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.

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.

How do I approach ML System Design interview questions?

ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

What difficulty level is this interview question?

This is a hard difficulty ML System Design question, commonly asked during Onsite rounds at Shopify.

What role is this question designed for?

This question is commonly asked for Machine Learning Engineer candidates at Shopify during technical interviews.

Design a baseline loan recommendation system

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.

Context

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.

Design a baseline loan recommendation system

Quick Overview

System Design: Baseline Loan Recommendation System

Context

Task

Solution

Comments (0)

Design a baseline loan recommendation system

Quick Overview

System Design: Baseline Loan Recommendation System

Context

Task

Solution

Comments (0)