Abstract: A measurement associated with a component being monitored is received. An operational variance of the component is detected based, at least in part, on the measurement. A variance intensity associated with the operational variance is determined and a variance intensity threshold associated with the variance intensity is determined.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods are provided for estimating a value for a target variable. A plurality of known entities are assigned to cells of a grid, where the known entities are assigned to the cells based upon attribute data. A determination is made as to whether each cell has at least a threshold number of assigned known entities. When one of the cells contains fewer than the threshold number of known entities, cells are combined to form a super cell. A model is generated for each cell and super cell based upon target variable values for known entities assigned to that cell or super cell. Data for a target entity is received, and the target entity is assigned to one the cells. One of the models is selected based upon the cell assignment, and an estimate is generated for the target variable for the target entity using the selected model.

Abstract: Systems and methods for using historical and current financial transaction data in implementing a marketing strategy are provided. A system and method can include updating stored signature data using current data associated with an entity. The signature data includes historic data including credit card transactions or debit card transactions associated with the entity. One or more model variables are generated using the updated signature data associated with the entity. A marketing score for the entity is determined by applying one or more model variables to a marketing model. The marketing score indicates a likelihood that the entity will respond to an offer. Whether the marketing score exceeds a predetermined marketing threshold is determined. Based upon determining that the marketing score exceeds the predetermined marketing threshold and determining that the entity is within the geographic area, an indication for triggering transmission of the offer to the entity is generated.

Abstract: Systems and methods for using online activity data in implementing a marketing strategy are provided. A system and method can include generating, on a computing device, variables using signature data that includes historic clickstream data and current clickstream data associated with an entity. A subset of the variables can be identified using a covariance matrix for the variables. Scores can be generated by applying the subset of the variables to models. Weighted scores can be generated by associating weights with the scores. The weighted scores can be used for selecting online advertisements. Target data can be received that includes online advertisement click data associated with the entity. New scores of the current data can be generated using the models. The weights associated with the new scores can be modified using the target data.

Abstract: Systems and methods are provided for estimating a value for a target variable. A plurality of known entities are assigned to cells of a grid, where the known entities are assigned to the cells based upon attribute data. A determination is made as to whether each cell has at least a threshold number of assigned known entities. When one of the cells contains fewer than the threshold number of known entities, cells are combined to form a super cell. A model is generated for each cell and super cell based upon target variable values for known entities assigned to that cell or super cell. Data for a target entity is received, and the target entity is assigned to one the cells. One of the models is selected based upon the cell assignment, and an estimate is generated for the target variable for the target entity using the selected model.

Abstract: 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.

Abstract: Systems and methods are provided for providing real-time scoring of received transaction data. Transaction data describing a particular transaction that has occurred is received. The transaction data is stored in an enterprise database, where the enterprise database is configured to store transactions of disparate types, where the transaction data is stored using a plurality of segments, where a segment is formatted according to a template, and where the template is selected based on an attribute of the transaction, wherein the attribute is a customer attribute, an activity attribute, or a channel attribute. A transaction type of the particular transaction is determined. One or more models are selected from a pool of models based on the transaction type, wherein the one or more models are configured based on a plurality of records from the enterprise database, and a score of the received transaction data is generated based on the transaction data.

Abstract: Computer-implemented systems and methods for determining one or more actions to be taken with respect to a first entity. A computer-implemented method can be configured to receive data that is related to characteristics of the first entity as well as data that is related to a plurality of segments. Assignments are determined between the first entity and the segments based upon the characteristics of the first entity and the characteristics associated with the segments. A determined assignment includes a membership probability that is indicative of how probable is membership of the first entity with respect to a segment. One or more actions are determined for the first entity based upon the membership probabilities and action information associated with the assigned segments.

Abstract: Computer-implemented systems and methods for analyzing activities associated with accesses of a computer network. A computer-implemented method can be configured to receive data related to the activities associated with the accesses of a computer network. The network activities data are segmented into a plurality of network activities segments. For each of the network activities segments, an anomaly detection predictive model is generated. The generated predictive models are for use in analyzing the activities associated with the computer network.