A classic classification model trained to assign claims | AIGP
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A classic classification model trained to assign claims: Considering model-type differences, which choice best

AIGP Understanding How to Govern AI Deployment and Use Medium

A bounded, auditable classification into fixed categories is best served by a classic classification model.

The question

An insurer needs to predict whether claims fall into one of five predefined fraud-risk categories, with outputs that are consistent and straightforward to audit. Considering model-type differences, which choice best fits this task?

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  1. A generative language model prompted to compose an open-ended narrative describing each claim's risk
    A generative model produces variable free-text and is harder to audit for a fixed-category classification need.
  2. A multimodal generative model that also interprets the images attached to each claim file
    Multimodal generation adds unneeded capability and variability for a task with fixed textual categories.
  3. A classic classification model trained to assign claims to the fixed, predefined risk categories ✓
    A classic classifier maps inputs to a fixed label set with consistent, auditable outputs, matching this bounded task.
  4. An open-ended generative model tuned to draft entirely new fraud-investigation policies
    Policy drafting is a different task entirely and does not perform the required categorical prediction.
The trap
Defaulting to a generative model for a task that a classic, more auditable classifier handles better.

How to remember it

A bounded, auditable classification into fixed categories is best served by a classic classification model.

How many of these would you get right?

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