Aug 20, 2021
Credit scores are instruments used by companies to discover the solvency of potential clients. This numerical score helps elucidate the best candidates for a loan, rejects doubtful profiles and minimizes risks.
Through traditional ratings, the bureau determines this score based on information related to credit history such as: payments, current debts and the number of open accounts. That is, a person that wants to request a loan needs to have a sufficient financial history to be considered reliable. The issue with this system is that it is prone to a vicious cycle; bad grades determine a poor credit history, and with no credit or help, it is very difficult to improve this score.
Unlike traditional models, the new alternative scores are not based on the past, but on current performance data. In addition, they are much more flexible because they allow companies to decide how to create their credit scores by choosing which parameters to use for analysis. These modern solutions with artificial intelligence can include a wide variety of metadata such as: telephone, cable, mobile payments, online purchases, property records, profession, criminal records, pending debts, among others. In this way, these solutions have the ability to include more clients; even those who don't have a great credit history.
Alternative scoring can also reach new markets without an increase in credit risk. On the contrary, thanks to machine learning mechanisms, credit scoring fintech companies can obtain much more precise non-linear prediction models, which are adapted to their needs. In this way, the ability to understand the customer is improved which in turn helps make better informed decisions.
Getting to know the client as an independent individual is the key to new credit scoring methods. Through the possibility of accessing more and better customer data, companies can obtain a complete customer profile. This helps know the customer's needs and be a lot more effective when launching a product or service. It is not about trying something new and trying to investigate if it works, but about knowing the client in depth and offering what he needs when he requires it, avoiding all possible risks.
Another advantage of alternative credit scores, different to traditional scoring, is the speed of loan processing. Hand in hand with artificial intelligence, alternative scoring has the ability to analyze multiple profiles and offer a score in just seconds. This allows companies to obtain more clients in less time impacting several people simultaneously with custom made offers.
Credit scores are essential so that companies can have information about the client and reduce credit risks as much as possible. However not all scoring methods are the same. Alternative scores set apart from traditional methods through the use of technology and data that provide accuracy and speed of response. This new analyzed data is a source of opportunity for companies, allowing them to access new markets more successfully. As for clients, alternative scoring opens the door to the best of all scenarios, progress.
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