Why has Konfio's small business loan approval rate dropped by 15% in the last quarter?

Introduction

Konfio's 15% drop in small business loan approval rates over the last quarter is a critical issue that demands immediate attention. This decline not only impacts the company's revenue but also affects its mission to support small businesses. I'll approach this problem systematically, examining both internal and external factors to identify the root cause and propose effective solutions.

Step 1

Clarifying Questions (3 minutes)

• Considering the timing, I'm wondering if there have been any recent changes to our underwriting criteria. Have we adjusted our risk assessment models in the past quarter?

Why it matters: Changes in underwriting could directly impact approval rates. Expected answer: Yes, we've tightened our criteria slightly. Impact on approach: If confirmed, we'd need to analyze the impact of these changes on approval rates.

• Looking at market conditions, has there been a shift in the overall economic landscape for small businesses? Are we seeing any industry-wide trends in loan demand or default rates?

Why it matters: External economic factors could be influencing loan approvals across the sector. Expected answer: There's been a slight economic downturn affecting small businesses. Impact on approach: This would lead us to investigate how our approval process adapts to changing economic conditions.

• Focusing on our user base, have we noticed any changes in the profile of businesses applying for loans? For instance, are we seeing more applications from higher-risk industries?

Why it matters: A shift in applicant demographics could explain the drop in approval rates. Expected answer: We've seen an increase in applications from the hospitality sector. Impact on approach: We'd need to analyze our risk models for different industry segments.

• Considering our tech stack, have there been any recent updates to our loan processing systems or algorithms? Are all our data pipelines and integrations functioning correctly?

Why it matters: Technical issues could be causing incorrect rejections or data inconsistencies. Expected answer: We implemented a new machine learning model for initial screenings. Impact on approach: This would prompt a thorough review of the new model's performance and accuracy.