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The credit industry is one of the most data-driven industries, which makes it ripe for artificial intelligence involvement. The use cases for the technology come with exciting advances, as well as concerning consequences. Either way, artificial intelligence will certainly change the credit industry. There were a few news stories that caught our eye this week that you should know about.
Tackling Legal Precedence with AI
The use of Artificial Intelligence is expanding at an extreme rate, and with that comes court cases trying to keep up in regulation. A supreme court case, Gonzalez vs Google, focuses on content recommendations and algorithms used by sites like YouTube. The decision of this case will set precedence for future legal liability companies can expect with AI recommendation technology, and how courts deal with AI-generated content. The case reaffirms the importance of consumer protection and noted that if a digital platform can recommend things to users with immunity, they need to design more accurate, usable, and safer products.
Content recommendations are primarily based on a user’s previous behavior with similar content, i.e. likes, clicks, views, etc. However, the concern is that these content recommendations are arranged and promoted based on ancillary indicators in data gathered from similar users and aggregated to influence behavior. For example, based on data gathered, users trying to build their credit could be advised to purchase a property in order to build wealth and establish a healthy mortgage payment history. While this may be a possible credit building opportunity, not everyone is financially able to purchase a property and doing so could be detrimental to their assets. It is imperative to understand the level of trust we give recommendation algorithms because recommending content is one thing, but recommending harmful or erroneous actions is another.
How AI Could Help Millions Get Access to Credit
Over the next few years, we expect to see many positive use cases for generative AI. The most viable and valuable are believed to be in personalization, credit scoring, payment optimization, and fraud detection. Machine learning algorithms can analyze alternative data sources such as utility payments, telecommunication behavioral metadata, or social media activity to determine a person’s creditworthiness. This way, more people could gain access to credit and credit scoring could be done more objectively.
However, AI is only as good as the data it is given, and if there was bias in the data used, the bias can be perpetuated. AI bias can manifest itself in many forms within the segment, ultimately resulting in discrimination against certain customers and rejecting their application for credit on that basis, whether it’s trying to secure a mortgage, a car loan or something else. As long as companies are intentional about the data they gather and how it is used, there is possibility for significant benefits of using this technology for credit scoring and building.