Applying machine learning to learn relationship weightage in risk networks
Number of patents in Portfolio can not be more than 2000
United States of America
Stats
-
Aug 6, 2024
Grant Date -
N/A
app pub date -
Nov 30, 2020
filing date -
Nov 30, 2020
priority date (Note) -
In Force
status (Latency Note)
Importance
Abstract
A computer-implemented system, method and computer program product for detecting fraud. The system and method receives data representing transacting parties where a party transacts with another party to define a relationship therebetween. The received data is incorporated into a relationship network graph comprising nodes that capture data about transacting parties and corresponding information associated with each transaction. A risk model is run that is configured to determine a risk weight for the relation between nodes associated with the transacting parties based on the data captured in the relationship network graph. Then there is determined a degree of a party's association score based on risk scores of nodes associated with the party and a defined relationship with another suspicious party. The system then dynamically updates the risk weight of the relation based on the party's association score.
First Claim
Family
International Classification(s)
- [Classification Symbol]
- [Patents Count]
Cited Art Landscape
Patent Citation Ranking
Recipient Email Address
Recipient Email Address
Comment
Recipient Email Address
Add to Portfolio(s)
To add this patent to one, or more, of your portfolios, simply click the add button.
This Patent is in these Portfolios:
Add to additional portfolios:
Last Refreshed On:
Changes done successfully