Design a baseline loan recommendation system

Quick 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.

Design a baseline loan recommendation system

Company: Shopify

Role: Machine Learning Engineer

Category: ML System Design

Difficulty: hard

Interview Round: Onsite

Design a baseline system to recommend loans to users. Define the objective(s) and constraints (e.g., approval likelihood, default risk, user experience, regulatory and fair lending requirements); describe features for users and loan products, handling cold-start and missing data; choose a simple yet defensible modeling approach (e.g., logistic ranking with calibrated risk), outline offline metrics and online experiment design, discuss bias/feedback-loop mitigation, safety checks, and a phased rollout and monitoring plan.

Quick Answer: 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.

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Sep 6, 2025, 12:00 AM
hardMachine Learning EngineerOnsiteML System Design
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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.

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