What caused the sudden spike in false positive rates for Feedzai's fraud detection models last week?

Introduction

The sudden spike in false positive rates for Feedzai's fraud detection models last week is a critical issue that demands immediate attention. As we delve into this product root cause analysis, we'll systematically investigate potential factors, generate hypotheses, and develop a comprehensive plan to address and prevent future occurrences.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might be a recent change in the model or data pipeline. Has there been any recent update to the fraud detection algorithms or data sources?

Why it matters: Recent changes often correlate with performance shifts. Expected answer: Yes, a model update was deployed last week. Impact on approach: If confirmed, we'd focus on the update's specifics and rollback options.

• Considering the nature of false positives, I'm wondering about the affected transaction types. Are we seeing this spike across all transaction categories or specific ones?

Why it matters: Helps narrow down potential causes and affected user segments. Expected answer: The spike is primarily in high-value transactions. Impact on approach: We'd investigate factors unique to high-value transactions and their processing.

• Given the sudden nature of the spike, I'm curious about any concurrent external events. Were there any significant market events or news that could have altered transaction patterns?

Why it matters: External factors can dramatically impact user behavior and model performance. Expected answer: No major external events noted. Impact on approach: We'd focus more on internal factors if external influences are ruled out.

• Thinking about system health, I'm concerned about potential data quality issues. Have we observed any anomalies in our data ingestion or processing pipelines?

Why it matters: Data quality directly impacts model performance. Expected answer: Some data pipeline issues were reported last week. Impact on approach: We'd prioritize investigating data pipeline integrity and recent changes.