Meta Asked: Design a Borrowing & Lending Product
Learn how to frame, structure, and solve a real-world product design interview question.
In this edition we are going to dive into a Product Design question asked at Meta (Facebook).
Here’s the question tile 👇
Question Source: Exponent
Design a product for borrowing and lending.
Define the Problem:
• Build a trustworthy and seamless platform for peer-to-peer borrowing and lending of money.
• Objective: Enable users to access short-term credit or earn interest by lending, while minimizing risk and ensuring a great user experience.
Identify Actors & Motives:
• Borrowers - Need quick access to funds; care about low interest, fast approval, flexible repayment.
• Lenders - Want to earn interest with minimal risk; care about returns, risk scoring, and borrower credibility.
• Platform Owner - Aims for high liquidity, low default rates, and regulatory compliance.
• Regulators - Ensure KYC, AML, and responsible lending practices.
• Customer Support - Want clear resolution workflows for defaults, disputes, or fraud.
Design System Components:
• Inputs & Signals
KYC details, bank info, credit scores, transaction history, behavioral signals
Borrower request (amount, tenure, purpose)
Lender preferences (risk appetite, tenure, interest rate)
• Detection Logic
Rule-based eligibility filters + ML-based credit scoring (for borrowers)
Risk tiering models to match lenders’ risk appetite
Fraud detection algorithms using device and usage patterns
• Actions Taken by the System
Matchmaking engine recommends borrower-lender pairs
Auto-contract generation & digital agreement signing
Disbursal and repayment via connected bank accounts
Real-time reminders, collection workflows, or legal escalation for defaults
Handle Trade-offs:
• Speed vs. Accuracy in Risk Scoring - Fast approvals may miss subtle fraud cues; detailed checks slow the UX
• Borrower Privacy vs. Transparency for Lenders - More borrower data builds lender trust, but risks overexposure
• Liquidity vs. Personalization - More matches = faster loans, but less custom risk-fit for lenders
Metrics for Success:
• Loan Approval Rate - % of loan requests successfully matched and disbursed
• Default Rate - % of loans that are overdue or unpaid
• Average Time to Match - Time taken to connect a borrower and lender
• Net Promoter Score (NPS) - User satisfaction across both borrowers and lenders
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