Top 5 Fraud Detection Questions for Data Engineers: SQL, PySpark & Real-Time Pipelines

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Top 5 Fraud Detection Questions for Data Engineers: SQL, PySpark & Real-Time Pipelines

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Introduction

 

Why Fraud Detection Matters in Data Engineering

Fraud detection is one of the most critical applications of data engineering in fintech, e-commerce, and enterprise systems. Whether you're interviewing at PayPal, Stripe, or any data-first company, expect to be tested on your ability to detect anomalies, design scalable pipelines, and write efficient SQL or PySpark code.

Let’s break down five high-impact fraud detection questions — with optimized answers and implementation strategies.

1. How Would You Detect Possible Fraudulent Transactions Using SQL or PySpark?

ANS:-

Objective: Identify suspicious transactions based on velocity, frequency, or unusual patterns.

Approach:

• Use window functions to analyze user behavior over time.
• Partition by user_id and order by transaction_time.
• Calculate metrics like transaction count, amount sum, or location variance within a time window.

Example (PySpark):

from pyspark.sql.window import Window
from pyspark.sql.functions import col, count, sum, unix_timestamp

window_spec = Window.partitionBy("user_id").orderBy("transaction_time").rangeBetween(-600, 0)

df = df.withColumn("txn_count_10min", count("*").over(window_spec)) \
       .withColumn("amount_sum_10min", sum("amount").over(window_spec)) \
       .filter(col("txn_count_10min") > 5)

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2. Write SQL to Detect Users Performing Too Many Actions Within X Minutes?

ANS:-

Objective: Identify users who exceed a threshold number of actions in a short time frame.

Approach:

• Use self-joins or window functions with RANGE BETWEEN or ROWS BETWEEN.
• Filter users with more than N actions in a rolling time window.

Example (SQL):

SELECT user_id, COUNT(*) AS action_count, MIN(event_time) AS start_time, MAX(event_time) AS end_time
FROM (
  SELECT user_id, event_time,
         COUNT(*) OVER (PARTITION BY user_id ORDER BY event_time 
                        RANGE BETWEEN INTERVAL '5' MINUTE PRECEDING AND CURRENT ROW) AS cnt
  FROM user_events
) sub
WHERE cnt > 10
GROUP BY user_id, event_time

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3. Find Transactions Occurring from Multiple Locations Within 10 Minutes?

ANS:-

Objective: Detect location-based anomalies — e.g., a user transacting from two cities within minutes.

Approach:

• Use lag/lead functions to compare consecutive transactions.
• Calculate time difference and location mismatch.

Example (SQL):

WITH ordered_txns AS (
  SELECT user_id, transaction_time, location,
         LAG(location) OVER (PARTITION BY user_id ORDER BY transaction_time) AS prev_location,
         LAG(transaction_time) OVER (PARTITION BY user_id ORDER BY transaction_time) AS prev_time
  FROM transactions
)
SELECT *
FROM ordered_txns
WHERE location != prev_location
  AND transaction_time - prev_time <= INTERVAL '10' MINUTE

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4. Detect Anomalous Payment Patterns for a User?

ANS:-

Objective: Identify deviations from a user’s typical behavior — e.g., sudden spikes in amount or frequency.

Approach:

• Use z-score, IQR, or rolling averages to detect outliers.
• Compare current transaction against user’s historical behavior.

Example (PySpark):

from pyspark.sql.functions import avg, stddev

user_stats = df.groupBy("user_id").agg(avg("amount").alias("avg_amt"), stddev("amount").alias("std_amt"))

df = df.join(user_stats, "user_id")
df = df.withColumn("z_score", (col("amount") - col("avg_amt")) / col("std_amt"))
df.filter(col("z_score") > 3).show()

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5. Design a Pipeline for Real-Time Fraud Detection in Spark or Snowflake?

ANS:-

Objective: Architect a scalable, low-latency fraud detection system.

Approach:

🔧 Spark Streaming Pipeline (Kafka + Spark):

• Ingest: Use Kafka to stream transaction data.
• Process: Apply Spark Structured Streaming with sliding windows and watermarking.
• Detect: Use PySpark logic for velocity, location, and anomaly detection.
• Store: Write flagged transactions to a NoSQL store or alerting system.
• 
  

* Snowflake Real-Time Detection:

• Use Snowpipe to ingest streaming data.
• Create materialized views for rolling aggregates.
• Use tasks and streams to trigger fraud detection logic.
• Integrate with external functions for ML scoring if needed.

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* Final Thoughts

Fraud detection isn’t just about writing queries — it’s about understanding user behavior, designing scalable systems, and balancing real-time accuracy with performance. Whether you're preparing for a PayPal interview or building your own detection system, these patterns are foundational.

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