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Default is a persistent issue in the credit card industry. Lenders have limited capacity to analyze information around credit card applicants, making it difficult to identify all potential credit risks associated with a borrower. According to Wells Fargo, the largest U.S. banks can attribute 15% of their revenue to their credit card portfolios. Incorrectly judging applicants’ credit worthiness costs banks billions of dollars in revenue annually. A core growth strategy for banks involves increasing the number of consumers who are offered credit; however, the challenge lies in assessing the risk of entering into relationships with new credit card consumers.
AI provides a scalable solution for credit card lenders to judge default risk using predictive models developed on historical default data. In addition to the consumer’s credit score, AI models also consider a wider array of consumer data to make more accurate predictions on the likelihood that a credit card will go into default. Leveraging these predictions at the time of application decisions will allow your organization to parse out applicants who are unlikely to pay back their credit card debt. Advancements in machine learning interpretability also give you the top statistical reasons behind why each credit card is classified as high or low risk. This not only saves you from the cost of charge offs, but also helps you avoid extraneous costs with debt collection and other operational processes that otherwise would have been required.
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