Stripe phone screen 4 part interview experience

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November 30, 2025148 reads

Summary

I experienced a 4-part phone screen for Stripe, focusing on building a fraud detection system from data integrity to smart error reporting.

Full Experience

I recently went through a 4-part phone screen with Stripe, which was quite an experience focusing on building a comprehensive fraud detection system. It started with fundamental data integrity checks and progressively moved towards more complex risk analysis and user behavior matching, culminating in a smart error reporting mechanism.

Interview Questions (4)

Q1
Verify Transaction Data Integrity
Data Structures & Algorithms

This section focused on building foundational data-integrity checks for fraud detection. The prompt was very long, but the logic itself was simple. I was tasked to read six fields from a CSV file and verify that all fields were non-empty.

Q2
High-Risk Rule Validation
Data Structures & Algorithms

On top of the basic transaction information, this part introduced two core risk-control rules:

  • Amount rule: the transaction amount must fall within the business-defined normal range.
  • Payment-method rule: the payment method must not appear in the blocked method list.

If a transaction violated any of these conditions, it was flagged as SUSPICIOUS.

Q3
User Behavior Matching for Fraud Detection
Data Structures & Algorithms

This section required validating whether the transaction aligned with the user’s behavioral baseline. I had to ensure that at least 50% of the behavioral attributes—such as commonly used spending countries, typical time ranges, and average transaction-amount intervals—matched the user’s historical behavior. The implementation involved feature extraction, normalization, and computing a match ratio. If the current transaction’s match ratio was below 50%, it was classified as a behavior mismatch and flagged as SUSPICIOUS.

Q4
Smart Fraud Error Reporting
Data Structures & Algorithms

The final section implemented smart error reporting, replacing the vague label “SUSPICIOUS” with specific error codes. The system outputs up to two error codes according to priority. If there are no issues, it outputs “OK”. The output report must maintain column alignment for readability.

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