How credit score apps help get more accurate credit evaluations

June 25, 2021

New mobile credit score apps have reduced credit risk, opening up opportunities for customer segments that were previously ignored. In addition, thanks to alternative data and new machine learning technologies, Fintech companies are now ready to explore untouched markets.


A credit score, as traditionally defined, is essentially a numerical score given to people based on the details of their credit report and that allows lenders to determine their creditworthiness. Unfortunately, traditional scores only allow people within the banking system to access a loan. Thus, many cash-only individuals - such as Millenials, immigrants, or unbanked ethnic minorities - are left out of the financial circuit or are penalized for having to pay very high fees.


Classic credit scores are established by making linear predictions based on data from traditional credit institutions. They use information related to credit records, such as credit products, debt levels, payment habits, among others. However, technological progress has shown that using this type of information alone is not enough. This is because many trusted people are left out of the system and because there is a wide variety of data required for more accurate ratings that are not taken into account.


In this sense, new alternative credit scoring methods use more complete information about people beyond just their credit history. This data includes insurance payments, rents, good purchases, social network behaviours, etc. In addition, alternative scoring methods use artificial intelligence to process the data and instantly obtain secure prediction models.

Adopting scoring apps with alternative data leads to a positive impact for both companies and people. On the one hand, companies can have a more complete view of the financial profile of potential clients and reduce the risks of bad debts in just seconds. On the other hand, more people can access loans and credits without the need for credit history.