Abstract: A computer system for detecting and assessing fraud risk employs group-based analysis to identify complex fraud patterns across various industries. The system identifies and analyzes groups of connected incidents by linking related events based on similarities in suspect identifiers, creating a network that reveals broader fraudulent behavior patterns. When processing a new incident, the system compares it against established groups of connected incidents, detecting subtle connections that may indicate relationships to known fraud patterns. This comparison can range from basic identifier matching to sophisticated analysis of multiple data points across different incidents within a group. Based on this comparison, the system generates a comprehensive fraud risk assessment for the new incident, leveraging collective information from grouped incidents to provide a nuanced and accurate evaluation of potential fraud risk.

Abstract: A method for automatically linking associated incidents related to criminal activity is disclosed. A system for processing the method is also disclosed. The method operates by scraping text from a number of incident reports. The scraped text is then analyzed to determine the present of one or more unique IDs, which are used to calculate similarity between each incident report. The system employs machine-learning to better identify these pairs in the future, and optionally with the assistance of a human user providing a feedback loop to enhance the machine-learning.