Nov 5, 2021
Up until a few years back, a credit score was only obtained through traditional credit scoring methods. Traditional credit scores or ratings result from a credit analysis performed by different credit bureaus, indicating whether or not a person or institution is soluble enough to obtain a loan. Previously, these traditional institutions were the only ones that had the ability to analyze and score the financial history of a natural or legal person.
However, nowadays with the emergence of advanced technologies and the democratization of information, new companies that have the ability to perform more accurate credit ratings have taken shape. These so-called alternative credit scores have been gaining ground over traditional scores due to their multiple benefits such as access to numerous sources of information, the possibility of making future predictions, their ability to prevent fraud, the opportunity to open up new markets, together with the capacity of continuous improvement.
One of the reasons traditional credit scoring methods have lost steam is that they rely on a single source of information, which is a person’s credit history. In contrast, new alternative scores derive from multiple data sources that proliferate automatically thanks to artificial intelligence algorithms.
A credit history is a record that indicates all the movements and states of a person within the financial system. It is a linear register which can see the past within the framework of the banking system but is incapable of outgrowing that context and understand the total behavior of a person in different aspects of their daily life. In contrast, new alternative scoring uses data enrichment systems that can complete, in real time, each person's profile with information from multiple sources, allowing companies to understand in a better way their prospects and make substantially more informed decisions.
Some of the data that an alternative credit rating may include are online purchases, bills and rent payments, social network behaviour, keyboard or mouse movements, among others.
Machine learning algorithms can collect information and are capable of learning and modeling non-linear behaviors that anticipate the prospect’s habits. In this way, alternative scoring has a greater ability to prevent risks and anticipate customers' needs by offering products that are in line with their wants. In contrast, traditional scores do not have the technology to offer this type of prediction since the data processing systems are different.
With the advancement of technology, fraud mechanisms have become more complex. That's why it's so important to have the right tools in order to detect fraudulent activities and to take action. Alternative credit scoring uses state-of-the-art technology that allows companies to stay ahead of suspicious behavior and set off the necessary alerts to mitigate risks. Instead, traditional scores require a much more arduous and manual process to find risk patterns.
Traditional rating schemes currently overlook people who are trustworthy but don't have a good credit history. However, alternative scoring offers new possibilities to people who are soluble but previously ignored, such as informal workers or students who are still young and don’t participate in the banking system. Thanks to alternative data, companies can obtain preciser credit profiles and make informed decisions and incorporate all of these currently forgotten people into the financial market.
Unlike traditional credit analysis, alternative credit scores can process information automatically and in real time without people´s supervision. At the same time, artificial intelligence algorithms have the ability to continuously learn, modify and complete the models with new data obtained.
Throughout history technological discoveries have produced changes in our undertakings and notions, surpassing previous versions. Who would write a book on a papyrus nowadays when there are laser printing systems? Like the printing press, new forms of credit scoring have come to stay and overcome or complement traditional models.
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