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FRM Part II · FRM Exam Part II · Advances in Artificial Intelligence: Implications for Capital Markets Activities

A bank wants to group its corporate clients into segments based on trading behavior, with no predefined labels for the segments. Which machine learning approach is most appropriate?

Unsupervised clustering such as k-means is appropriate because there are no predefined labels. The goal is discovering natural groupings in client trading behavior, whereas supervised classification or regression requires labeled outcomes, and reinforcement learning requires a reward-based environment.

  1. ASupervised classification using a logistic regression trained on default labels
  2. BUnsupervised clustering, such as k-means, to find natural groupings in the dataCorrect
  3. CReinforcement learning with a reward for each correct trade
  4. DSupervised regression on a continuous target such as next-day price

Explanation

With no labels, the task is to discover structure in the data, which is unsupervised learning. Clustering such as k-means does this. The other options require labeled targets or a reward environment.

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