Banks and credit card companies use credit scores to evaluate potential risk when lending money or providing credit. Traditional credit scoring uses a scorecard method which weights various factors including payment history, dept burden, length of credit history, types of credit used, and recent credit inquiries. This traditional method is based on broad segments and will deny credit to consumers without considering their current situation or other extenuating factors. Traditional methods may also give credit to consumers, called churners, who are “gaming the system” and taking out a large number of reward credit cards but are not profitable for the issuers. For credit decisions there is also the additional regulatory burden that banks and credit card companies must explain to the consumer why they have been denied credit.
AI is a great solution for credit scoring using more data to provide an individualized credit score based on factors including current income, employment opportunity, recent credit history, and ability to earn in addition to older credit history. This more granular and individualized approach allows banks and credit card companies the ability to more accurately assess each borrower and allows them to provide credit to people who would have been denied under the scorecard system including people with income potential such as new college graduates or temporary foreign nationals. AI can also adapt to new problems, like credit card churners, who might have a high credit score, but are not likely to be profitable for the card issuer. AI can also satisfy regulatory requirements to provide reason codes for credit decisions that explain the key factors in credit decisions.