Fraud Protection Platform

Create unique device fingerprints based on a combination of both constant (hardware) and variable (software) parameters

Utilize canvas and WebGL fingerprinting techniques to generate highly distinctive browser fingerprints for accurate device tracking

Connect multiple device IDs to a single physical device, even across browser updates and changes in device configurations

Utilize a broader range of device and network parameters to provide enhanced short-term device identification, even in challenging scenarios such as virtual machine usage

Apply device fingerprints in the Graph section to identify connections between devices and user accounts, uncovering suspicious patterns and potential fraud rings

Streamline user authentification and authorizations across devices and SIM cards

Minimize false positives to ensure the seamless flow

Avoid degrading the overall user experience

Collect data from both web and mobile applications, including mouse movements, keystrokes, screen touches, and sensor data

Create unique behavioral profiles for each user, encompassing digital interaction patterns, access preferences, and device usage

Compare user behavior against median profiles generated for similar user groups or segments to identify potential outliers and suspicious patterns

Continuous coverage of all digital channels - webpage or mobile apps, user enter logins and passwords

Independently create conditions for responding to newly identified cases of fraud

Benefit from our all-team friendly no-code solution

Modify rules and adapt them to your current needs

Defend your users from social engineering (information theft, caller ID spoofing, buyer fraud, or banking scams etc.)

Enrich your anti-scam systems

Protect the personal and sensitive data of your users with flexible configuration and call timeline analysis

Uncover malicious activity, VPN usage, and server information

Evaluate IP, ISP, and subnet risk levels for informed decision-making

Get detailed information on geolocation, ISP, and network attributes

Easily integrate with your existing systems using JSON format via API

Identify deliberate blocking or alteration of JavaScript APIs used for browser fingerprinting

Detect the use of noise injection techniques employed by antidetect browsers to distort canvas fingerprints

Leverage machine learning models to flag unusual combinations of device parameters, browser versions, and geographic locations

Identify inconsistencies in reported WebGL capabilities, video card information, and User-Agent strings to pinpoint attempts at browser spoofing

Generate dynamic risk scores based on detected anomalies and inconsistencies, empowering fraud teams to take swift action

Provide clear explanations for each risk score, highlighting the key factors and events contributing to the assessment

Explain the specific rules and thresholds triggered, allowing for easy comprehension and auditability of fraud detection processes

Empower fraud analysts to review and understand AI-driven decisions, fostering trust and collaboration between human expertise and machine intelligence

Constantly get threat intelligence insights to fine-tune your security measures

Use the Group-IB Threat Intelligence solution to identify and block known fraudsters, compromised credentials, and other threats in real-time

Follow the global threat trends and stay vigilant

Detect mule accounts through device fingerprinting, unusual device usage, and anomalous activity patterns

Uncover complex mule networks by linking accounts with devices or global IDs associated with known mule activity

Trigger alerts and risk scores for suspicious accounts, enabling timely investigation and intervention

Leverage machine learning models and behavioral analytics to identify subtle mule behavior patterns

Create and manage blocklists of known mule devices and identities to actively prevent fraudulent transactions

Identify suspicious behavior patterns, such as the changing of default SMS applications or enabling of accessibility services

Record and analyze user sessions to detect in-progress attacks, including phishing attempts

Leverage a combination of rules to identify both the presence of malicious software and the telltale actions they perform

Create and manage blocklists of known malicious applications and suspicious behaviors to prevent future attack

Detect known malware and trojans by analyzing application attributes, including installation sources, package hashes, requested permissions, and installation dates