Analyze and mitigate fake advertiser accounts

Q: Analyze and mitigate fake advertiser accounts

This question evaluates competency in fraud detection analytics, including operationally defining fake advertiser accounts, designing longitudinal metrics, estimating prevalence with incomplete labels, and assessing detection and enforcement changes.

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Question

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Your ads platform suspects there are fake advertiser accounts (fraudulent accounts created to scam users, evade policy, or manipulate spend). You are asked to lead the analytics approach.

Prompt

Define what “fake account” means operationally and propose metrics to measure the problem over time.
Given that labels are incomplete (only a small fraction of accounts are manually reviewed), how would you estimate prevalence and track trends without being misled by enforcement changes?
Propose an approach to evaluate a detection/enforcement change (e.g., a new model or new threshold). Include:

offline model metrics and cost tradeoffs
online experiment design and guardrails
key failure modes (false positives, adversarial adaptation, selection bias)

Analyze and mitigate fake advertiser accounts

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Prompt

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