In recent months I have visited a number of companies that do not use credit information in their customer acceptance process, or in managing their existing customer portfolio. I also spoke to credit managers who, because of internal regulations, use the cheapest possible supplier just to comply with the internal rules without affecting their annual budget too much, or who have credit insurance and are convinced that this meant they no longer needed credit information.
All in all you could say that all these people see credit information as a ballast or at best a necessary evil. In recent years I have used information from various suppliers and have experienced first hand what the difference in quality can lead to in daily practice. (Spoiler alert: Cheap is often expensive!)
Roughly speaking, you could say that I have used credit information in three different applications. I'm happy to share some of the lessons I've learned in the process.
When entering into a new customer relationship, it is important that you know who you are dealing with. Of course, you'll get an idea of your client's creditworthiness by analyzing their financial statements. But don't forget that there are many companies that have no obligation to file and that annual accounts are by definition based on figures from a year ago. So the insight they give you is limited. It is therefore important to know how a company is doing at the moment. You can measure this by looking at the payment behavior towards other suppliers. By having a good picture of this, you can say something about the financial health of a potential customer. (And don't think of one late payment as a bad payer, but rather look at the trend in recent payment behavior).
In addition, it can help to look at the family structure around a customer. Not only does that show you if you are already doing business with other entities in the same group (Commercial opportunity vs financial risk) but it also shows you what potential risks are within the group that could have an effect on your customer. For example, think about the financial health of other entities within the group or a liability declaration by a parent company. Especially with international corporate structures, this can be crucial and it is therefore important to have the family structure well mapped out... and keep it that way!
So my advice would always be to look for a vendor that provides high quality data, and demonstrably maintains it. Also remember that the use of intelligent and high quality scoring models is essential in the interpretation of this data. The better and more refined these models are, the more accurate they will be in predicting the (credit) risks of your new customer without compromising on commercial opportunities.
TIP: Also feel free to ask for a retro analysis if you want to compare different providers. You'll see that quality proves itself.
Monitoring of existing customers
Once you have accepted your new customer, it only begins. After all, from that moment on your 'real' risk only begins. You start delivering goods, providing money, renting out objects, in short, doing business for which it remains to be seen whether your customer will eventually pay for it. To ensure that the risk assessment you made at the acceptance stage does not become a futile 'photo opportunity', it is important that you keep your customer's credit information up to date. Monitoring and receiving Alerts help to identify risks at an early stage. (Needless to say, the point applies here too that only timely and high-quality data will lead to the right Alerts).
Credit reporting agencies have developed valuable predictive indicators that help you get and keep a good picture. By proactively sharing this risk data with your (sales) team you not only increase risk awareness in the organization but you also help to spot commercial opportunities. After all, a positive change in the risk profile of your customer can lead to new commercial successes.
So involve the Sales team in the credit check and periodically share the credit information with them. You will see that they will start to look more critically at new prospects and get a better sense of risks and the potential impact on your organization.
To substantiate/add to a credit insurance policy
As a third example, I would like to talk about the use of credit reports in a situation where there is credit insurance.
Depending on the type of policy and the coverage it provides, there may be a "self-assessment limit. This is a credit limit that you may grant to your customer without having it reviewed in advance by your insurer. Insurers set a range in the policy within which that limit must fall, and require a credit report that "justifies" that limit.
Now I hear many credit managers say that they go for the cheapest provider in this... "after all, the risk is insured anyway." But in this they are mistaken.
First of all, in almost all cases you have an excess and the insurer will only reimburse part of the damage. (Usually 80% to 90%) The remaining amount is still for your own account. Depending on the nature of the relationship, this can be quite expensive.
In addition, when it comes to insurance with a self-assessment limit, it is actually interesting to use a high-quality credit report. Because the scoring models in these are much more accurate, you can take maximum advantage of the credit space they display. You will then receive a limit in more cases, which leads to more commercial space to do business.
Providers working with inferior data and scoring models will not be able to provide a large portion of the market with a limit because they do not have enough in-depth data to do so or their scoring models are not sophisticated enough to arrive at good ratings. This ultimately costs trade and therefore money!
From personal experience I can say that cheap is often expensive. Of course no one can predict the future and doing business equals taking risks. But you can take these risks in a responsible way, if you use the right data. So my advice is: choose quality! That choice will always pay off in the long run.