How might Lendable (London) refine its credit scoring algorithm to better assess creditworthiness for thin-file borrowers?

Introduction

To refine Lendable's credit scoring algorithm for better assessing creditworthiness of thin-file borrowers, we need to take a comprehensive approach that considers both traditional and alternative data sources. I'll outline a strategy to enhance the algorithm's accuracy and inclusivity, focusing on the unique challenges faced by thin-file borrowers in London's financial landscape.

Step 1

Clarifying Questions

• Looking at Lendable's position in the London fintech market, I'm curious about their current market share and growth trajectory. Could you share some insights on Lendable's current position relative to traditional banks and other fintech lenders in London?

Why it matters: Understanding Lendable's market position helps determine if we should focus on aggressive expansion or consolidation of existing market share. Expected answer: Lendable has a growing but still relatively small market share compared to traditional banks. Impact on approach: If true, we might focus on differentiating factors that can attract more thin-file borrowers from traditional institutions.

• Considering the importance of data in credit scoring, I'm wondering about Lendable's current data sources. What types of alternative data, if any, is Lendable currently using in its credit scoring model for thin-file borrowers?

Why it matters: Identifies gaps in current data collection and potential areas for expansion. Expected answer: Lendable uses some alternative data like utility payments and rental history, but there's room for expansion. Impact on approach: If confirmed, we'd focus on integrating more diverse and predictive alternative data sources.

• Given the regulatory environment in the UK, I'm interested in understanding the current compliance landscape. What are the key regulatory constraints or opportunities that Lendable faces in expanding its credit scoring model for thin-file borrowers?

Why it matters: Ensures our solution aligns with regulatory requirements and identifies potential barriers. Expected answer: Strict data protection laws under GDPR, but increasing support for open banking initiatives. Impact on approach: Would emphasize privacy-compliant data sources and leverage open banking opportunities.

• Thinking about the user experience, I'm curious about the current application process. What's the typical timeline from application to decision for thin-file borrowers, and how does this compare to traditional borrowers?

Why it matters: Identifies potential friction points in the current process that may be deterring thin-file borrowers. Expected answer: Longer processing times for thin-file borrowers due to manual reviews and additional data requests. Impact on approach: Would focus on streamlining the process and automating decision-making where possible.