How to create advanced Scoring with AI Search & Assign?

One of the foundations of the credit manager profession is the ability to assess the solvency and credit risk of a company's customers. There are several approaches and methods for doing so, which have evolved significantly in recent years thanks to the availability of information and the development of digital solutions that interpret this data using increasingly advanced and relevant algorithms.

Thus, financial information providers and credit insurers have become leaders in credit analysis and business scoring. The former offer services for sharing this information and its analyses, while the latter provide guarantees against the risk of non-payment.

Among the progress made, the integration of financial data as well as sector data and even companies' payment behavior has increased the reliability of scoring.

Thanks to our connectors, it is very easy to integrate and use this data from service providers in your My DSO Manager platform, as soon as you are registered with them as a customer.

Indeed, a credit analysis carried out by a well-informed specialized company is valuable, but what if you could combine this evaluation with the characteristics of your own relationship with the customer? Is it a strategic client or a new one, a long-standing partner with whom you maintain close relations, etc.? What has been your experience with this company?

Taking this information into account transforms a cold assessment, disconnected from your relationship and your business challenges, into a complete analysis that combines third-party information with your own customer experience and commercial priorities. The risk assessment therefore becomes far more relevant and leads to better-informed decision-making.

This is exactly what we offer with Search & Assign!

First step: define the rules for enriched scoring

Here is a simplified example on our demo platform My Company SAS, where an external score is enriched with your customers' "internal" payment profile.

My Company SAS integrates into My DSO Manager data from a financial information provider offering a risk class with four values: A, B, C, and D, with A being the best rating. Find the list of providers connected to My DSO Manager here.

In addition, the customers of My Company SAS are rather recurrent with many payment experiences, which enables the identification of a relevant payer profile. See how to configure payment profiles in My DSO Manager.

We will therefore combine this information to determine a score mixing external assessment and internal experience by creating several categories represented in the table below, with the external risk category on the horizontal axis and the My DSO Manager payment profile on the vertical axis:


Each box is filled with the desired risk category and created in My DSO Manager as separate Customer Groups, i.e. four categories: low, medium, high, and very high risk. See how to create customer groups.

Second step: implementation with Search & Assign

The approach consists of creating as many searches as there are cells in the table. That is, a search selecting customers who are "Very good payers" with an "A" risk category, another for "Very good payers" with a "B" risk category, etc. In this case, 10 searches must be created and saved. Then, in Search & Assign, in the "Groups" section, configure the assignment rules between searches and groups as follows:

Create the assignment rules as follows:

To obtain: Thus, My DSO Manager will automatically assign the correct category to each customer daily, or even several times a day depending on the frequency of data imports.

The collection agenda and the risk agenda and all reports can be filtered by this enriched risk category.

This enriched risk category can also be used for other settings such as the dynamic assignment of collection scenarios, the management of collection officers' portfolios, the activation of late-payment penalties, etc.

As you will have noticed, this is a simple and highly dynamic scoring system, automatically and seamlessly linking data that usually is not connected. And this is what gives it its value, makes it scalable, and all the more relevant since it is then used directly for other actions and settings.