Design a baseline loan recommendation system
Overview
This question evaluates understanding of ML-driven recommendation and credit-risk systems, including skills in personalization, feature design, risk calibration, fairness and regulatory compliance, explainability, and operational monitoring.
Shopify
Sep 6, 2025, 12:00 AM
Machine Learning Engineer
Onsite
ML System Design
0
0
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:
- Define objectives and constraints (approval likelihood, default risk, user experience, regulatory and fair lending requirements).
- Describe features for users and loan products; include handling of cold-start and missing data.
- Choose a simple, defensible modeling approach (e.g., logistic ranking with calibrated risk).
- Outline offline metrics and online experiment design.
- Discuss bias and feedback-loop mitigation.
- Propose safety checks, phased rollout, and monitoring plan.
Solution
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