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

US7165037B2 · kind B2 · utility

Assignee

• Fair Isaac Corporation · US

Inventors

• Michael A. Lazarus · San Diego, US
• Larry Peranich · San Diego, US
• Frederique Vernhes · Encinitas, US
• A. U. Mattias Blume · San Diego, US
• Kenneth B. Brown · San Diego, 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: Dec 14, 2004
• Grant date: Jan 16, 2007
• Expiry date: Dec 14, 2024

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, which 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. Supervised segmentation is applied to merchant vectors to form merchant segments. Merchant segment predictive models provide predictions of spending in each merchant segment for any particular consumer, based on previous spending by the consumer. Consumer profiles describe summary statistics of each consumer's spending in the merchant segments, and across merchant segments. Consumer profiles include consumer vectors derived as summary vectors of selected merchants patronized by the consumer. Predictions of consumer behavior are made by applying nearest-neighbor analysis to consumer vectors.