You are building a binary classifier for a digital payments product to detect fraudulent transactions before they are approved. Fraud cases are rare, but missing them is costly, while too many false positives create friction for legitimate users.
How would you approach training and evaluating a model on a highly imbalanced classification dataset in this setting? Explain how you would handle the class imbalance, choose metrics, and make tradeoffs between catching more fraud and limiting unnecessary declines.