How might Sunbit enhance its credit decision engine to provide more accurate and faster approvals for applicants?

Introduction

To enhance Sunbit's credit decision engine for more accurate and faster approvals, we need to analyze the current system, identify pain points, and propose innovative solutions. I'll approach this by examining user segments, analyzing pain points, generating solutions, and prioritizing improvements. Let's dive in.

Step 1

Clarifying Questions

• Looking at Sunbit's position in the fintech space, I'm thinking about the scale of operations. Could you share some information about the current volume of credit applications Sunbit processes daily and the average approval time?

Why it matters: This helps us understand the scale of the problem and set appropriate goals for improvement. Expected answer: Processing around 10,000 applications daily with an average approval time of 30 minutes. Impact on approach: High volume would prioritize automation and scalability, while longer approval times might focus on process optimization.

• Considering the competitive landscape, I'm curious about Sunbit's current approval rate compared to industry standards. What's our current approval rate, and how does it compare to our main competitors?

Why it matters: This helps us gauge if we need to focus more on accuracy or speed of approvals. Expected answer: Current approval rate is 70%, slightly below the industry average of 75%. Impact on approach: If below average, we might prioritize accuracy improvements; if above, we could focus on speed.

• Thinking about the data sources Sunbit currently uses, I'm wondering about the breadth and depth of our credit data. Can you tell me what types of data we currently incorporate into our credit decision engine?

Why it matters: This helps identify potential gaps in our data sources that could improve accuracy. Expected answer: Currently using traditional credit scores, income verification, and basic transaction history. Impact on approach: Limited data sources would suggest exploring additional data points for more comprehensive assessments.

• Considering the user experience, I'm interested in understanding the current pain points in the application process. What are the most common reasons for customer drop-offs during the credit application?

Why it matters: This helps us identify areas where we can improve the user experience alongside the decision engine. Expected answer: Main drop-offs occur during income verification and when asked for additional documentation. Impact on approach: Would focus on streamlining these specific areas of the application process.