What factors are contributing to the sudden 30% increase in default rates for Snap Finance's no credit check loans in the last quarter?

Introduction

The sudden 30% increase in default rates for Snap Finance's no credit check loans in the last quarter is a critical issue that demands immediate attention. This analysis will systematically identify, validate, and address the root cause while considering both short-term and long-term implications for the business.

I'll approach this problem by first clarifying key details, ruling out external factors, and then diving deep into the product, user journey, and metrics. From there, I'll generate data-driven hypotheses, conduct root cause analysis, and propose validation methods and solutions.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might be a seasonal component. Has this increase coincided with any particular time of year or event?

Why it matters: Seasonal factors could explain temporary spikes in default rates. Expected answer: No clear seasonal pattern identified. Impact on approach: If seasonal, we'd focus on cyclical mitigation strategies.

• Considering user segments, I'm wondering if this increase is uniform across all borrower types. Have you noticed any particular demographic or risk profile being disproportionately affected?

Why it matters: Identifying affected segments helps narrow down potential causes. Expected answer: Higher default rates among younger borrowers or those with lower income. Impact on approach: We'd tailor solutions to specific high-risk segments.

• Thinking about recent changes, has there been any modification to the loan approval algorithm or underwriting criteria in the past 6 months?

Why it matters: Changes in loan criteria could inadvertently increase risk. Expected answer: Minor tweaks to increase approval rates for borderline applicants. Impact on approach: We'd review and possibly revert recent algorithm changes.

• Considering data integrity, I'm curious if there have been any changes to how default rates are calculated or reported. Can you confirm the consistency of the metric definition and measurement systems?

Why it matters: Ensures we're dealing with a real issue, not a reporting anomaly. Expected answer: No changes in calculation methods or reporting systems. Impact on approach: If inconsistencies found, we'd focus on data reconciliation first.