Curvers | Manager Credit Risk at Boels Rental