What factors are causing the increased error rates in Blend (Financial Software)'s income verification API calls this quarter?

Introduction

The increased error rates in Blend's income verification API calls this quarter present a critical issue that demands immediate attention. As we delve into this product root cause analysis, we'll systematically examine potential factors contributing to this performance decline. Our approach will involve a comprehensive investigation of internal and external variables, data-driven hypothesis formation, and strategic solution development.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might be a recent change in the API. Has there been any significant update to the income verification API in the last quarter?

Why it matters: Recent changes could directly impact error rates. Expected answer: Yes, there was a major update. Impact on approach: If yes, we'd focus on the update's specifics and potential issues.

• Considering user segments, I'm wondering if the increased error rates are uniform across all users. Are we seeing higher error rates for specific user groups or income brackets?

Why it matters: Helps identify if the issue is systemic or isolated to certain segments. Expected answer: Error rates are higher for users with non-traditional income sources. Impact on approach: If segmented, we'd investigate unique characteristics of affected groups.

• Thinking about data sources, has there been any change in how we're sourcing income data or in our data providers?

Why it matters: Changes in data sources could affect verification accuracy. Expected answer: No changes in data sources. Impact on approach: If changed, we'd examine the new data sources and integration points.

• Considering external factors, have there been any regulatory changes affecting income verification processes in the financial sector?

Why it matters: Regulatory changes could necessitate adjustments in our verification process. Expected answer: No significant regulatory changes. Impact on approach: If yes, we'd need to ensure our API complies with new regulations.

• Reflecting on system performance, has there been any notable increase in API call volume or changes in usage patterns this quarter?

Why it matters: Increased load or changed usage could strain the system, leading to errors. Expected answer: API call volume has increased by 30%. Impact on approach: If volume has significantly increased, we'd focus on scalability and performance optimization.