Example

When assessing the creditworthiness of a customer, a variety of criteria is taken into account. Assessing a customer’s creditworthiness does not only involve checking their financials, it also includes the consideration of the sector in which it operates and the legal form it has, for example. Many data sources are available to support organizations in assessing a customer’s creditworthiness. The most used sources are Chamber of Commerce documents, third-party credit information and data from one’s own ERP system.

However, most sales departments still rely on the assessment of the available data by their respective finance departments. The latter has access to the necessary data and is familiar with the criteria (and rules) that are important in their decision making. Considering the necessity of this specialist knowledge, you should ask yourself to what extent it is desirable to make a sales employee responsible for assessing a customer’s creditworthiness, in the first place.

By effectively implementing ones decision-making strategy and the available data in a user-friendly application, non-financial professionals become instant decision makers. It allows them to make sound decisions, which are based on knowledge and expertise from colleagues in finance.

Example

The assessment of a customer’s creditworthiness can be rather arbitrary. One financial expert might feel a customer’s payment behavior is the most important thing to take into account, while another believes a customer’s net worth should have the most influence on the outcome, for example.

And how about a credit manager who is being asked to assess the creditworthiness of a construction company, while a few minutes ago he read about a construction firm that was forced into liquidation? I am pretty sure this will have a negative effect on the assessment and might even result in an unnecessary rejection of a healthy customer!

With a scoring model, gut feelings and guesswork are a thing of the past. Thanks to the fact that one’s decision-making strategy is implemented and only predetermined parameters are used, scoring models ensure objective, uniform decisions.

Example

Decision strategies can vary greatly because no organization is the same. Organization A might think you should always avoid taking risks (low-risk appetite), while Organization B believes taking risk is inevitable if you want your company to be successful. Therefore, a generic method for assessing a customer’s creditworthiness will never perfectly match one’s decision strategy.

Scoring models allow organizations themselves to determine which rules must be followed and which criteria are taken into account while assessing a customer’s creditworthiness. Consequently, scoring models are ideal for organizations that want to make sound decisions in accordance with their specific decision strategy.

Example

As noted earlier, most sales departments still rely on the finance department for the assessment of a customer’s creditworthiness. But what if the financial expert is not immediately available or (because of the complexity) an extensive analysis needs to take place first? There is a big chance your customer already ordered somewhere else, while you were still waiting for your colleague.

And how about customers that want to place an order in your webshop on a Saturday afternoon? Should they wait for your response on Monday? Or do you agree it should be possible to place an order without human intervention, while the webshop does all the work for you?

Not only does a scoring model allow non-financial professionals to make sound and fast decisions, it also offers opportunities to further automation of your decisioning process. Scoring models can be integrated with existing platforms, such as webshops and ERP systems. Highly efficient, also outside office hours.