Want to determine the credit risk of smaller businesses? Then the personal financial health of the owner plays a role. So for a creditworthiness check you actually need to understand the risk of the business and that of the owner or director. The solution? Mix credit risk information about companies with consumer data, possibly supplemented with your internal data.
As much as 70 percent of the Dutch business community consists of sole proprietorships, general partnerships and smaller companies. So there is a good chance that you also generate a (significant) portion of your turnover from them. Nice of course, but determining the creditworthiness provides headaches. Because how do you include the personal financial health of the director or owner in the credit risk picture?
If he or she is financially ill, weak or infirm, then you would rather not deal with the company. But even if the owner is known to be creditworthy, that does not mean that this company is the ideal business partner.
Combine business and consumer data
The solution lies in mixing data streams. Do you combine company data with consumer data? Then you get a complete picture of the credit risk. With company data you can think of payment and collection experiences, trading experiences, bank information and judgments on bankruptcies and moratoriums. And at consumer level, data about the creditworthiness of the owner, his or her home and any debt restructuring.
Mixing these data streams is widely used for creditworthiness checks. Dutch energy and telecom companies, for example, often work with the Altares Dun & Bradstreet data and solutions to screen SMEs who want to take out a business subscription.
Energy and telecom companies receive many requests from sole proprietorships, general partnerships and limited liability companies, whose owners take out business subscriptions at their home addresses because of tax advantages.
When applying for such a subscription, three data sources are mixed. Company data comes from the Dun & Bradstreet Data Cloud, the world's largest business database with real-time data on payment behavior, collection information, sales figures and balance sheets, among other things. The consumer data we provide is collected through our tradepartnerprogramma. These two sources are supplemented by information that the telecom or energy company itself already has, for example from previous consumer connections.
A customized decision model then gives all factors a certain weight, from which a credit score emerges.
Fully automated credit decisions
Now comes the best part: this is all fully automated. The rules for the decision model are defined in advance and loaded into the system. Because the data sources are integrated into the CRM, a credit decision is automatically made based on real-time data. All a front desk or call center employee has to do is enter the Chamber of Commerce number. A traffic light in the CRM then indicates the score.
The benefits of automation
The benefits are obvious. On a daily basis, your organization can make numerous credit decisions without the need for qualified staff. Credit policies are proactively enforced and consistently implemented, no matter who takes the case. And because a credit check can be done at any time, ongoing risk management becomes possible. Last but not least, the credit manager has time and resources to focus on the real problems.
Altares Dun & Bradstreet's customized scoring and predictive fraud indicator solutions help our clients automate customer acceptance checks and identify potential fraud early
signaling. The result? Decisions that match your risk appetite. Enormous time savings. And even employees without a finance or compliance background can now make credit decisions.
Want to know more about the possibilities of automating credit decisions based on multiple data sources? Then check out our solutions for automating the customer acceptance process.