Abstract: A computer-based fraud detection system stores extracts of the payment and other activity of the payer as well as the payee by efficiently profiling their accounts, and also stores extracts of the overall global or segmented payment activities. This profile information is then used to effectively capture fraudulent activity. The fraud detection system accepts messages from the payment processing system or from the associated financial institution, and depending on the nature of the message will update its databases, or assess fraud risk by computing a score, or both. If a score is computed, the higher the score, the greater the likelihood that the transaction is fraudulent. Said score is based on automatically determined risky values of variables and is also be automatically scaled using self-calibrating analytics using the captured profile information.

Abstract: A computer-implemented method for technologically improving a computer-implemented machine-learning model, the method comprising receiving, by a model, at least a first data record; generating a first score representing a first likelihood that the first data record is associated with a first classification, in response to feedback received from one or more data sources communicating with at least one computing system on which the model is implemented; generating a second score to represent a second likelihood that the first data record is associated with the first classification, in response to the first score being higher than a threshold value.

Abstract: A computer-implemented method includes receiving a new data record associated with a transaction, and generating, using an adaptive model executed by the computer, a score to represent a likelihood that the transaction is associated with fraud. The adaptive model employs feedback from one or more external data sources, the feedback containing information about one or more previous data records associated with fraud and non-fraud by at least one of the one or more external data sources. Further, the adaptive model uses the information about the one or more previous data records as input variables to update scoring parameters used to generate the score for the new data record.

Abstract: Methods, systems, and apparatus, including computer program products, for implementing a software architecture design for a software application implementing service request and order management. The application is structured as multiple process components interacting with each other through service interfaces, and multiple service interface operations, each being implemented for a respective process component. The process components include an Outbound Delivery process component, a Site Logistics Processing process component, a Confirmation and Inventory process component, a Customer Requirement Processing process component, a Supply and Demand Matching process component, a Logistics Execution Control process component, a Service Request Processing process component, a Customer Quote Processing process component, a Service Order Processing process component, a Service Contract process component, and a Service Confirmation Processing process component.

Abstract: A computer-implemented method and system for automated entity identification for efficient profiling in an event probability prediction system. A first subset of entities belonging to one or more entity classes is defined. At least one historical profile is constructed for each entity in the subset of entities based on a set of possible outcomes of transaction behavior of each entity in the first subset of entities. Based on the historical profiles, a second subset of entities having transaction behavior associated with a transaction is selected, the transaction behavior being predictive of at least one targeted outcome from the set of possible outcomes. The first subset of entities is redefined with the second subset of entities.

Abstract: A computer-implemented method and system for automated entity identification for efficient profiling in an event probability prediction system. A first subset of entities belonging to one or more entity classes is defined. At least one historical profile is constructed for each entity in the subset of entities based on a set of possible outcomes of transaction behavior of each entity in the first subset of entities. Based on the historical profiles, a second subset of entities having transaction behavior associated with a transaction is selected, the transaction behavior being predictive of at least one targeted outcome from the set of possible outcomes. The first subset of entities is redefined with the second subset of entities.

Abstract: Methods, systems, and apparatus, including computer program products, for implementing a software architecture design for a software application implementing plan-driven procurement. The application is structured as multiple process components interacting with each other through service interfaces, and multiple service operations, each being implemented for a respective process component. The process components include an Inbound Delivery Processing process component, a Site Logistics Processing process component, an Inventory Processing process component, a Purchase Request Processing process component, a Purchase Order Processing process component, a Purchasing Contract process component, a Supplier Invoice Processing process component, a Demand Forecast Processing process component, a Supply and Demand Matching process component, an External Procurement Trigger and Response process component, and a Logistics Execution Control process component.

Abstract: A computer-implemented method and system for automated entity identification for efficient profiling in an event probability prediction system. A first subset of entities belonging to one or more entity classes is defined. At least one historical profile is constructed for each entity in the subset of entities based on a set of possible outcomes of transaction behavior of each entity in the first subset of entities. Based on the historical profiles, a second subset of entities having transaction behavior associated with a transaction is selected, the transaction behavior being predictive of at least one targeted outcome from the set of possible outcomes. The first subset of entities is redefined with the second subset of entities.

Abstract: A computer-implemented method and system for automated entity identification for efficient profiling in an event probability prediction system. A first subset of entities belonging to one or more entity classes is defined. At least one historical profile is constructed for each entity in the subset of entities based on a set of possible outcomes of transaction behavior of each entity in the first subset of entities. Based on the historical profiles, a second subset of entities having transaction behavior associated with a transaction is selected, the transaction behavior being predictive of at least one targeted outcome from the set of possible outcomes. The first subset of entities is redefined with the second subset of entities.

