Abstract: A fraud detection and protection method and system are disclosed. The method and system utilize a fraud detection and protection server to monitor online commercial transactions between a webserver and a client computer, and generate a risk assessment of a user associated with the client computer. The system and method further utilize a number of beacon servers geographically dispersed in an area. Each beacon server is configured to receive packet header information associated with the online commercial transactions, analyze the packet header information for authenticating information, and send the authenticating information to the fraud detection and protection server for the risk assessment.

Abstract: A system and method for protecting identity fraud are disclosed. A system includes a detection subsystem to identify applications and/or accounts at risk of identity fraud, and a disposition subsystem to process data provided by the detection system and to determine whether identity fraud exists in the applications and/or accounts. According to an implementation, one or more neural network models are defined, each neural network model being configured to handle a class of cases related to the subject and a specific data configuration describing a case of the class. The one or more neural network models are run to generate data requests about the subject's identity, and the data requests are passed to a detection system that monitor transactions associated with the subject. Additional data associated with the transactions is requested until a threshold certainty is achieved or until available data or models are exhausted.

Abstract: A fraud detection and protection method and system are disclosed. The method and system utilize a fraud detection and protection server to monitor online commercial transactions between a webserver and a client computer, and generate a risk assessment of a user associated with the client computer. The system and method further utilize a number of beacon servers geographically dispersed in an area. Each beacon server is configured to receive packet header information associated with the online commercial transactions, analyze the packet header information for authenticating information, and send the authenticating information to the fraud detection and protection server for the risk assessment.

Abstract: A system and method for protecting identity fraud are disclosed. A system includes a detection subsystem to identify applications and/or accounts at risk of identity fraud, and a disposition subsystem to process data provided by the detection system and to determine whether identity fraud exists in the applications and/or accounts. According to an implementation, one or more neural network models are defined, each neural network model being configured to handle a class of cases related to the subject and a specific data configuration describing a case of the class. The one or more neural network models are run to generate data requests about the subject's identity, and the data requests are passed to a detection system that monitor transactions associated with the subject. Additional data associated with the transactions is requested until a threshold certainty is achieved or until available data or models are exhausted.

Abstract: A fraud detection and protection method and system are disclosed. The method and system utilize a fraud detection and protection server to monitor online commercial transactions between a webserver and a client computer, and generate a risk assessment of a user associated with the client computer. The system and method further utilize a number of beacon servers geographically dispersed in an area. Each beacon server is configured to receive packet header information associated with the online commercial transactions, analyze the packet header information for authenticating information, and send the authenticating information to the fraud detection and protection server for the risk assessment.

Abstract: A system and method for protecting identity fraud are disclosed. A system includes a detection subsystem to identify applications and/or accounts at risk of identity fraud, and a disposition subsystem to process data provided by the detection system and to determine whether identity fraud exists in the applications and/or accounts. According to an implementation, one or more neural network models are defined, each neural network model being configured to handle a class of cases related to the subject and a specific data configuration describing a case of the class. The one or more neural network models are run to generate data requests about the subject's identity, and the data requests are passed to a detection system that monitor transactions associated with the subject. Additional data associated with the transactions is requested until a threshold certainty is achieved or until available data or models are exhausted.

Abstract: A system and method for protecting identity fraud are disclosed. A system includes a detection subsystem to identify applications and/or accounts at risk of identity fraud, and a disposition subsystem to process data provided by the detection system and to determine whether identity fraud exists in the applications and/or accounts. According to an implementation, one or more neural network models are defined, each neural network model being configured to handle a class of cases related to the subject and a specific data configuration describing a case of the class. The one or more neural network models are run to generate data requests about the subject's identity, and the data requests are passed to a detection system that monitor transactions associated with the subject. Additional data associated with the transactions is requested until a threshold certainty is achieved or until available data or models are exhausted.

Abstract: A system and method for protecting identity fraud are disclosed. A system includes a detection subsystem to identify applications and/or accounts at risk of identity fraud, and a disposition subsystem to process data provided by the detection system and to determine whether identity fraud exists in the applications and/or accounts. According to an implementation, one or more neural network models are defined, each neural network model being configured to handle a class of cases related to the subject and a specific data configuration describing a case of the class. The one or more neural network models are run to generate data requests about the subject's identity, and the data requests are passed to a detection system that monitor transactions associated with the subject. Additional data associated with the transactions is requested until a threshold certainty is achieved or until available data or models are exhausted.

Abstract: A method includes obtaining from a first financial organization first information relating to a first financial account indicative of financial performance for the first financial account, obtaining from a second financial organization independent of the first financial organization second information relating to a second financial account indicative of financial performance for the second financial account, and determining if the first financial account and the second financial account relate to a common customer.

Abstract: The similarity between two data objects of the same type (e.g., two resumes, two job descriptions, etc.) is determined using predictive modeling. A basic assumption is that training datasets are available containing compatibility measures between objects of the first type and data objects of a second type, but that training datasets measuring similarity between objects of the first type are not. A first predictive model is trained to assess compatibility between data objects of a first type and data objects of a second type. Then, in one scenario, pairs of objects of the first type are compared for similarity by running them through the first predictive model as if one object of the pair is an object of the first type and the other object of the pair is an object of the second type.

Abstract: A method and apparatus is disclosed for making predictions about entities represented in documents and for information analysis of text documents or the like, from a large number of such documents. Predictive models are executed responsive to variables derived from canonical documents to determine documents containing desired attributes or characteristics. The canonical documents are derived from standardized documents, which, in turn, are derived from original documents.

Abstract: An apparatus for rental of articles such as videocassettes comprises an outer housing, and a storage assembly within the housing having storage locations for storing a plurality of videocassettes. The housing has a front panel including an array of separate display areas each containing pictorial indicia representing a particular videocassette and a predetermined selection code corresponding to that videocassette, a selection device adjacent the array for allowing a user to enter the selection code of a selected videocassette, and a delivery chute for delivering selected videocassettes to users. A transport mechanism is provided for retrieving selected videocassettes from the storage assembly for delivery to users, and for returning videocassettes to the storage assembly. A control device associates each stored videocassette with its storage location and controls the transport mechanism to deliver selected videocassettes to customers.