	US 7,533,038 B2
	Predictive modeling of consumer financial behavior using supervised segmentation and nearest-neighbor matching
Matthias Blume, San Diego, Calif. (US); Michael A. Lazarus, San Diego, Calif. (US); Larry S. Peranich, San Diego, Calif. (US); Frederique Vernhes, Encinitas, Calif. (US); Kenneth B. Brown, San Diego, Calif. (US); William R. Caid, San Diego, Calif. (US); Ted E. Dunning, San Diego, Calif. (US); Gerald R. Russell, San Diego, Calif. (US); and Kevin L. Sitze, San Diego, Calif. (US)
Assigned to Fair Isaac Corporation, Minneapolis, Minn. (US)
Filed on Jan. 15, 2007, as Appl. No. 11/623,266.
Application 11/012812 is a division of application No. 09/679022, filed on Oct. 03, 2000, granted, now 6,839,682, filed on Jan. 04, 2005.
Application 11/623266 is a continuation of application No. 11/012812, filed on Dec. 14, 2004, granted, now 7,165,037, filed on Jan. 16, 2007.
Application 09/679022 is a continuation in part of application No. 09/306237, filed on May 06, 1999, granted, now 6,430,539, filed on Aug. 06, 2002.
Prior Publication US 2007/0244741 A1, Oct. 18, 2007
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 10/00 (2006.01)

U.S. Cl. 705—10

10 Claims

1. A computer-implemented method of predicting a target consumer's response rates to a given offer, the method being performed by execution of computer readable program code by at least one processor of at least one computer system, the method comprising:

establishing, using at least one of the processors, a set of reference consumers, where actual financial behavior data is available for each reference consumer, the actual financial behavior data including known or substantially predictable response rates to the given offer arising from having been presented with the given offer or a substantially similar offer and given a chance to respond;

for each merchant in a merchant group, utilizing data from consumer transaction records to generate, using at least one of the processors, a merchant vector characterizing the merchant by relatedness to other merchants, the merchant vector being generated using an unexpected deviation learning approach to determine values of the merchant vectors, the unexpected deviation learning approach comparing co-occurrences of merchant descriptions in the actual financial behavior data to determine if a pair of merchants are either positively or negatively concurrent, the positive or negative concurrency determining values for the merchant vectors;

for the target consumer and each of the reference consumers, computing, using at least one of the processors, a consumer vector by summarizing merchant vectors for merchants at which the consumer made purchases during a prescribed period of time;

identifying, using at least one of the processors, one or more reference consumers whose consumer vectors are substantially similar to the consumer vector of the target consumer according to predetermined criteria;

computationally determining, using at least one of the processors, a predicted response rate of the target consumer to the given offer by aggregating said actual financial behavior data for a group of consumers limited to the following: the identified consumers;

providing a machine-readable output of the prediction operation.