What factors are causing the sudden increase in default rates for TVS Credit's used car loans over the past month?

Introduction

The sudden increase in default rates for TVS Credit's used car loans over the past month 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 recent change in the loan approval process. Has there been any modification to the credit scoring model or underwriting criteria in the last 1-2 months?

Why it matters: Changes in approval criteria could directly impact default rates. Expected answer: Yes, there was a recent update to the credit scoring model. Impact on approach: If confirmed, we'd focus on analyzing the new model's performance.

• Considering the specificity of used car loans, I'm curious about the vehicle inventory. Has there been a significant shift in the types or ages of vehicles being financed recently?

Why it matters: Changes in vehicle quality could affect borrowers' ability to repay. Expected answer: There's been an increase in older vehicle financing. Impact on approach: We'd investigate the correlation between vehicle age and default rates.

• Given the sudden nature of the increase, I'm wondering about external economic factors. Have there been any major economic events or policy changes affecting the used car market or consumer finances in the past month?

Why it matters: External economic pressures could explain the sudden rise in defaults. Expected answer: No significant economic events noted. Impact on approach: We'd focus more on internal factors if external events are ruled out.

• Thinking about user segments, I'm curious if the increase is uniform across all borrower profiles. Can we break down the default rates by customer segments such as income levels, credit scores, or geographic regions?

Why it matters: Identifying affected segments could point to specific underlying issues. Expected answer: The increase is more pronounced in certain customer segments. Impact on approach: We'd tailor our analysis and solutions to the most affected segments.