Predictive modeling of consumer financial behavior using supervised segmentation and nearest-neighbor matching

US6839682B1 · kind B1 · utility

Assignee

• Fair Isaac Corporation · US

Inventors

• Matthias Blume · San Diego, US
• Michael A. Lazarus · San Diego, US
• Larry Peranich · San Diego, US
• Frederique Vernhes · Encinitas, US
• William R. Caid · San Diego, US
• Ted E. Dunning · San Diego, US
• Gerald R. Russell · San Diego, US
• Kevin Sitze · San Diego, US

Key dates

• Filing date: Oct 3, 2000
• Grant date: Jan 4, 2005
• Expiry date: Aug 1, 2022

Classification

• Technology area (CPC G): Physics
• CPC primary: G06Q30/0601
• WIPO field: IT methods for management
• WIPO sector: Electrical engineering

Abstract

Predictive modeling of consumer financial behavior, including determination of likely responses to particular marketing efforts, is provided by application of consumer transaction data to predictive models associated with merchant segments. The merchant segments are derived from the consumer transaction data based on co-occurrences of merchants in sequences of transactions. Merchant vectors represent specific merchants, and are aligned in a vector space as a function of the degree to which the merchants co-occur more or less frequently than expected. Consumer vectors are developed within the vector space, to represent interests of particular consumers by virtue of relative vector positions of consumer and merchant vectors. Various techniques, including clustering, supervised segmentation, and nearest-neighbor analysis, are applied separately or in combination to generate improved predictions of consumer behavior.