Explanation

This is a supervised learning problem because:

1. Labeled Data: The bank has historical data where customers are already labeled as "default" or "non-default." This means the target variable (the outcome we want to predict) is known for the training data.

2. Classification Task: The problem involves predicting a categorical outcome (default vs. non-default), which is a classic classification problem in supervised learning.

3. Why not other options:

   • Unsupervised learning (A): Used when there are no labels in the data (e.g., clustering, anomaly detection).
   • Transfer learning (C): Involves using knowledge gained from solving one problem to help solve a different but related problem.
   • Reinforcement learning (D): Involves an agent learning through trial-and-error interactions with an environment to maximize cumulative reward.

In supervised learning, the model learns the relationship between input features (customer characteristics) and output labels (default/non-default) from the labeled training data, then uses this learned pattern to make predictions on new, unseen data.