Empowers analysts to prevent fraud across the enterprise by utilizing the data learned from the Fraud Risk Identifier and adding point and click deep link analysis on common customer data attributes (e.g., phone numbers, IPs, device IDs, etc.). Through visualization, the Fraud Risk Identifier Plus will reveal the point of compromise, the actors, and money mules used by cybercriminals defrauding your customers.
Provides visualizations to track data inflows and customers identified.
These are broken down into the following categories:
1. General threat types such as malware, phishing, credential replay, etc.
2. Specific threats such as malware family name, phishing websites, etc.
3. Malicious Internet infrastructure enabling attacks such as command and control panels, testing software, and malicious proxy services.
4. Specific criminal identification data such as online moniker names, email addresses, IPs, cryptocurrency wallets, chat forum names, etc.
Data Visualization & Point & Click Link Analysis
Customers identified by the FRI and all threat/attribution tags are displayed as objects.
The Point and Click interface then allows an analyst to pull additional data points from stored enterprise customer information (e.g., phone numbers, IP addresses, device fingerprints, etc.).
By combining these data elements, the FRI+ will facilitate identification of other impacted customers across the enterprise touched by the same cyber threats that the FRI could not identify using Cyber Threat Intelligence alone.
Automated Discovery & Artificial Intelligence
Unsupervised machine learning clustering algorithms automates the initial data point gathered from customers identified by the FRI.
The automation utilizes the same facility used by the Point and Click Link Analysis feature to explore likely related data points and additional victimized customers.
The results are displayed in the visualization platform allowing an analyst to manually remove extraneous data points or add newly uncovered relevant data points.
The Automated Discovery and AI improves user efficiency by quickly retrieving linked data points saving analysts from having to manually drive the entire discovery process. Analysts can focus on the higher-order, unclear links which require human attention.