Introduction
The trade-off between investing in automated credit scoring algorithms or expanding our manual underwriting team for more accurate risk assessment is a critical decision for Julo. This scenario involves balancing technological innovation with human expertise in the fintech lending space. I'll analyze this trade-off by examining the product context, potential impacts, key metrics, and experimental approaches to guide our decision-making process.
I'd like to start by asking a few clarifying questions to ensure we're aligned on the key aspects of this trade-off. Then, I'll walk you through my analysis framework, covering product understanding, hypothesis formation, metrics identification, experiment design, and ultimately, a recommendation with next steps.
Step 1
Clarifying Questions (3 minutes)
Why it matters: Helps identify if speed or accuracy is our primary concern Expected answer: Approval times are slower than competitors Impact on approach: Would prioritize automation if speed is the main issue
Why it matters: Determines the urgency and scale of the solution needed Expected answer: Aiming for 50-100% growth Impact on approach: Higher growth targets would favor automation for scalability
Why it matters: Helps balance speed vs. accuracy from the user perspective Expected answer: Higher satisfaction with faster approvals, but also higher default rates Impact on approach: Would need to find a balance between speed and accuracy
Why it matters: Affects the feasibility and timeline of implementing automated scoring Expected answer: Basic ML capabilities in place, but not fully integrated Impact on approach: Might need to factor in additional time and resources for tech development
Why it matters: Helps assess the scalability of the manual approach Expected answer: Team of 20, operating at 80% capacity Impact on approach: Limited room for manual scaling might push towards automation
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