Procure a predictive model that estimates fraud likelihood | AIGP
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Procure a predictive model that estimates fraud likelihood: Which approach fits that constraint?

AIGP Understanding the Foundations of AI Governance Easy

Choose predictive modeling because the requirement concerns estimating fraud, not creating new content.

The question

A software procurement team needs a tool that estimates whether an invoice is likely fraudulent from historical features. The team explicitly prohibits producing new text, images, or other artifacts. Which approach fits that constraint?

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  1. Procure a language model that summarizes prior fraud cases.
    Summarization generates text about prior cases, rather than producing the requested prediction about an invoice.
  2. Procure an unsupervised model that creates synthetic invoices.
    Synthetic invoice creation is generated content and does not directly satisfy the required fraud-likelihood prediction.
  3. Procure a generative model that drafts fraud narratives.
    This produces new content, conflicting with the stated requirement for predictive output rather than generated artifacts.
  4. Procure a predictive model that estimates fraud likelihood. ✓
    A predictive model estimates an outcome from inputs and satisfies the prohibition on generating new content.
The trap
Identify the required output first; do not infer system type from the business domain alone.

How to remember it

Choose predictive modeling because the requirement concerns estimating fraud, not creating new content.

How many of these would you get right?

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