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Product Improvement Hard Member-only

What improvements could Blend (Financial Software) make to its income verification feature to increase accuracy and reduce manual review time?

Prepared by NextSprints

15 mins
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Problem Solving Feature Prioritization Data Analysis Financial Services Mortgage Lending Technology User Experience Product Improvement Automation FinTech Income Verification
Product Management Improvement Question: Enhancing Blend's income verification feature for accuracy and efficiency

Introduction

To improve Blend's income verification feature, we need to focus on increasing accuracy and reducing manual review time. This challenge sits at the intersection of financial technology, user experience, and operational efficiency. I'll approach this by examining user segments, pain points, and potential solutions, keeping in mind the dual goals of accuracy and speed.

Step 1

Clarifying Questions

  • Looking at the product context, I'm thinking about the primary use cases for income verification. Could you help me understand the main scenarios where users need to verify their income through Blend?

Why it matters: Determines the scope and variety of income sources we need to handle. Expected answer: Mortgage applications, personal loans, and rental applications. Impact on approach: Would focus on solutions that cater to diverse income types and documentation.

  • Considering user behavior, I'm curious about the current manual review process. What percentage of income verifications currently require manual review, and what's the average time spent on each?

Why it matters: Helps quantify the problem and set benchmarks for improvement. Expected answer: 40% of verifications require manual review, taking an average of 30 minutes each. Impact on approach: Would prioritize automating the most time-consuming aspects of manual review.

  • Thinking about Blend's position in the market, how does our accuracy rate compare to competitors, and what's our target improvement?

Why it matters: Helps set realistic goals and understand competitive pressures. Expected answer: We're slightly behind top competitors with 92% accuracy, aiming for 98%. Impact on approach: Would focus on innovative solutions to leapfrog competitors rather than incremental improvements.

  • Considering external factors, how have recent changes in employment patterns (e.g., gig economy, remote work) affected the complexity of income verification?

Why it matters: Identifies emerging challenges that our solution needs to address. Expected answer: Significant increase in non-traditional income sources, complicating verification. Impact on approach: Would emphasize flexibility and adaptability in our income verification methods.

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Updated Mar 29, 2025