In a previous post, we talked about how newer developments in AI can be used by fraudsters to conduct scams. In this article we'll take a look at the opposite side, how AI can be use by fraud fighters to combat bad actors. One common way risk analysts enforce fraud rules is by using SQL to set up data monitoring alerts on a platform like LogicLoop. AI can be used by analysts to generate both common fraud monitoring topics as well as actual SQL formulas to enact these rules. In this example, we asked ChatGPT this question:
Write an article with examples of rules you can write using SQL to monitor fraud and risk on your platform.
Let's take a look at the output that ChatGPT generated:
In today's world, the threat of fraud and risk has become increasingly prevalent, and it is important for companies to take proactive measures to mitigate their risk exposure. SQL is a powerful tool that can be used to monitor fraud and risk on your platform. In this article, we will explore some examples of rules you can write using SQL to monitor fraud and risk on your platform.
1. Identifying suspicious transactions. One way to monitor for fraud and risk is to identify transactions that are suspicious. For example, you can write a rule that flags transactions where the amount is significantly higher than usual or where the transaction is taking place from a location that is different from the customer's usual location. Here is an example SQL query to identify transactions that are above a certain amount threshold:
SELECT *
FROM transactions
WHERE amount > 10000
2. Identifying anomalies in user behavior. Another way to monitor for fraud and risk is to identify anomalies in user behavior. For example, you can write a rule that flags users who are logging in from multiple devices or IP addresses within a short period of time. Here is an example SQL query to identify users who have logged in from more than one device in the past 24 hours:
SELECT user_id, COUNT(DISTINCT device_id) AS device_count
FROM logins
WHERE login_time > DATEADD(day, -1, GETDATE())
GROUP BY user_id
HAVING COUNT(DISTINCT device_id) > 1
3. Identifying potential credit card fraud. Credit card fraud is a common type of fraud that companies need to monitor for. One way to do this is to identify credit card transactions that are potentially fraudulent. For example, you can write a rule that flags transactions where the billing address is different from the shipping address or where the CVV code is incorrect. Here is an example SQL query to identify credit card transactions where the billing address is different from the shipping address:
SELECT *
FROM transactions
WHERE payment_method = 'Credit Card'
AND billing_address <> shipping_address
4. Identifying potential money laundering. Money laundering is a serious risk that companies need to monitor for. One way to do this is to identify transactions that may be indicative of money laundering. For example, you can write a rule that flags transactions where the amount is just below a reporting threshold or where multiple transactions are being made in quick succession. Here is an example SQL query to identify transactions where the amount is just below a reporting threshold:
SELECT *
FROM transactions
WHERE amount > 9000
AND amount < 10000
5. Identifying potential insider threats. Insider threats are a risk that companies need to monitor for. One way to do this is to identify employees who are accessing sensitive data or performing unusual actions. For example, you can write a rule that flags employees who are accessing data outside of their normal job responsibilities or who are logging in outside of normal business hours. Here is an example SQL query to identify employees who are accessing data outside of their normal job responsibilities:
SELECT *
FROM employee_logs
WHERE activity = 'Accessed sensitive data'
AND job_title <> 'Data Analyst'
In conclusion, SQL is a powerful tool that can be used to monitor for fraud and risk on your platform. By writing rules to identify suspicious transactions, anomalies in user behavior, potential credit card fraud, potential money laundering, and insider threats, companies can proactively monitor for and mitigate their risk exposure.
With tools like ChatGPT, risk and fraud analysts now have powerful tools at their disposal to generate fraud monitoring formulas using a popular language like SQL. Paired with a SQL alerting & automation platform like LogicLoop, you can now quickly create powerful workflows to protect your platform, all without needing engineers.
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