The prompt mixes a regression target with precision and recall: untangle it, and pick metrics and a modeling plan tied to the real decision.

Ask what decision the output drives. Approve or decline is binary, so use precision and recall at the operating threshold, set by error costs. How much, like a loss amount, is regression, judged by MAE or RMSE.

Often it's both: predict the probability of default and the loss if it happens, and combine them into expected value per decision. Agree the metric with stakeholders before modelling, and validate on a time-based split.