Score based decisioning

• US 8,175,897 B2
• Filed: 04/20/2007
• Issued: 05/08/2012
• Est. Priority Date: 09/08/1992
• Status: Expired due to Fees

• Current Assignee: Fair Isaac Corporation
• Original Assignee: Fair Isaac Corporation
• Inventors: Lee, Walter W., Shoham, Daniel, Wilhelm, Wesley K., Milana, Joseph P., Jost, Allen P.
• Primary Examiner(s): Najarian, Lena
• Application Number:
  US11/738,362
• Publication Number:
  US 20070288641A1
• Time in Patent Office: 1,845 Days
• Field of Search: 705/26, 706/21
• US Class Current: 705/4
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]
• CPC Class Codes: G06Q 30/0609 Buyer or seller confidence or verification
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

The invention aids an entity operating on the Internet or on another network to selectively request additional data about a user who has made a request for an interaction with the entity. The invention helps an entity to determine when and how to request additional data so as to reduce the likelihood of causing the user to have an adverse reaction, e.g., terminate the interaction. One embodiment of the invention concerns customers requesting transactions with on-line merchants. More specifically, this embodiment aids merchants by detecting Internet credit card transactions that are likely to be fraudulent, and providing the merchants with mechanisms for managing a suspected transaction as it occurs to obtain additional information that can be useful to reducing the likelihood of fraud.

Claims

1. A computer-implemented method for processing a request for an interaction over a computer network, the method being implemented by one or more data processors and comprising:

receiving, by at least one data processor, a request for an interaction from a user, the request including a first set of interaction data for variables descriptive of the interaction;

determining, by at least one data processor, a value of the interaction based on the first set of interaction data; and

responsive to the value of the interaction, performing, by at least one processor;

terminating the interaction if the value is at or above a first threshold;

proceeding with the request for an interaction if the value is at or below a second threshold; and

obtaining additional data from the user in order to facilitate the determination of whether to proceed with the interaction if the value is between the first threshold and the second threshold by;

determining, by at least one data processor for each of a plurality of follow-up question sets, a probability of non-attrited fulfillment after use of the follow-up question set based on a metric of the value of additional data and on a likelihood that the user will terminate the interaction if the user is presented the follow-up question set, and

presenting, by at least one data processor to the user, the follow-up question set with the greatest probability of non-attrited fulfillment.

2. The computer-implemented method of claim 1, wherein receiving a request for an interaction comprises:

generating and forwarding, by at least one data processor, to a user'"'"'s computer, by at least one data processor, a form for obtaining the first set of interaction data.

3. The computer-implemented method of claim 1, wherein receiving a request for an interaction from a user comprises:

determining, by at least one data processor for each of a plurality of question sets, a probability of non-attrited fulfillment after use of the question set based on a metric of the value of data provided by the question set and on a likelihood that the user will terminate the interaction if the user is presented the follow-up question set; and

presenting, by at least one data processor, to the user the question set with the greatest probability of non-attrited fulfillment.

4. The computer-implemented method of claim 1, wherein the additional data is obtained and obtaining additional data comprises:

generating and forwarding, by at least one data processor, a request for additional data to a user'"'"'s computer.

5. The computer-implemented method of claim 4, wherein generating and forwarding a request for additional data comprises generating and forwarding, by at least one data processor, to a user'"'"'s computer a form for obtaining additional data.

6. The computer-implemented method of claim 1, wherein the additional data is obtained and obtaining additional data comprises:

determining, by at least one data processor for each of a plurality of follow-up question sets, an expected value of fulfillment after use of the follow-up question set; and

presenting, by at least one data processor, to the user a follow-up question set having at least a preselected expected value of fulfillment.

7. The computer-implemented method of claim 6, wherein determining an expected value of fulfillment is a function of at least one of:

the value of additional data;

the likelihood that the user will terminate the interaction if the user is presented the follow-up question set; and

the monetary value of the transaction.

8. The computer-implemented method of claim 1, wherein the additional data is obtained and obtaining additional data further comprises prior to determining a probability of non-attrited fulfillment:

calculating, by at least one data processor for each of a plurality of follow-up question sets, a metric for the value of additional data provided by a follow-up question set; and

determining, by at least one data processor for each of a plurality of follow-up question sets, the likelihood that the user will terminate the interaction if the user is presented with the follow-up question set.

