The lending industry has been integrating artificial-intelligence technology into the mix over the past few years. Access to new consumer data points — including purchasing history, bank data, utility information, and social media habits — has led lenders to seek ways to improve the accuracy of their decisions.
In the past, lenders generally used consumers’ credit reports before deciding whether to approve a loan. Now the industry has moved toward considering more variables, which artificial intelligence allows them to do.
In this article from Forbes.com, the author says, "AI and machine learning could streamline the process for developing a portfolio by assessing a customer’s goals and risk tolerance to develop an individualized portfolio -- and, according to Wired, several companies are already working with this technology. In order to complete this task, an algorithm could analyze information — such as the age of a customer, their income and current assets — before spreading the customer’s assets across investments based on data predictions."
Another great reason private lenders have begun opting for AI technology has to do with security. Security can be compromised in various ways now that information is shared through digital platforms, but AI and machine learning processes can ensure a greater level of protection. Routine checks of risk factors that could affect customer information can provide an understanding of the potential threats.
As a result, the response of initial invasion detection can be quicker, and the security at financial institutions can flag the unusual behavior for monitoring. (Vian Chinner explored this idea in a recent article.) AI and machine learning can also replace passwords with personalized data like the recognition of a customer’s voice or face.
The benefits of machine learning extend to populations who struggled in the past to establish credit. For instance, some consumers may not have recent credit or may not have credit at all. Reports say that machine learning has developed ways to conduct risk assessments that could accurately predict credit scores for consumers, which could allow underserved consumers the chance to present themselves with credit profiles. Consumer lenders, in turn, could gain a competitive advantage over other institutions using traditional credit scores, because machine learning scores and targets consumers that may not have been identified before.