Anomaly detection can be a good candidate for machine learning since it is often hard to write a series of rule-based statements to identify outliers in data. Fraud detection is a type of anomaly detection, and presents two interesting challenges when it comes to machine learning:
Very imbalanced datasets: because anomalies are, well, anomalies, there are not many of them. ML works best when datasets are balanced, so things can get complicated when outliers make up less than 1% of your data.
Need to explain results: if you're looking for fraudulent activity, chances are you'll want to know why a system flagged something as fraudulent rather than just take its word for it. Explainability tools can help with this.