Your question is Handling Imbalanced Fraud Labels. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
You are training a binary classifier where the positive class is rare, as is common in fraud detection. A model with high accuracy can still be useless if it misses most true positives or floods investigators with false alarms.
How do you handle highly imbalanced datasets, which are common in insurance fraud detection?