Credit score: analyze your customer's risk in an automatic and customized way

Even with the great technological advances in the field of Artificial Intelligence, most Brazilian financial institutions still operate with archaic credit score models. This delay means that credit is made easier for people with more assets, and the lower social classes are the ones who consume the most in the country.

Last year's survey by Serasa shows that Brazilian banks usually deny 44% of applications for loans, financing and other financial products for those earning less than five minimum wages a month. Among requests from those earning above this amount, only 18% are rejected.

During the pandemic, many used credit as a solution to financial problems: 79%, according to data from Serasa, sought this solution to pay off debts.

Those with higher incomes and who have assets as collateral are the ones who have the most access to credit, according to the Central Bank's 2021 Financial Citizenship Report. The same document points out that only 49% of Brazilians have access to credit.

This data shows that, in addition to authorizing credit in a disparate manner, financial institutions may be exposing themselves to more risk and still losing sales because of their current credit score tools.

Generally, these are generically constructed models that don't reflect that institution's market niche. In other words, they don't match the payment behavior of the company's customers. What's more, they are usually expensive.

Our credit score evaluates a company's clients according to the data of that specific business. This guarantees more assertive results, reducing default costs and, of course, generating an increase in sales. 

4KST Credit Score: how it works

The Credit Score is a credit score that consumers - whether they are customers at a store or borrowers at a bank - receive from a large set of data that is collected and analyzed by tools based on Machine Learning.

Machine learning is a branch of Artificial Intelligence that allows predictive models to be built to help make financial decisions, whether in financial institutions, industry or commerce.

Let's get a better understanding of how 4KST's Credit Score tool works by taking a bank's credit granting policy as an example.

Specifically, the tool compares the profile of new customers interested in taking out loans with the profile of old customers, with the aim of trying to predict the behavior of new customers. 

The difference with our predictive model is that it can be customized based on the data provided by the client (retailer or financial institution) to guide machine learning, whereas credit bureaus use generic models, never oriented to the client's own niche.

Personalization has two advantages: the service is cheaper than credit bureau scores (such as Serasa, QUOD and Boa Vista) and it is more accurate because it is based on the customer's reality.

After analyzing the risk profile, the 4KST Credit Score assigns the customer a score on a scale of 0 to 1,000 that will indicate, with a high degree of accuracy, whether or not the borrower will be a good payer.

Of course, with this in hand, financial institutions can develop personalized and more precise credit granting policies that will increase sales and significantly reduce exposure to delinquent customers.

Are you interested in the 4KST Credit Score? Find out more about our packages and hire our tool now!