How can Octane improve its credit decisioning model to better assess risk for non-traditional borrowers?

Introduction

To improve Octane's credit decisioning model for non-traditional borrowers, we need to rethink our approach to risk assessment. This challenge involves balancing financial inclusion with responsible lending practices. I'll outline a strategy to enhance our model, focusing on innovative data sources, advanced analytics, and user-centric design.

Step 1

Clarifying Questions (5 mins)

• Looking at the product context, I'm thinking Octane might be facing challenges with traditional credit scoring methods for certain user segments. Could you elaborate on the specific types of non-traditional borrowers we're targeting and why our current model isn't adequately serving them?

Why it matters: Determines the focus of our improvement efforts and potential data sources. Expected answer: Gig economy workers, recent immigrants, or young professionals with limited credit history. Impact on approach: Would tailor our solution to specific user needs and alternative data sources.

• Considering user behavior, I'm curious about the application process and user journey. Can you walk me through the current credit application flow and where we're seeing the highest drop-off rates or friction points?

Why it matters: Identifies areas for immediate improvement in the user experience. Expected answer: High drop-off during document upload or income verification stages. Impact on approach: Would prioritize streamlining these specific steps in the process.

• Regarding company alignment, I'd like to understand our risk tolerance and business goals. What's the target approval rate we're aiming for, and how does this balance with our default rate objectives?

Why it matters: Helps calibrate the model's strictness and informs our success metrics. Expected answer: Aiming for a 70% approval rate while maintaining a default rate below 5%. Impact on approach: Would focus on fine-tuning the model's sensitivity and specificity.

• Considering external factors, I'm interested in the regulatory landscape. Are there any upcoming changes in financial regulations that might impact our credit decisioning process for non-traditional borrowers?

Why it matters: Ensures our solution is future-proof and compliant. Expected answer: Potential new guidelines on alternative data usage in credit decisions. Impact on approach: Would incorporate flexibility in our model to adapt to regulatory changes.