How Fraud Score Works

[chushpan]

What is Fraud Score?​

Fraud Score is a numerical score used to estimate the likelihood that a particular transaction or activity is fraudulent. Fraud Score is calculated by analyzing user, transaction, and behavioral data to help banks, payment systems, and merchants make decisions about whether to block or approve a transaction.

How does Fraud Score work?​

Fraud Score is based on the analysis of many factors that may indicate fraudulent activity (carding). Here are the main stages of work:

1. Data collection​

The system collects data from various sources for risk analysis. This data includes:

• User data:
  • IP address.
  • Geographical location (country, region).
  • Device (device type, operating system, browser).
  • History of user actions (e.g. previous transactions).
• Transaction details:
  • Amount and type of transaction.
  • Time of transaction execution.
  • Frequency of operations in a short period of time.
• Card details:
  • Billing Address matches the card issuer address.
  • Checking CVV/CVC and other card parameters.
• Additional information:
  • Using TOR, VPN or proxy servers.
  • Presence of suspicious patterns (for example, purchase of highly liquid goods).

2. Data Analysis​

The collected data is analyzed using algorithms and technologies:

a. Rules and thresholds​

• The system checks the transaction for compliance with pre-set rules. For example:
  • If a transaction is made from a country with a high fraud rate, this adds points to the Fraud Score.
  • If the transaction amount is significantly higher than the average for a given client, this also increases the risk.

b. Machine learning (ML)​

• Machine learning algorithms analyze historical data to identify fraud patterns.
• For example, if fraudsters have frequently used certain IP addresses or devices in the past, the system takes this into account when calculating the Fraud Score.

c. Clustering and linkage analysis​

• The system can group suspicious accounts or IP addresses to identify organized attacks.
• For example, if multiple transactions occur from the same device but use different cards, this could be a sign of fraud.

3. Fraud Score Calculation​

Based on the data analysis, the system assigns each transaction a numerical score that reflects the level of risk. Typically, the scale looks like this:

• Low risk: 0–30 points.
  • The transaction is considered secure.
  • The action is approved without further checks.
• Average risk: 31–70 points.
  • The transaction requires additional verification (for example, a code request via SMS or push notification).
• High risk: 71–100 points.
  • The transaction is blocked or sent for manual review.

4. Fraud Score Reaction​

After calculating the Fraud Score, the system decides on further actions:

• Transaction approval:
  • If the Fraud Score is low, the transaction is automatically approved.
• Additional check:
  • If the Fraud Score is in the middle zone, the system may request:
    • Confirmation via 3D Secure.
    • Entering the code from the SMS.
    • Biometric authentication.
• Transaction blocking:
  • If the Fraud Score is high, the transaction is blocked and a notification is sent to the client.

5. Example of Fraud Score in action​

Scenario: Online Shopping​

1. A customer makes a $2,000 purchase from a new device.
2. The system collects data:
   • The client's IP address is located in a country with a high fraud rate (+30 points).
   • This is the first login from a new device (+20 points).
   • The transaction amount is significantly higher than average for this client (+25 points).
3. Fraud Score is calculated: 30 + 20 + 25 = 75 points.
4. Since the score is high, the system blocks the transaction and sends a notification to the client.

6. Advantages of Fraud Score​

1. Automation:
   • The system quickly analyzes transactions and makes decisions, minimizing human involvement.
2. Flexibility:
   • Fraud Score can be customized to meet specific business needs (e.g. change thresholds).
3. Scalability:
   • Suitable for processing large volumes of transactions.
4. Forecasting:
   • Machine learning algorithms help predict new fraud methods.

7. Limitations of Fraud Score​

1. False positives:
   • The system may incorrectly flag legitimate transactions as suspicious.
2. Dependence on data quality:
   • If the data is incomplete or inaccurate, the analysis results may be incorrect.
3. Adapting to new threats:
   • Fraudsters are constantly improving their methods, so systems need to be updated regularly.

8. Examples of systems with Fraud Score​

• FICO Falcon Fraud Manager:
  • Widely used by banks for real-time transaction analysis.
• Feedzai:
  • Machine learning-based platform for detecting fraudulent transactions.
• SAS Fraud Framework:
  • A comprehensive solution for fraud monitoring and prevention.

9. Conclusion​

Fraud Score is a powerful tool for detecting fraudulent transactions. It helps banks, payment systems and merchants minimize risks and protect their customers. However, it is important to remember that the effectiveness of the system depends on the quality of data and regular updates of algorithms.

If you have additional questions about how Fraud Score works or examples of its use, ask them!

 

[Man]

Fraud Score is a numerical risk score that banks and payment systems assign to each transaction to determine the likelihood of fraud. The higher the score, the higher the risk – and the more likely it is that the payment will be blocked.

1. How is Fraud Score calculated?​

Antifraud systems analyze hundreds of parameters in real time. Here are the key factors:

1.1 Behavioral anomalies​

• Geolocation:
  • Payment from New York, and 5 minutes later from Thailand.
  • Mismatch between IP address and map country.
• Time patterns:
  • The client usually spends $50 a day, but suddenly sends $2,000.
  • Surgery at night if usually active during the day.

1.2. Device and network data​

• New device: Login from unknown phone/PC.
• Suspicious technologies:
  • Using VPN/Tor, emulators (Bluestacks).
  • Change IMEI/Android ID before transaction.
• Data input speed:
  • Autofill CVV/CVC (like a bot).

1.3. Nature of the operation​

• Risky merchants: Casinos, crypto exchanges, sites with data leaks.
• Unusual recipients: Transfer to an individual instead of the usual legal entities.
• A series of small payments (testing the card).

1.4. Social indicators​

• Pressure on the client:
  • Attempted transfer after a call "from the bank" (vishing).
  • Urgency ("The account will be blocked in 5 minutes!").

2. Fraud Score​

Each factor increases the risk score. Approximate scale:

Fraud Score	Risk level	Bank action
0–30	Short	Misses a payment.
30–70	Average	Requests 2FA (SMS, push, biometrics).
70–100	High	Blocks the operation + call to the client.

Example :

• Spending $10 at a local cafe = 10 points.
• Transfer $3,000 to Nigeria from a new device = 85 points.

3. How do banks use Fraud Score?​

3.1. Automatic decisions​

• Engine Rules:
  • If the score is >70 → blocking.
  • If payment to a "gray" merchant → 2FA requirement.
• Machine learning:
  • The system learns from historical data (for example, after a card is hacked, it adjusts the algorithms).

3.2. Investigation​

• Chain analysis:
  • Identification of "drops" - accounts for cashing out.
  • Linked to other suspicious transactions.
• Transfer of data to cyber police: If fraud is confirmed.

4. How to reduce Fraud Score for legal payments?​

• Use your favorite devices for translations.
• Notify your bank about trips and large transactions.
• Enable two-factor authentication (TOTP, biometrics).
• Avoid anonymous technologies (VPN, Tor) when making payments.

5. Case studies​

Case 1: Testing the map​

• Fraudster's actions:
  5 payments of $1 on Steam → attempt to transfer $1,000.
• Bank reaction:
  Blocking at Score >75 (pattern anomaly).

Case 2: Vishing​

• Scenario:
  A client transfers money to a "bank employee" after a call.
• Detect:
  High Fraud Score due to unusual recipient + pressure.

Conclusion​

1. Fraud Score is an algorithmic risk score based on behavior, device data and transaction type.
2. Banks block payments at high scores to protect customers.
3. False positives do happen - they can be avoided by observing "digital hygiene".

If your payment is blocked, but you are sure that it is safe, call your bank to unblock it.