Operating principles of antifraud protection systems: Geocomply, MaxMind, Sift

[Cloned Boy]

Educational analysis to understand anti-fraud mechanisms.

1. Geocomply: Geolocation control for payments and gambling​

Purpose: To verify that the user is located in an approved jurisdiction (e.g. for online casinos in the US).

Technical components​

Verification method	How it works	Example of bypass (for testing)
IP analysis	Compares IP with residential address database (ISP vs data center).	Using residential proxies (but they are hard to find).
GPS/Wi-Fi triangulation	Checks GPS matches with Wi-Fi/Cell Tower data.	Physical movement of the device (not spoofing).
Digital fingerprint	Analyzes: browser, OS, screen resolution, installed fonts.	Manual User-Agent and WebGL configuration.
Behavioural analysis	Measures input speed, cursor movement, frequency of actions.	Simulating human behavior with Selenium.

Example code for GPS analysis (legal test):

# Check GPS accuracy (PyGPSd)
import gpsd
gpsd.connect()
packet = gpsd.get_current()
print(packet.position()) # Prints (lat, lon)

2. MaxMind (GeoIP): IP geolocation databases​

Purpose: Determining the country/city by IP for antifraud.

How it works​

• Uses a database of IP blocks mapped to locations.
• Analyzes ASN (which provider issued the IP).
• Assesses risk (eg IP from a country with high fraud rates).

False positives:

• VPN/Tor are marked as risky, although they can be used legally.

Example of a request to the MaxMind API (legal use):

import geoip2.database
reader = geoip2.database.Reader('GeoLite2-City.mmdb')
response = reader.city('8.8.8.8')
print(response.country.iso_code)  # 'US'

3. Sift Science: behavioral antifraud​

Goal: Detect fraudulent patterns in real time.

Key technologies​

Method	Details
Machine learning	Analyzes thousands of parameters: form filling speed, IP, device.
Connection graph	Links accounts using common data (cards, email, devices).
Risk scoring	Assigns a score from 0 to 100 (eg 80+ = blocked).

Example of operation:

• User #1 and #2 log in from the same IP, but use the same card → fraud flag.

Legal testing:

# Emulate "clean" behavior for tests
import time
from selenium import webdriver

driver = webdriver.Chrome()
driver.get("https://example.com")
time.sleep(2) # Simulate human delay

4. How companies bypass protection (and how systems evolve)​

Historical Attack Methods​

• IP Spoofing: Using VPN/Proxy → currently detected by ASN.
• Device emulation: Currently analyzing WebGL, canvas fingerprint.
• SIM swap: To bypass SMS verification → now confirmation by phone call is required.

Modern protection​

• Biometrics: Face ID, Touch ID.
• Hardware analysis: MAC address, TPM chip.
• Quantum Algorithms (in development): RSA Hacking Protection.

5. Legal Research​

To study antifraud systems:

1. Documentation:
   • MaxMind Developer Docs
   • Geocomply Integrations
2. Certifications:
   • PCI DSS for payment systems.
   • OSCP for pentesting.
3. Bug Bounty:
   • HackerOne programs (for example, PayPal).

Conclusion​

Systems like Geocomply/MaxMind/Sift use:
Geolocation (IP, GPS, Wi-Fi).
Digital fingerprinting.
Behavioral analysis.
Machine learning.

For developers: This knowledge helps to create more secure applications.
For researchers: It is important to test systems only within legal frameworks (sanctioned pentesting).

Need details on a specific analysis method? Ready to dig deeper!