Credit Engine vs. Score in ERP: Difference
Posted: Thu Jan 23, 2025 9:05 am
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 credit granting 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 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 facebook database 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.
Ideal credit limit and installment plan
Setting the credit limit and the ideal installment plan are two possible issues with the credit engine.
After pre-classifying customers, the need arises to generate the customer limit. This should be based on the risk profile and not only on income.
In Meu Crediário, for shoe, clothing and optics stores , for example, we do not even use income to generate limits, we use other variables according to the behavior profile.
What is the appropriate limit for the client? This will vary depending on the risk profile.
It is possible to bet more on customers who have a low risk of default. And, for customers with a high-risk profile, it is preferable to deny credit or give a low credit limit.
Another feature of the credit engine is to identify the ideal installment plan for this customer, that is, how many times will I let this customer buy on the store's credit plan.
For some people, it is possible to sell in 10 installments. For others, it will be preferable to do it in fewer installments, always depending on the risk profile that this customer has within the store, remembering that this varies according to age, information that may have come from a credit bureau, marital status, number of children...
Ultimately, there are several pieces of information that interact with each other to arrive at this analysis model.
In short, the credit engine is created in the following way: we read past history to try to identify what the future will be like.
In the Meu crediário model, for example, it is pre-configured, we deliver all these rules ready to the store, so that the business can start operating.
After a period of time, there is indeed an evolution of these configurations. But this initial configuration is entirely based on statistics.
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 credit granting 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 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 facebook database 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.
Ideal credit limit and installment plan
Setting the credit limit and the ideal installment plan are two possible issues with the credit engine.
After pre-classifying customers, the need arises to generate the customer limit. This should be based on the risk profile and not only on income.
In Meu Crediário, for shoe, clothing and optics stores , for example, we do not even use income to generate limits, we use other variables according to the behavior profile.
What is the appropriate limit for the client? This will vary depending on the risk profile.
It is possible to bet more on customers who have a low risk of default. And, for customers with a high-risk profile, it is preferable to deny credit or give a low credit limit.
Another feature of the credit engine is to identify the ideal installment plan for this customer, that is, how many times will I let this customer buy on the store's credit plan.
For some people, it is possible to sell in 10 installments. For others, it will be preferable to do it in fewer installments, always depending on the risk profile that this customer has within the store, remembering that this varies according to age, information that may have come from a credit bureau, marital status, number of children...
Ultimately, there are several pieces of information that interact with each other to arrive at this analysis model.
In short, the credit engine is created in the following way: we read past history to try to identify what the future will be like.
In the Meu crediário model, for example, it is pre-configured, we deliver all these rules ready to the store, so that the business can start operating.
After a period of time, there is indeed an evolution of these configurations. But this initial configuration is entirely based on statistics.