Abstract: A computer-implemented method and system for automated entity identification for efficient profiling in an event probability prediction system. A first subset of entities belonging to one or more entity classes is defined. At least one historical profile is constructed for each entity in the subset of entities based on a set of possible outcomes of transaction behavior of each entity in the first subset of entities. Based on the historical profiles, a second subset of entities having transaction behavior associated with a transaction is selected, the transaction behavior being predictive of at least one targeted outcome from the set of possible outcomes. The first subset of entities is redefined with the second subset of entities.

Abstract: A computer-implemented technique for fuzzy matching. This works quickly yet accurately to determine if a given computer-readable record is represented, by exact match or pretty close match, in a large collection of computer-readable records. Further tools may be provided to assess the character of the match.

Abstract: Methods, systems, and apparatus, including computer program products, for implementing a software architecture design for a software application implementing plan-driven procurement. The application is structured as multiple process components interacting with each other through service interfaces, and multiple service operations, each being implemented for a respective process component. The process components include an Inbound Delivery Processing process component, a Site Logistics Processing process component, an Inventory Processing process component, a Purchase Request Processing process component, a Purchase Order Processing process component, a Purchasing Contract process component, a Supplier Invoice Processing process component, a Demand Forecast Processing process component, a Supply and Demand Matching process component, an External Procurement Trigger and Response process component, and a Logistics Execution Control process component.

Abstract: A computer-implemented method and system for automated entity identification for efficient profiling in an event probability prediction system. A first subset of entities belonging to one or more entity classes is defined. At least one historical profile is constructed for each entity in the subset of entities based on a set of possible outcomes of transaction behavior of each entity in the first subset of entities. Based on the historical profiles, a second subset of entities having transaction behavior associated with a transaction is selected, the transaction behavior being predictive of at least one targeted outcome from the set of possible outcomes. The first subset of entities is redefined with the second subset of entities.

Abstract: A computer-implemented method includes receiving a new data record associated with a transaction, and generating, using an adaptive model executed by the computer, a score to represent a likelihood that the transaction is associated with fraud. The adaptive model employs feedback from one or more external data sources, the feedback containing information about one or more previous data records associated with fraud and non-fraud by at least one of the one or more external data sources. Further, the adaptive model uses the information about the one or more previous data records as input variables to update scoring parameters used to generate the score for the new data record.

Abstract: A computer-based fraud detection system stores extracts of the payment and other activity of the payer as well as the payee by efficiently profiling their accounts, and also stores extracts of the overall global or segmented payment activities. This profile information is then used to effectively capture fraudulent activity. The fraud detection system accepts messages from the payment processing system or from the associated financial institution, and depending on the nature of the message will update its databases, or assess fraud risk by computing a score, or both. If a score is computed, the higher the score, the greater the likelihood that the transaction is fraudulent. Said score is based on automatically determined risky values of variables and is also be automatically scaled using self-calibrating analytics using the captured profile information.

Abstract: A computer-implemented technique for fuzzy matching. This works quickly yet accurately to determine if a given computer-readable record is represented, by exact match or pretty close match, in a large collection of computer-readable records. Further tools may be provided to assess the character of the match.

Abstract: Methods, systems, and apparatus, including computer program products, for implementing a software architecture design for a software application implementing service request and order management. The application is structured as multiple process components interacting with each other through service interfaces, and multiple service interface operations, each being implemented for a respective process component. The process components include an Outbound Delivery process component, a Site Logistics Processing process component, a Confirmation and Inventory process component, a Customer Requirement Processing process component, a Supply and Demand Matching process component, a Logistics Execution Control process component, a Service Request Processing process component, a Customer Quote Processing process component, a Service Order Processing process component, a Service Contract process component, and a Service Confirmation Processing process component.

Abstract: Methods, systems, and apparatus, including computer program products, for implementing a software architecture design for a software application implementing sell from stock software useful to process quotations, capture orders, process delivery and invoice. The application is structured as multiple process components interacting with each other through service interfaces, and multiple service interface operations, each being implemented for a respective process component. The process components include an Outbound Delivery process component, a Site Logistics Processing process component, a Confirmation and Inventory process component, a Customer Requirement Processing process component, a Supply and Demand Matching process component, a Logistics Execution Control process component, a Customer Quote Processing process component, a Sales Order Processing process component, and a Sales Contract Processing process component.

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 guide rail for a linear bearing has at least one rail element with a guideway for rolling bodies. The rail element is connected to a plastic body or borders the plastic body, with the plastic body forming the base body of the guide rail. To increase the precision of the guide rail, especially when used in an environment involving a wide temperature range, the rail element is connected to a metallic support frame which is surrounded at least partially by the material of the plastic body.