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Co-pilot: Write an SQL to check fraud transactions

 Write an SQL to check fraud transactions

Certainly! Detecting and preventing fraud using SQL can be a powerful approach. Below, I’ll guide you through creating a simple SQL query to identify potential fraudulent transactions. Let’s assume you’re working as a data analyst at the popular automobile company, Drezzla Autos, which is facing fraudulent transactions on its e-commerce platform.

Step 1: Database Setup Before querying, set up your database. Create four tables: users, products, transactions, and fraud_report. You can follow the sample code below:

SQL

-- Creating Tables --
-- Users table
CREATE TABLE users (
    user_id SERIAL PRIMARY KEY,
    user_name VARCHAR(100),
    -- Add other relevant columns as needed
);

-- Products table
CREATE TABLE products (
    product_id SERIAL PRIMARY KEY,
    product_name VARCHAR(100),
    -- Add other relevant columns as needed
);

-- Transactions table
CREATE TABLE transactions (
    transaction_id SERIAL PRIMARY KEY,
    user_id INT,
    product_id INT,
    transaction_date TIMESTAMP,
    amount DECIMAL(10, 2),
    is_fraud BOOLEAN DEFAULT FALSE,
    -- Add other relevant columns as needed
);

-- Fraud report table
CREATE TABLE fraud_report (
    report_id SERIAL PRIMARY KEY,
    transaction_id INT,
    report_date TIMESTAMP,
    report_description TEXT,
    -- Add other relevant columns as needed
);

AI-generated code. Review and use carefully. More info on FAQ.

Step 2: Detecting Fraudulent Transactions Now let’s write a SQL query to identify potential fraudulent transactions. For example, you can group fraudulent transactions by the hour of the day and count the occurrences:

SQL

SELECT EXTRACT(HOUR FROM transaction_date) AS transaction_hour,
       COUNT(*) AS fraud_count
FROM transactions
WHERE is_fraud = TRUE
GROUP BY transaction_hour
ORDER BY fraud_count DESC;

AI-generated code. Review and use carefully. More info on FAQ.

This query groups all fraudulent transactions by the hour of the day and counts the number of fraudulent transactions for each hour1. You can adapt and enhance this query based on your specific requirements and additional data available in your database.

Remember to adjust the table names, column names, and conditions according to your actual database schema. Happy fraud detection! 🕵️‍♂️🔍🚫📊123

If you need further assistance or have more specific scenarios, feel free to ask! 😊👍

From Co-pilot