✅ B. Unsupervised learning to identify transactions likely to be fraudulent.

Without labels indicating fraud, unsupervised learning, such as anomaly detection, can uncover unusual patterns or outliers in transaction data, potentially signaling fraudulent activity. Techniques like Isolation Forest or k-Means clustering are useful here.

✅ A. Clustering to categorize transactions into N groups based on feature similarity.

Clustering, an unsupervised method, groups data by similarity. Applying it to transaction data can reveal natural groupings based on type, location, and amount, aiding in understanding customer behaviors or transaction patterns.

✅ C. Supervised learning to forecast the location of a transaction.

With historical data labeled with transaction locations, supervised learning can predict future transaction locations based on user ID, type, and amount. This is a classification task for discrete locations or regression for numerical coordinates.

Incorrect Options: ❌ D. Supervised learning to detect fraudulent transactions.

Supervised learning for fraud detection requires labeled data, which isn't mentioned as available initially.

❌ E. Reinforcement learning to estimate the location of a transaction.

Reinforcement learning is for sequential decision-making, not static predictive tasks like location prediction.

❌ F. Unsupervised learning to estimate the location of a transaction.

Unsupervised learning discovers patterns but doesn't predict specific outcomes like locations without labeled examples.