Credit Engine vs. Score in ERP: Difference

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Do you know the difference between a credit engine and a credit score?

There is often confusion between these terms whenever we talk about the Meu Crediário credit engine.

Although, at first, it may seem a bit like the logic of a ios database credit score, the Meu Crediário engine works in a very different and more assertive way.

To clarify this, you can watch the video or continue reading. Let's go?

YouTube video
What is a credit engine?
The credit engine appears in every automated credit granting operation. It is the technology that analyzes the customer's risk and determines safe limits to approve a sale on credit.

The credit engine is made up of several parts. One of them is the credit score – the best analysis model for your store network .

The credit score, in turn, is a statistical base created from various information from thousands of sales that have taken place. With this data, a statistical analysis is performed to identify the behavioral profile of these sales.

Depending on the system where the engine is installed, the analysis data is displayed in a simple credit score model that classifies the customer into different risk profiles. There is then a scoring, a score.

How the score works in the credit engine
In the scoring system, the closer the customer is to 0, the more likely they are to default.

In Meu Crediário, through this variation, we list, from a range of numbers, certain letters. These letters help you, the user, to identify that a customer with a risk profile of A is a good customer.

This letter-based classification system makes the process much easier than identifying, through the numbered score, whether customer 2673, for example, is a good customer.

The way we modulate in Meu Crediário is a statistical scoring, with the platform itself identifying it.

It is worth mentioning that, in the system, information such as income and age do not influence the classification of the client's profile as low or high risk alone. In fact, several connected pieces of information influence it.

In this sense, one change or another can have an impact. To illustrate this, just think about the zip code, which can have a greater or lesser weight. A peripheral neighborhood, for example, may have a higher default rate. Thus, this neighborhood causes the customer's rating to go down, regardless of other information.

And where does analysis fit into bureaus?
After obtaining a credit score, that is, the customer's risk profile, the system identifies what you should do with that customer. Do you need to look for information from the credit bureaus ? Maybe so.

In any case, the store will be able to make the sale to a customer with a risk profile of A, for example, even if he or she has a negative credit rating. This is because the system allows it because this is a customer who tends to have a default rate of around 1%.

But of course this question will vary from client to client. And all this means that the analysis at the credit bureau is a small part of the credit analysis.