Estimating default risk from historical data
Credit scoring models estimate the probability that a borrower will default on a loan, using historical data such as past repayment behavior, income, existing debt, and other financial signals. AI-assisted approaches can pick up on more complex, nonlinear relationships in this data than traditional scorecards, potentially improving accuracy, but that added complexity comes with a cost that is unusually consequential in this specific domain.
The output of a credit model isn't just a technical score, it directly decides whether a real person gets a loan, at what interest rate, and on what terms. That direct link between a model's output and a person's financial life is what makes this one of the highest-stakes applications of predictive modeling in everyday use.