Fraud Detection
Identifies collusions and patterns in digital data and
detects fraud involving collusion by calculating a suspicion
score through graph analysis.
How can you identify the patterns of relationships among suspects?
How can you shorten the time it takes to track cases of conspiracy fraud?
Is there any way to visually explore the point of suspicion?
It identifies the hidden conspiracy relationships and patterns in various digital data
(communication, finance, etc.) with traces of crime, and identifies the true nature of crimes
involving conspiracy by calculating the charge score through graph analysis.
Key Data
Currency, account data
Korea Exchange order and execution data
Insurance subscription and claims data of property and life insurers
Key Contents
Discovering and tracking the key specific patterns of crime/collusion fraud
Example) In auto accidents, when perpetrators and victims collude, a circular
relationship is formed that connects them to each other.
Calculation of suspicion score through centrality analysis in the relationship
Relationship-based analysis/visualization function automatically discovers
intermediaries connecting suspects to expand suspicion groups
Utility and Expected Effects
Increased number of detections through the relationship analysis
Reduction of the time required to analyze the relationship between conspirators
Work efficiency is increased by reducing the time required to create a
connection diagram through the exploratory visualization of conspirators
Major Customers
Insurance companies, financial institutions, and
public institutions
Check out the related materials.
Customer Stories