Risk Monitoring Rules

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Risk monitoring is the process of identifying, analyzing, and managing potential threats to business, financial operations, or security. In the context of banks, payment systems, and other financial institutions, risk monitoring aims to prevent fraud, money laundering, unauthorized access, and other risks. Here are the main rules and principles used in risk monitoring:

1. Customer segmentation​

• Objective: To divide clients into groups based on their behavior, risk level and type of transactions.
• Examples of rules:
  • New clients are subject to more stringent checks.
  • Large transactions are analyzed separately.
  • VIP clients may have higher limits, but their activities are also monitored.

2. Behavioral Analysis​

• Objective: To identify anomalies in the client's actions compared to his usual behavior.
• Examples of rules:
  • If a customer typically makes purchases of up to 500 and then suddenly makes a transaction for 5,000, it may be flagged as suspicious.
  • Transactions from a new geographic region (e.g. another country) require additional verification.

3. Checking card details​

• Objective: To ensure that the card data corresponds to the real owner.
• Examples of rules:
  • AVS (Address Verification System): Checks whether the Billing Address matches the address registered for the card.
  • CVV/CVC code: Requirement to enter a three-digit code on the back of the card.
  • 3D Secure: Additional level of authentication via SMS or app.

4. Monitoring IP addresses and devices​

• Objective To monitor devices and networks from which operations are performed.
• Examples of rules:
  • Disable operations from IP addresses associated with TOR, VPN or proxy servers.
  • Flag transactions if the card is used from a new device.
  • Block access from countries with high fraud rates.

5. Transaction limits​

• Objective: To limit the size or frequency of transactions to reduce risk.
• Examples of rules:
  • Set daily or monthly limits on transfers.
  • Limit the number of transactions in a short period of time.
  • Set the minimum and maximum amount for transactions.

6. Checking transaction speed​

• Objective: To identify attempts to quickly withdraw funds.
• Examples of rules:
  • If a customer makes several large transactions in a short period of time, the system may block the transactions.
  • Limit the frequency of account login attempts.

7. Use of Artificial Intelligence (AI) and Machine Learning (ML)​

• Objective: Automate data analysis and identify complex fraud patterns.
• Examples of rules:
  • ML algorithms can predict the likelihood of fraud based on historical data.
  • AI helps identify connections between suspicious accounts or IP addresses.

8. Geography check​

• Objective: To analyze the location of the client and his operations.
• Examples of rules:
  • Block transactions from countries with high fraud rates (e.g. countries with offshore zones).
  • Flag transactions if the client is in one region and the transaction is made from another.

9. Responding to suspicious transactions​

• Objective: To minimize damage from fraudulent activities.
• Examples of rules:
  • Send a notification to the client via SMS, email or push notification.
  • Temporarily block the card or account until the legitimacy of the transaction is confirmed.
  • Conduct an internal investigation to determine the cause of the suspicious activity.

10. Collaboration with other organizations​

• Objective: To exchange data on fraudulent transactions and new threats.
• Examples of rules:
  • Participate in information exchange programs with Visa, Mastercard and other payment systems.
  • Use databases of compromised cards and accounts.

11. Compliance with regulatory requirements​

• Objective: To comply with laws and standards to prevent risks.
• Examples of rules:
  • PCI DSS (Payment Card Industry Data Security Standard) compliance to protect card data.
  • Implementation of KYC (Know Your Customer) and AML (Anti-Money Laundering) procedures.
  • Regularly check the system for compliance with GDPR or other international standards.

12. Training of employees and clients​

• Objective: To raise awareness of risks and ways to prevent them.
• Examples of rules:
  • Conduct training for employees on recognizing fraudulent transactions.
  • Educate customers about the importance of two-factor authentication and data protection.

13. Regular audit and testing​

• Objective: To continuously improve the risk monitoring system.
• Examples of rules:
  • Conduct regular system checks for vulnerabilities.
  • Test new algorithms and rules on historical data.
  • Analyze fraud cases to improve the system.

14. Adaptive approach​

• Objective: To respond flexibly to new threats and changes in customer behavior.
• Examples of rules:
  • Update risk monitoring algorithms based on new data.
  • Implement new technologies such as biometric authentication.
  • Adjust rules based on current fraud trends.

Conclusion​

Risk monitoring is an ongoing process that requires a combination of technology, analytics and human involvement. Compliance with these rules helps to minimize risks, protect customers and ensure the security of financial transactions. However, it is important to remember that fraudsters are constantly improving their methods, so risk monitoring systems must be adaptive and ready for new challenges.

If you have additional questions about risk monitoring rules or examples of their application, please clarify them!

 

[Man]

Risk Monitoring Rules in Banks: How Fraud (Carding) Is Detected​

Banks use automated monitoring systems that analyze each transaction based on hundreds of parameters. Here are the key rules by which decisions on blocking are made:

1. Geographic anomalies​

• Sudden change of country/city:
  • The card was used in London and 2 hours later in Bangkok.
  • Solution: Confirmation (SMS, call) is required.
• IP and map geolocation mismatch:
  • Payment from Russian IP, but the merchant is in Nigeria.

2. Suspicious transaction patterns​

• A series of small payments(testing the card):
  • 5 payments of $1, then an attempt to transfer $1,000.
• Repeating identical transactions:
  • Multiple attempts to pay for the same item (for example, 10 times for $50 in one store).
• Operations that are not typical for the client:
  • Pensioner suddenly buys $5,000 worth of cryptocurrency.

3. Device and behavior data​

• Change device:
  • Logging into the app from a new phone + attempting to transfer.
• Suspicious activity in Internet banking:
  • Quick CVV entry (bot), change details before payment.
• Use of anonymous technologies:
  • VPN/Tor, Android emulators (e.g. Bluestacks).

4. Risky merchants​

• Shops from the "gray" lists:
  • Casinos, forex brokers, sites with data leaks.
• Payments to new/untested services:
  • Transfer to a newly registered wallet in an exotic country.

5. Social indicators​

• Pressure on the client:
  • An attempt to transfer under the pretext of "blocking the account" (classic vishing).
• Non-standard recipients:
  • Transfer to an individual with a history of payments only to legal entities.

How do banks apply these rules?​

1. Risk Scoring: Each transaction is assigned a threat score (e.g. 1 to 100).
   • A $10 payment at a local cafe = 5 points.
   • Transfer $2,000 to China from a new device = 85 points.
2. Automatic actions:
   • 0–30 points: Pass.
   • 30–70 points: 2FA (SMS, push) required.
   • 70+ points: Blocking + call to the client.

Examples from practice​

• Case 1:
  • The client always paid in the USA → sudden transfer of $3000 to Cyprus.
  • Bank action: Freeze + call. It turned out that the account was hacked.
• Case 2:
  • 10 payments of $10 on Steam in 5 minutes.
  • Bank action : Blocking the card (testing stolen data).

How to avoid false blocking?​

• Notify your bank about trips/large payments.
• Use native devices for translations.
• Enable all 2FA methods (TOTP is better than SMS).

Banks are constantly updating their rules, but so are scammers, so it’s important to practice digital hygiene.