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Product Management Root Cause Analysis Question: Investigating sudden drop in Affirm's Split Pay approval rate

Why has the approval rate for Affirm's Split Pay dropped by 15% in the last week?

Data Analysis Problem-Solving Risk Management Fintech E-commerce Consumer Finance
Data Analysis Fintech Root Cause Analysis Risk Assessment Metric Optimization

Introduction

The recent 15% drop in Affirm's Split Pay approval rate is a critical issue that demands immediate attention. This unexpected decline could significantly impact user experience, revenue, and overall business performance. I'll approach this problem systematically, focusing on identifying the root cause, validating hypotheses, and developing both short-term and long-term solutions.

Framework overview

This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development.

Step 1

Clarifying Questions (3 minutes)

  • Looking at the timing, I'm thinking there might have been a recent change in our risk assessment model. Has there been any update to our credit scoring algorithm in the past week?

Why it matters: Changes in risk assessment directly impact approval rates. Expected answer: Yes, there was a minor update to the algorithm. Impact on approach: If confirmed, we'd focus on analyzing the algorithm change and its effects.

  • Considering user segments, I'm wondering if this drop is uniform across all user groups. Can you provide a breakdown of the approval rate change by user segments (e.g., new vs. returning customers, age groups, credit score ranges)?

Why it matters: Helps identify if the issue is systemic or specific to certain user groups. Expected answer: The drop is more pronounced in new customers and those with lower credit scores. Impact on approach: We'd investigate factors specifically affecting these segments.

  • Given the sudden nature of the drop, I'm curious about any recent changes in our data sources. Have there been any disruptions or changes in our credit data providers in the last week?

Why it matters: Data quality issues could lead to incorrect risk assessments. Expected answer: No known issues with data providers. Impact on approach: If confirmed, we'd shift focus to internal factors rather than external data issues.

  • Considering potential technical issues, I'm wondering about our system's performance. Have there been any unusual spikes in latency or errors in our approval process systems?

Why it matters: Technical issues could lead to incomplete assessments or false rejections. Expected answer: Some intermittent latency issues were observed. Impact on approach: We'd prioritize investigating the technical infrastructure and its impact on approvals.

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