9. The computer-implemented method of claim 1, wherein the method further comprises:

obtaining, by at least one data processor, a second set of interaction data in response to the follow-up question set;

determining, by at least one data processor, a revised value of the interaction based on data including the second set of interaction data; and

responsive to the revised value of the interaction, terminating, by at least one data processor, the interaction or fulfilling the request for an interaction.

10. The computer-implemented method of claim 1, wherein the probability of non-attrited fulfillment is a function of the probability of non-attrition after presentment of the follow-up question set and a function of the probability that the interaction value will exceed a threshold if the interaction value is based on data that includes data provided in response to the follow-up question set.

11. The computer-implemented method of claim 1, wherein the additional data is obtained and obtaining additional data comprises:

determining, by at least one data processor, a probability of non-attrited fulfillment after presentment of a follow-up question set based on a metric for the value of additional data provided by the follow-up question set and based on a likelihood that the user will terminate the interaction once presented with the follow-up question set.

12. The computer-implemented method of claim 11, further comprising:

calculating, by at least one data processor, a metric for the value of additional data provided by a follow-up question set by applying data representing the follow-up question set and the initial interaction data to a predictive metric model.

13. The computer-implemented method of claim 11, wherein calculating a metric for the value of additional data provided by a follow-up question set comprises:

applying, by at least one data processor, data representing the follow-up question set, initial interaction data, and user data to a predictive metric model.

14. The computer-implemented method of claim 11, wherein determining the probability of non-attrited fulfillment after presentment comprises:

applying, by at least one data processor, the follow-up question set and the initial interaction data to a predictive friction model, the predictive friction model derived from pre-processing past interaction data for a plurality of users to derive past attrition related variables, the past interaction data including variables descriptive of a past interaction and at least one variable descriptive of whether the interaction was attrited.

15. The computer-implemented method of claim 11, further comprising:

calculating, by at least one data processor, a metric for the value of additional data provided by a follow-up question set by applying data representing the follow-up question set to a predictive metric model.

16. The computer-implemented method of claim 15, wherein the predictive metric model is trained by:

(a) scoring, by at least one data processor, a set of transactions with and without data provided by a follow up question set;

(b) determining, by at least one data processor, the difference in scores with and without the data provided by the follow-up question set; and

(c) training, by at least one data processor, the predictive metric model based on the difference in scores.

17. The computer-implemented method of claim 15, wherein the predictive metric model is trained by:

(a) obtaining, by at least one data processor, data for a set of transactions that have at least a specified transaction score;

(b) scoring, by at least one data processor, the set of transactions without data provided by a follow-up question set; and

(c) calculating, by at least one data processor, a metric for the value of additional data provided by a follow-up question set based on the likelihood that the data provided by the follow-up question set will move a score for a transaction past a specified transaction score.

18. A system for processing a request for an interaction over a computer network, the system comprising:

one or more data processors;

memory for storing instructions, which when executed by at least one data processor, result in operations comprising;

receiving a request for an interaction from a user, the request including a first set of interaction data for variables descriptive of the interaction;

determining a value of the interaction based on the first set of interaction data; and

responsive to the value of the interaction, performing;

terminating the interaction if the value is at or above a first threshold;

proceeding with the request for an interaction if the value is at or below a second threshold; and

obtaining additional data from the user in order to facilitate the determination of whether to proceed with the interaction if the value is between the first threshold and the second threshold by;

determining, for each of a plurality of follow-up question sets, a probability of non-attrited fulfillment after use of the follow-up question set based on a metric of the value of additional data and on a likelihood that the user will terminate the interaction if the user is presented the follow-up question set, and

presenting to the user the follow-up question set with the greatest probability of non-attrited fulfillment.

19. A system for processing a request for an interaction over a computer network, the system comprising:

means for receiving a request for an interaction from a user, the request including a first set of interaction data for variables descriptive of the interaction;

means for determining a value of the interaction based on the first set of interaction data; and

means, responsive to the value of the interaction, for performing;

terminating the interaction if the value is at or above a first threshold;

proceeding with the request for an interaction if the value is at or below a second threshold; and

obtaining additional data from the user in order to facilitate the determination of whether to proceed with the interaction if the value is between the first threshold and the second threshold by;

determining, for each of a plurality of follow-up question sets, a probability of non-attrited fulfillment after use of the follow-up question set based on a metric of the value of additional data and on a likelihood that the user will terminate the interaction if the user is presented the follow-up question set, and

presenting to the user the follow-up question set with the greatest probability of non-attrited fulfillment.