Anti-fraud technologies and how they are used

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The topic will be useful for beginners in this business. If you have something to add, share the information what settings are available and what information they see.

1. Machine learning and AI:

• How it's used: Machine learning algorithms are applied to historical fraud data as well as current transactions to identify suspicious activity.
• What it tracks:
  • User behavior patterns:
    • Unusual purchases or withdrawals
    • Login attempts from different locations
    • Multiple transactions processed too quickly
  • Transaction characteristics:
    • Unusual amounts or categories of transactions
    • Shopping in suspicious stores
    • Transactions made at unusual times
• What settings are available:
  • Sensitivity level:
    • Determines how aggressively the system responds to potential threats.
  • Types of transactions that are monitored:
    • Allows you to specify which types of transactions will be subject to more thorough checking.
  • Data sources:
    • Determines which data sources will be used to train the algorithms.
• What information does it see:
  • Personal data:
    • Name, address, phone number
  • Transaction details:
    • Amounts, dates, places of execution
  • Device details:
    • IP address, device type, operating system

2. Blockchain:

• How it is used: Transactions are recorded in a decentralized database that is accessible to all participants in the system. This ensures transparency and immutability of records, making it impossible to counterfeit or double-spend transactions.
• What it tracks:
  • Movement of funds:
    • Tracks the movement of funds between different wallets.
  • User identification:
    • Links transactions to users' wallet addresses.
• What settings are available:
  • Privacy level:
    • Determines what transaction information will be publicly available.
  • Network participation:
    • Allows users to become network nodes and help validate transactions.
• What information does it see:
  • Wallet addresses:
    • Unique identifiers used to send and receive funds.
  • Transaction amounts:
    • How much money was sent or received.
  • Transaction times:
    • When the transaction was made.

3. Biometric identification:

• How it is used: Unique physiological characteristics of the user , such as fingerprints, face or iris, are used to confirm their identity .
• What it tracks:
  • User biometric data:
    • Stores images of fingerprints, faces, or irises.
  • Login attempts:
    • Compares the user's biometric data with those stored during a login attempt.
• What settings are available:
  • Biometric authentication methods:
    • Allows you to select which biometric authentication methods will be available (e.g. fingerprints, facial recognition).
  • Authentication Request Frequency:
    • Determines how often the user must authenticate (for example, every time they log in, or after a certain amount of time).
• What information does it see:
  • Biometric templates:
    • Mathematical representations of user biometric data.
  • Authentication logs:
    • Records of when and how a user authenticated.

4. HTTPS and SSL/TLS:

• How it is used: These protocols encrypt data transmitted between the user's browser and the website server.
  • What it tracks:
  • Connection between browser and server:
  • Ensures that data cannot be intercepted or read by third parties.
  • What settings are available:
  • Certificate type:
  • Determines the level of encryption security.
  • Certificate validity period:
  • Specifies how long the certificate will be valid.
  • What information does it see:
  • Contents of web pages:
  • Text, images, code, and other data displayed on a web page.
  • User details:
  • User-entered information such as name, address, and telephone number.

5. WAF (Web Application Firewall):

• How it is used: WAF analyzes incoming traffic to the site and blocks requests that may pose a threat.
• What it tracks:
• Potentially dangerous requests:
• SQL injections, XSS attacks, password cracking attempts and other types of web attacks.
• What settings are available:
• Blocking rules:
• Determines which types of requests will be blocked.
• Protection level:
• Determines how aggressively WAF will filter traffic.
• What information does it see:
• Contents of requests:
• Data sent by the client to the server.
• IP addresses:
• Addresses of devices from which requests are received.

6. IDS/IPS (Intrusion Detection and Prevention Systems):

• How it is used: IDS/IPS monitors network traffic and detects suspicious activity .
• What it tracks:
• Unauthorized access attempts:
• Port scanning, password cracking attempts, denial of service (DoS) attacks and other types of network attacks.
• What settings are available:
• Detection rules:
• Determines what types of activity will be considered suspicious.
• Actions upon detection:
• Determines what will happen when suspicious activity is detected (e.g. notifying the administrator, blocking traffic).
• What information does it see:
• Network packages:
• Units of data transmitted over a network.
• IP addresses:
• Addresses of devices participating in network traffic.

7. Advanced data analysis:

• How it is used: Big Data technologies analyze large volumes of transactional data to identify anomalies and potential fraud .
• What it tracks:
• Transaction patterns:
• Unusual amounts, purchases from suspicious stores, transactions made at unusual times.
• What settings are available:
• Methods of analysis:
• Determines what methods will be used to analyze the data.
• Threshold values:
• Determines what will be considered an anomaly.
• What information does it see:
• Transaction details:
• Amounts, dates, locations, user data.

8. Federated learning:

• How it's used: Machine learning algorithms are trained on users' devices , not on a central server.
• What it tracks:
  • User transaction data:
  • Used to train fraud detection algorithms.
• What settings are available:
  • User participation level:
    • Determines how much data users will provide to train algorithms.
  • Algorithm update frequency:
    • Determines how often the algorithms will be updated with new data.
• What information does it see:
  • Local user transaction data:
    • Data does not leave users' devices.
  • Aggregated Machine Learning Models:
    • Used to detect fraud without revealing personal information.

9. Data anonymization:

• How it's used: Anonymization techniques hide users' personal information when collecting and analyzing data.
• What it tracks:
  • Transaction details:
    • Amounts, dates, places of commission.
• What settings are available:
  • Anonymization methods:
    • Determines what methods will be used to hide personal information.
  • Level of detail:
    • Determines how much information will be saved.
• What information does it see:
  • Anonymized transaction data:
    • Personal information of users is not disclosed.
  • Aggregated statistics:
    • Used to analyze trends and detect fraud.

If you have any other questions, please feel free to ask.