For Varo Bank's instant cash advance feature, how should we weigh increasing the maximum advance amount versus minimizing the risk of defaults?

Introduction

The trade-off we're examining today is how to balance increasing the maximum advance amount for Varo Bank's instant cash advance feature against minimizing the risk of defaults. This scenario involves weighing the potential benefits of offering larger cash advances to customers against the increased financial risk to the bank. I'll approach this analysis by considering the product's context, evaluating key metrics, designing experiments, and providing a structured decision framework.

Step 1

Clarifying Questions (3 minutes)

• Based on Varo's positioning as a digital bank, I'm thinking this feature might be crucial for customer acquisition and retention. Could you share more about how the instant cash advance feature fits into Varo's overall product strategy?

Why it matters: Helps prioritize the feature's importance in the product ecosystem Expected answer: Critical for differentiation and customer value proposition Impact on approach: Would influence the balance between growth and risk management

• Considering the current economic climate, I'm curious about the default rates we're currently seeing. Can you provide some context on our current default rates and how they compare to industry benchmarks?

Why it matters: Establishes a baseline for risk assessment Expected answer: Current default rates and industry comparisons Impact on approach: Would inform the risk tolerance in our decision-making process

• Looking at user behavior, I'm wondering about the typical usage patterns of the instant cash advance feature. Could you share insights on frequency of use, average advance amounts, and repayment trends?

Why it matters: Helps understand user needs and potential impact of changes Expected answer: Usage statistics and patterns Impact on approach: Would guide the determination of optimal advance amounts and risk profiles

• From a technical perspective, I'm thinking about our ability to implement dynamic lending limits. Is our current system capable of adjusting advance limits based on individual user profiles and behaviors?

Why it matters: Determines the feasibility of personalized risk management Expected answer: Current capabilities and potential limitations Impact on approach: Would influence the complexity of solutions we can consider

• Considering resource allocation, I'm curious about our capacity for enhancing our risk assessment models. Do we have the data science resources to develop more sophisticated predictive models for default risk?

Why it matters: Affects our ability to mitigate increased risk from higher advance amounts Expected answer: Available resources and current roadmap priorities Impact on approach: Would impact the timeline and depth of risk management strategies we can implement