System Design: Baseline Loan Recommendation System

Context

Design a baseline system that recommends loan offers to users on a digital platform. The system should present a ranked set of loan products (amount, term, APR) personalized to each user while meeting risk, regulatory, and fairness requirements.

Assumptions:

• Loans are originated by partner lenders with defined credit policies; the platform controls which offers to show and in what order.
• Outcomes of interest include approval, user acceptance, repayment/default, and user experience.
• We must comply with applicable fair lending and consumer credit regulations (e.g., ECOA/FCRA-like requirements), provide explainability, and avoid use of protected attributes.

Task

Design a defensible baseline system. Specifically:

1. Define objectives and constraints (approval likelihood, default risk, user experience, regulatory and fair lending requirements).
2. Describe features for users and loan products; include handling of cold-start and missing data.
3. Choose a simple, defensible modeling approach (e.g., logistic ranking with calibrated risk).
4. Outline offline metrics and online experiment design.
5. Discuss bias and feedback-loop mitigation.
6. Propose safety checks, phased rollout, and monitoring plan.