Introduction
To improve QI Tech's credit scoring algorithm for small business loans, we need to focus on enhancing risk assessment accuracy while maintaining fairness and transparency. This challenge involves balancing data-driven insights with the unique characteristics of small businesses. I'll approach this by examining our current algorithm, identifying key areas for improvement, and proposing innovative solutions that leverage both traditional and alternative data sources.
Step 1
Clarifying Questions (5 mins)
Why it matters: This helps us identify specific areas where our algorithm might be underperforming and guides our focus for improvement. Expected answer: Default rates are slightly higher than industry average, particularly in newer industries like tech startups. Impact on approach: Would focus on incorporating more relevant data points for emerging industries.
Why it matters: This indicates potential friction points in the process that might be caused by our credit scoring requirements. Expected answer: 70% completion rate, with most dropoffs occurring during financial document upload. Impact on approach: Would explore ways to simplify document requirements or offer alternative data sources.
Why it matters: This helps us understand if our algorithm needs to be more adaptive to macroeconomic changes. Expected answer: Smaller businesses (under 10 employees) have been more volatile in loan performance. Impact on approach: Would consider incorporating more real-time economic indicators into our model.
Why it matters: Ensures our solution aligns with broader company goals. Expected answer: Primary goal is to reduce risk while maintaining current approval rates. Impact on approach: Would focus on increasing the precision of our risk assessments without significantly tightening criteria.
At this point, you can ask interviewer to take a 1-minute break to organize your thoughts before diving into the next step.
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