Top 50 fraud detection Interview Questions
The most frequently asked fraud detection questions across all roles and companies, ranked by real interview frequency. Updated daily.
Identify repeated transaction records within February 2025 using aggregation, date filtering, and account joins.
SoFiUse CTEs, joins, aggregations, and CASE logic to flag Lyft driver accounts with suspicious early activity patterns.
LyftUse joins, a CTE, and HAVING to find payment methods linked to multiple Lyft user accounts.
LyftIdentify potentially fraudulent loan applications by combining repeated applicants, shared identifiers, and income inconsistencies.
UpstartUse PostgreSQL window functions and time filtering to find nearby duplicate charges by account and amount.
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Tests feature engineering ability to detect coordinated fraud patterns relevant to SentiLink identity and risk solutions.
SentiLinkEvaluates your ability to design an ML approach for detecting fraud in eBay listings.
eBayEvaluates system design for low-latency detection, data flow, and reliability.
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