Detecting and measuring risk with predictive models using content mining

US8032448B2 · kind B2 · utility

Assignee

• Fair Isaac Corporation · US

Inventors

• Russell W. Anderson · Mahwah, US
• Larry Peranich · San Diego, US
• Ricardo M. Dungca · San Diego, US
• Joseph P. Milana · San Diego, US
• Xuhui Shao · San Diego, US
• Paul C. Dulany · San Diego, US
• Khosrow Hassibi · Danville, US
• James C. Baker · Woodbury, US

Key dates

• Filing date: Oct 4, 2007
• Grant date: Oct 4, 2011
• Expiry date: Oct 10, 2029

Classification

• Technology area (CPC G): Physics
• CPC primary: G06Q40/12
• WIPO field: IT methods for management
• WIPO sector: Electrical engineering

Abstract

Computer implemented methods and systems of processing transactions to determine the risk of transaction convert high categorical information, such as text data, to low categorical information, such as category or cluster IDs. The text data may be merchant names or other textual content of the transactions, or data related to a consumer, or any other type of entity which engages in the transaction. Content mining techniques are used to provide the conversion from high to low categorical information. In operation, the resulting low categorical information is input, along with other data, into a statistical model. The statistical model provides an output of the level of risk in the transaction. Methods of converting the high categorical information to low categorical clusters, of using such information, and other aspects of the use of such clusters are disclosed.