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Credit scoring is a system used to rate credits and thus try to automate the decision making process to prevent the risk of non-payment. Traditionally, it has been done taking into account different variables that allow to to know the credit and solvency situation of the company of individuals or companies.
Credit scoring is generally used by financial institutions when deciding whether to accept a risk, i.e. to provide financing to a customer. It is even used by banks when offering a card with a credit limit to an individual customer. What the entity does is to evaluate the customer based on a series of data it collects about him, sometimes through an interview.
A good example is when a person applies for a loan or mortgage: the bank requires professional information (payroll, properties owned and even expenses for the last year) and personal information (marital status, family burdens, etc.) to, with these data, grant a score.
The entity has scales that determine when a potential debtor presents a high risk of non-paymentIn this case, it will decide whether to increase the interest rate or deny the loan outright.
There is a certain aura of mystery about the factors and variables that determine credit scoring of a person or company. And this is because financial institutions do not disclose the factors taken into account or the score they give to each factor.
Despite this, it has been possible to determine that the payment history accounts for more than one third of the weight of a person's or company's credit score. Another third falls on the credit utilizationi.e. the amount of credit consumed in relation to the total available for that particular customer.
And there are other factors, such as the credit history or credit applications madewhich also have an important impact on credit scoring.
In the first case, because the more extensive the (positive) relationship of that customer with credit, the greater the probability of obtaining financing, since the entities will lend money knowing that that person has been borrowing money for a long time and has always paid it back.
On the other hand, in the second case, if this customer has made numerous credit requests in a short period of time, without any justification to support it, it may be suspected that there are liquidity problems that force him to constantly refinance debt.
Although digitalization has long since enabled companies to use technology for credit scoring, the advance of artificial intelligence has changed everything. There are now platforms that allow applying artificial intelligence to credit scoringThis allows you to improve and automate processes to an unprecedented degree.
For example, it is now possible to design a credit scoring system that is updated in real time thanks to algorithms that collect and analyze creditworthiness information of a customer or supplier. Thus, when a company is going to do business with another subject, it will simply enter data as basic as the company name and automatically obtain a detailed credit scoring report.
This will make it easier (and quicker) to decide whether to go ahead with the business or whether, on the contrary, there is a risk of non-payment that requires the operation to be stopped or to be properly provisioned. Moreover, if technology has brought with it anything, it is that credit scoring is no longer reserved only for large financial institutions: even an SME can have its own system, tailored to its business and its customers, without the need to invent anything.
One example is the ARM SaaS (Advanced Risk Management), which enables small and medium-sized enterprises to use artificial intelligence to predict default risk. It is a customizable digital platform that automatically analyzes the characteristics of each customer or supplier, so that companies do not need to have a department in charge of collecting and processing information: everything is automated.
It is a system very similar to that used by large financial institutions to grant financing to their customers, except that it is available to companies of any sector and size. Thanks to platforms such as the GAMCO ARM SaaSSMEs can rely on a system for assessing the risk of non-payment in anticipation of circumstancesThis allows them to focus exclusively on their business and to better fine-tune their operations to make the right decisions in the shortest possible time.
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