Ca, Inc.

Accenture Global Solutions Limited

Sas Institute Inc.

Hongrui Gong

Revathi Subramanian

Paul Charles Dulany

Kannan Shashank Shah

Steven Lloyd Greenspan

Debra Jean Danielson

Kenneth W S Morrison

Vijay Desai

Ibrahim Ali Odeh Al-Shyoukh

A device may receive data that includes invoice data related to historical invoices from an organization, contact data related to historical contacts between the organization and various entities, and dispute data related to historical disputes between the organization and the various entities. The device may determine a profile for the data. The device may determine a set of supervised learning models for the historical invoices based on one or more of the historical contacts, the historical disputes, the historical invoices, or historical patterns related to the historical invoices. The device may determine, using the profile, a set of unsupervised learning models for the historical invoices independent of the one or more of the historical contacts, the historical disputes, or the historical patterns. The device may determine, utilizing a super model, a prediction for the invoice after the super model is trained. The device may perform one or more actions.

Predictive issue detection

In an embodiment, a password risk evaluator may receive a request including a user identifier (ID) and a password. The password risk evaluator may retrieve a password preference model associated with the user ID, and may determine a risk score indicating a likelihood that the password is associated with the user ID. For example, the password preference model may be based on previous passwords used by the user, and may identify one or more characteristics, formulas, rules, or other indicia typically employed by the user in creating passwords. If the password supplied in the request matches or is similar to one or more elements of the password preference model, it may be more likely that the password in the request is a password supplied by the user. That is, the risk score may be an authentication of the user, or part of the authentication of the user, in some embodiments.

Authenticating identity for password changes

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

Computer-implemented systems and methods for determining a subset of unknown targets to investigate. For example, a method can be configured to receive a target data set, wherein the target data set includes known targets and unknown targets. A supervised model such as a neural network model is generated using the known targets. The unknown targets are used with the neural network model to generate values for the unknown targets. Analysis with an unsupervised model is performed using the target data set in order to determine which of the unknown targets are outliers. A comparison of list of outlier unknown targets is performed with the values for the unknown targets that were generated by the neural network model. The subset of unknown targets to investigate is determined based upon the comparison.

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