For high-stakes domains, it’s not just about the code, it’s about risk mitigation! Aside from handling class imbalance using resampling or specialized loss functions, I would focus heavily on building a Human-in-the-Loop (HITL) framework. For edge cases or low-confidence model predictions, the pipeline should safely route the data to a human expert (like a doctor or fraud analyst) rather than making a blind automated guess.