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Course Content
Part 1. Introduction: Fraud & Risk Data Foundations
Query the customers table to understand risk profiles
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Explore accounts to understand financial relationships
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Retrieve transaction data to understand activity patterns
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Examine merchants and devices to understand behavior context
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Identify key fraud indicators such as location, amount, and frequency
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Filter transaction data to isolate suspicious patterns
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Part 2 . Intermediate: Fraud Detection SQL Queries
Join transactions and accounts to trace financial activity
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Join customers and transactions to analyze behavior
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Combine transactions and merchants to identify risky merchants
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Use transaction timestamps to detect unusual frequency patterns
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Aggregate transaction data to identify high-value or abnormal activity
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Identify anomalies using GROUP BY and HAVING
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Part 3 . Advanced: Fraud & AML Analytics
Perform transaction anomaly detection using transaction patterns
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Conduct velocity checks to detect rapid transactions
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Analyze fraud alerts and cases to identify trends
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Evaluate customer risk using risk_scores and behavior data
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Analyze AML alerts and sanctions screening results
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Detect suspicious relationships across accounts and transactions
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