Patent application number | Description | Published |
20090192855 | 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 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. | 07-30-2009 |
20090192957 | 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 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. | 07-30-2009 |
20120317008 | Computer-Implemented Systems And Methods For Handling And Scoring Enterprise Data - Systems and methods for storing transaction data associated with transactions of disparate types are provided. Transaction data is received describing a transaction that has occurred, the transaction being performed by an customer of a particular customer type and the transaction being performed using a channel of a particular channel type. Transaction data about the customer is stored in an customer segment according to one of a plurality of customer templates, the one of the plurality of customer templates being selected according to the customer type. Transaction data about the channel is stored in a channel segment according to one of a plurality of channel templates, the one of the plurality of channel templates being selected according to the channel type. Data from the customer segment, the activity segment, and the channel segment for the transaction is extracted and scored by a predictive model. | 12-13-2012 |
20120317013 | Computer-Implemented Systems And Methods For Scoring Stored Enterprise Data - Systems and methods are provided for scoring transaction data representative of transactions of disparate types transaction data describing a transaction that has occurred is received. The transaction data is stored in a plurality of segments, where a segment is formatted according to a template, where the template is selected based on an attribute of the transaction, and where the attribute is a customer attribute, an activity attribute, or a channel attribute. Transaction data associated with a segment is aggregated based on a particular attribute. The aggregate transaction data is provided to a predictive model to generate a fraud score. New transaction data is received describing a new transaction, wherein the new transaction includes the particular attribute. A real-time score is provided indicating a likelihood of fraud for the new transaction, wherein the score is based at least in part on the fraud score generated using the aggregate transaction data. | 12-13-2012 |
20120317027 | Computer-Implemented Systems And Methods For Real-Time Scoring Of Enterprise Data - 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. | 12-13-2012 |
20130339218 | 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 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. | 12-19-2013 |
20130346350 | COMPUTER-IMPLEMENTED SEMI-SUPERVISED LEARNING SYSTEMS AND METHODS - 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. | 12-26-2013 |
20140172547 | Scoring Online Data for Advertising Servers - 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. | 06-19-2014 |
20140172551 | Using Transaction Data and Platform for Mobile Devices - 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. | 06-19-2014 |
20140172690 | Systems and Methods For Matching Domain Specific Transactions - Systems and methods for matching domain-specific transactions are provided. Some of the disclosed systems and methods can include receiving, on a computing device, transaction data associated with an entity, retrieving signature data associated with the entity, wherein the signature data includes historic data associated with the entity; updating the signature data to include the transaction data, wherein updating includes using a model, and generating a score for the transaction data using the updated signature data and the model. The disclosed system and method further includes receiving new transaction data associated with the entity; retrieving the updated signature data associated with the entity; determining whether the transaction data and the new transaction data are related, and if so, updating the transaction data with the new transaction data, and generating a score for the updated transaction data using the updated signature data and the model. | 06-19-2014 |