What caused the sudden 30% increase in false positives for CHEQ's Click Fraud Prevention tool last week?

Introduction

The sudden 30% increase in false positives for CHEQ's Click Fraud Prevention tool last week is a critical issue that demands immediate attention. This unexpected spike could significantly impact our clients' trust and the overall effectiveness of our fraud detection system. I'll approach this problem systematically, focusing on identifying the root cause, validating hypotheses, and developing both short-term fixes and long-term solutions.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might have been a recent update. Has there been any recent changes to the Click Fraud Prevention algorithm or underlying infrastructure?

Why it matters: Recent changes often correlate with sudden metric shifts. Expected answer: Yes, there was a minor update to the algorithm last week. Impact on approach: If confirmed, we'd focus on the update's specifics and rollback options.

• Considering the scale of the increase, I'm wondering about traffic patterns. Have we seen any unusual spikes or changes in traffic volume or sources recently?

Why it matters: Abnormal traffic could trigger false positives if not accounted for. Expected answer: Traffic has been relatively stable. Impact on approach: If stable, we'd shift focus to internal system issues rather than external factors.

• Given the nature of click fraud, I'm curious about new fraud patterns. Have we detected any new or evolving click fraud techniques in the past month?

Why it matters: Sophisticated new fraud methods could potentially trigger false positives. Expected answer: No significant new patterns detected. Impact on approach: If no new patterns, we'd investigate our detection logic and thresholds.

• Thinking about system health, I'm concerned about potential data processing issues. Have there been any reported issues with data ingestion or processing pipelines?

Why it matters: Data pipeline issues could lead to incorrect classifications. Expected answer: Some intermittent delays in data processing were noticed. Impact on approach: If confirmed, we'd prioritize investigating and optimizing our data pipeline.