Sas Institute Inc.

Ca, Inc.

Fair Isaac Corporation

Paul Charles Dulany

Revathi Subramanian

Kannan Shashank Shah

Hongrui Gong

Brian Lee Duke

Joseph P Milana

Vijay Desai

Larry Peranich

Xuhui Shao

Radu Drossu

Ho Ming Luk

Russell Anderson

James C Baker

Kevin Chaowen Chen

Ricardo M Dungca

Khosrow M Hassibi

William Szczuka

Chao-Wen (Kevin) Chen

Andrew Engel

Daniel J Dotson

Systems and methods are provided for identifying and detecting unauthorized user activity and for decreasing the rate of false-positives. The disclosed systems and techniques may involve analysis of users' past activity data so that individual classifications and authorization decisions with respect to requested user activity are based on activity data associated with a user's use of multiple services.

Unauthorized activity detection and classification

A method of operating a computer system is disclosed. An eCommerce authentication request is received from a merchant node. The eCommerce authentication request has content including cardholder information. A risk score for the eCommerce authentication request is generated based on comparison of the cardholder information of the eCommerce authentication request to cardholder information of eCommerce authentication requests of a plurality of merchant nodes. The eCommerce authentication request is selectively provided to an authentication node based on the risk score.

Controlling eCommerce authentication based on comparing cardholder information among eCommerce authentication requests from merchant nodes

A method of operating a computer system is disclosed. An eCommerce authentication request is received from a merchant node. The eCommerce authentication request has content including merchant information. A risk score for the eCommerce authentication request is generated based on comparison of the merchant information of the eCommerce authentication request to merchant information of eCommerce authentication requests of a plurality of merchant nodes. The eCommerce authentication request is selectively provided to an authentication node based on the risk score.

Controlling eCommerce authentication based on comparing merchant information of eCommerce authentication requests

A method of operating a computer system is disclosed. An eCommerce authentication request is received. Content of the eCommerce authentication request is processed through a non-linear analytical model to generate a risk score. The eCommerce authentication request is selectively provided to an authentication node based on the risk score. Related authentication gateway nodes and computer program products are disclosed.

Controlling ecommerce authentication with non-linear analytical models

This disclosure describes methods, systems, and computer-program products for determining classification rules to use within a fraud detection system The classification rules are determined by accessing distributional data representing a distribution of historical transactional events over a multivariate observational sample space defined with respect to multiple transactional variables. Each of the transactional events is represented by data with respect to each of the variables, and the distributional data is organized with respect to multi-dimensional subspaces of the sample space. A classification rule that references at least one of the subspaces is accessed, and the rule is modified using local optimization applied using the distributional data. A pending transaction is classified based on the modified classification rule and the transactional data.

Rule optimization for classification and detection

Systems and methods are provided for operation upon data processing devices are provided for operating with a fraud detection system. As an example, a system and method can be configured for receiving, throughout a current day in real-time or near real-time, financial transaction data representative of financial transactions initiated by different entities. At multiple times throughout the day, a summarization of the financial transaction data (which has been received within a time period within the current day) is generated. The generated summarization is used to determine whether fraud has occurred with respect to a financial transaction contained in the received authorization data or with respect to a subsequently occurring financial transaction.

Computer-implemented multiple entity dynamic summarization systems and methods

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.

Detecting and measuring risk with predictive models using content mining

Systems and methods for performing fraud detection. As an example, a system and method can be configured to contain a raw data repository for storing raw data related to financial transactions. A data store contains rules to indicate how many generations or to indicate a time period within which data items are to be stored in the raw data repository. Data items stored in the raw data repository are then accessed by a predictive model in order to perform fraud detection.

Computer-implemented data storage systems and methods for use with predictive model systems

Systems and methods for performing fraud detection. As an example, a system and method can be configured to build a set of predictive models to predict credit card or debit card fraud. A first predictive model is trained using a set of training data. A partitioning criterion is used to determine how to partition the training data into partitions. Another predictive model is trained using at least one of the partitions of training data in order to generate a second predictive model. The predictive models are combined for use in predicting credit card or debit card fraud.

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San Diego, CA, United States of America

Paul Charles Dulany