An online marketplace wants to flag suspicious account registrations and payments. The company employs no data scientists, but its analysts hold a few years of records showing which past registrations turned out to be fraudulent, and they want to build and use a fraud model themselves through a no-code interface on AWS. Which approach should the company use?
- A.
Amazon Personalize, to rank registrations by how closely each one resembles the registrations made by the marketplace's most valuable customers.
- B.
Amazon Comprehend, to analyse the text of each registration form and infer from its wording whether the applicant's intent is fraudulent.
- C.
Amazon SageMaker Canvas, to build a binary classification model from the labelled fraud history without code and generate predictions for new events.
- D.
Amazon Rekognition, to compare the profile photograph supplied at registration against a stored collection of photographs of known fraudsters.
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Answer: C
Fraud detection from labelled history is a binary classification problem, and SageMaker Canvas lets analysts build and use that model without writing code.
- A. Personalize generates recommendations from interaction data; it does not detect fraudulent activity.
- B. Comprehend analyses natural language text and would ignore the behavioural and payment signals.
- C. Canvas builds a binary classification model from the analysts' own labelled fraud history with no code, which fits both the data and the skills available.
- D. Face comparison addresses only one narrow signal and presumes a collection of known fraudster photographs.