A retail bank's fraud team notices that rule-based systems miss novel attack patterns as soon as fraudsters adjust their behaviour. Which AI capability is best suited to detecting previously unseen fraud without requiring manual rule updates?
- AA machine learning model that learns normal transaction patterns and flags statistical outliers Correct
- BA collaborative filtering recommender system trained on purchase history
- CA large language model that summarises customer support tickets
- DA graph database that stores historical transaction records for audit queries
Why A is correct: Supervised and unsupervised ML models build a statistical baseline of legitimate behaviour and score deviations in real time, catching novel fraud patterns that no explicit rule anticipates.
Why B is wrong: Collaborative filtering identifies similarities between users to suggest products; it is designed for recommendation, not anomaly detection in transaction streams.
Why C is wrong: LLM summarisation addresses unstructured text workloads and has no mechanism for scoring numerical transaction features against a learned fraud baseline.
Why D is wrong: A graph database is a storage and query technology, not an AI model that learns patterns; it cannot adaptively score new transactions for fraud likelihood.