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 PayPal.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at PayPal during technical interviews.

Detect credit-card transaction fraud | PayPal Interview Question

Quick Overview

This question evaluates a data scientist's competency in real-time fraud detection system design, including feature engineering, rule-based and model-based decisioning, unsupervised anomaly detection, handling delayed labels and class imbalance, and operational constraints such as low-latency inference.

Credit-Card Fraud Detection: Decisions and System Design

Context

You are designing a real-time decisioning system for card transactions with strict latency constraints. At authorization time, only a subset of features is available; post-transaction outcomes (e.g., chargebacks) arrive weeks later.

For the accept/decline exercise, assume the following two minimal transaction snippets are available at decision time:

Transaction A
- Channel: e-commerce (card-not-present)
- Amount: $4,200
- Cardholder home country: UK
- Merchant country: US (electronics)
- Local time at cardholder: 03:17
- Device fingerprint: new to platform
- IP geolocation: NG (Nigeria), VPN likely
- Velocity: 5 auth attempts in last 10 minutes on this card across different merchants
Transaction B
- Channel: card-present (EMV chip + PIN)
- Amount: $18.75
- Cardholder home country: UK
- Merchant country: UK (coffee shop)
- Local time at cardholder: 12:41
- Device/terminal: known merchant terminal, low dispute history
- Velocity: consistent with past user pattern (daily coffee purchases)

Tasks

Decide whether to accept or decline each transaction and justify your reasoning. If a conditional action (e.g., step-up authentication) is preferable, state it.
Outline a full fraud-detection strategy for card transactions: data required, feature engineering, real-time rules, and model-based approaches (including system/latency considerations).
Explain how unsupervised learning can be applied to detect fraudulent transactions and list suitable algorithms.
Detail appropriate evaluation metrics for fraud models, including how to assess unsupervised methods without labeled data.

Quick Overview

Context

For the accept/decline exercise, assume the following two minimal transaction snippets are available at decision time:

Transaction A

Channel: e-commerce (card-not-present)
Amount: $4,200
Cardholder home country: UK
Merchant country: US (electronics)
Local time at cardholder: 03:17
Device fingerprint: new to platform
IP geolocation: NG (Nigeria), VPN likely
Velocity: 5 auth attempts in last 10 minutes on this card across different merchants

Transaction B

Channel: card-present (EMV chip + PIN)
Amount: $18.75
Cardholder home country: UK
Merchant country: UK (coffee shop)
Local time at cardholder: 12:41
Device/terminal: known merchant terminal, low dispute history
Velocity: consistent with past user pattern (daily coffee purchases)

Tasks

Decide whether to accept or decline each transaction and justify your reasoning. If a conditional action (e.g., step-up authentication) is preferable, state it.

Outline a full fraud-detection strategy for card transactions: data required, feature engineering, real-time rules, and model-based approaches (including system/latency considerations).

Explain how unsupervised learning can be applied to detect fraudulent transactions and list suitable algorithms.

Detail appropriate evaluation metrics for fraud models, including how to assess unsupervised methods without labeled data.

Detect credit-card transaction fraud

Quick Overview

Credit-Card Fraud Detection: Decisions and System Design

Context

Tasks

Solution

Comments (0)

Detect credit-card transaction fraud

Quick Overview

Credit-Card Fraud Detection: Decisions and System Design

Context

Tasks

Solution

Comments (0)