Abstract: A node associated with an organization may receive a storage identifier for new credit data associated with an individual. A distributed ledger and distributed data sources may be used to share the new credit data with a network of nodes. The node may update a smart contract with the storage identifier for the new credit data. The node may receive, from a particular device associated with the organization, a request for the new credit data. The node may obtain the storage identifier for the new credit data from the smart contract. The node may obtain the new credit data by using the storage identifier to search the distributed data sources. The node may provide the new credit data to the particular device. The node may perform actions to obtain additional new credit data from the distributed data sources or provide the additional new credit data to the distributed data sources.

Abstract: A serverless computing framework is secured against malicious payload injection. A series of functions can be strung together to perform a workflow in response to a triggering event. A validator can be included with a function that verifies that an input payload originated from a trusted source. A validation value, such as a hash, can be computed based on the result payload in combination with the source code of the function that produced the result payload. A downstream function can receive the result payload and the hash and utilize the result payload and a copy of the upstream source code to produce another hash. The received and generated hashes can then be compared and utilized to control execution of the downstream function. Execution can be prevented when there is a mismatch between the hashes.

Abstract: A node associated with an organization may receive a storage identifier for new credit data associated with an individual. A distributed ledger and distributed data sources may be used to share the new credit data with a network of nodes. The node may update a smart contract with the storage identifier for the new credit data. The node may receive, from a particular device associated with the organization, a request for the new credit data. The node may obtain the storage identifier for the new credit data from the smart contract. The node may obtain the new credit data by using the storage identifier to search the distributed data sources. The node may provide the new credit data to the particular device. The node may perform actions to obtain additional new credit data from the distributed data sources or provide the additional new credit data to the distributed data sources.

Abstract: Aspects discussed herein relate to the storage of data in graph databases and detecting fraudulent behavior in the stored data. Fraud detection systems may use graph databases to store data, allowing for querying the graph database to obtain data using a variety of graph semantics such as nodes, edges, and properties. Graph databases in accordance with embodiments of the invention may include account nodes and attribute nodes, where nodes of the same type are not directly linked to each other. When a particular node is updated, an updated node may be created with a higher version number than the existing node. Each node may include an indication of the node being associated with fraudulent activity. Fraud indicators may be calculated based on the relationships between the nodes and fraud indicators for the nodes.

Abstract: Systems as described herein may include authorizing the sharing of data and sharing data between a variety of systems. A request to share data may be provided to a first system. The system may create sharing session data on a distributed ledger accessible by a number of systems. Sharing session data may be stored using a transaction stored on a distributed ledger. A second system may obtain the sharing session account and verify the sharing session. On verification of the sharing session, a variety of data may be shared between the systems identified in the sharing session data. The sharing session data may be established between two or more systems. The distributed ledger may be maintained by the systems themselves and/or a distributed network system. In a variety of embodiments, encrypted data may be stored and/or obtained using the distributed ledger.

Abstract: Web presence data is employed to determine merchant legitimacy. An address of a web page associated with a merchant named on a credit card transaction can be determined. Subsequently, a secure socket layer certificate associated with the web page can be acquired and utilized as a basis for computing a risk score of a merchant. The number of subject alternate names identified by the certificate can be determined and utilized as a factor in risk score computation. Other factors can include similarity of subject alternate names as well as web page code and content. The risk score can be compared to a threshold. When the risk score satisfies the threshold, an action can be triggered, such as generating a customer alert.

Abstract: A serverless computing framework is secured against malicious payload injection. A series of functions can be strung together to perform a workflow in response to a triggering event. A validator can be included with a function that verifies that an input payload originated from a trusted source. A validation value, such as a hash, can be computed based on the result payload in combination with the source code of the function that produced the result payload. A downstream function can receive the result payload and the hash and utilize the result payload and a copy of the upstream source code to produce another hash. The received and generated hashes can then be compared and utilized to control execution of the downstream function. Execution can be prevented when there is a mismatch between the hashes.

Abstract: Aspects discussed herein relate to the storage of data in graph databases and detecting fraudulent behavior in the stored data. Fraud detection systems may use graph databases to store data, allowing for querying the graph database to obtain data using a variety of graph semantics such as nodes, edges, and properties. Graph databases in accordance with embodiments of the invention may include account nodes and attribute nodes, where nodes of the same type are not directly linked to each other. When a particular node is updated, an updated node may be created with a higher version number than the existing node. Each node may include an indication of the node being associated with fraudulent activity. Fraud indicators may be calculated based on the relationships between the nodes and fraud indicators for the nodes.

Abstract: In some implementations, a system may identify a mitigation record in a record log associated with an account of an application. The system may append a message prompt to the mitigation record that causes the application to request, during a user session associated with the account, an indication of whether there is an association between the mitigation record and an event record. The system may receive, from a user device associated with the user session, feedback associated with the message prompt that indicates whether the mitigation record is associated with the event record. The system may perform, based on the feedback, an action associated with indicating whether the mitigation record and the event record are associated.

Abstract: Aspects discussed herein relate to the storage of data in graph databases and detecting fraudulent behavior in the stored data. Fraud detection systems may use graph databases to store data, allowing for querying the graph database to obtain data using a variety of graph semantics such as nodes, edges, and properties. Graph databases in accordance with embodiments of the invention may include account nodes and attribute nodes, where nodes of the same type are not directly linked to each other. When a particular node is updated, an updated node may be created with a higher version number than the existing node. Each node may include an indication of the node being associated with fraudulent activity. Fraud indicators may be calculated based on the relationships between the nodes and fraud indicators for the nodes.

Abstract: Systems as described herein may include authorizing the sharing of data and sharing data between a variety of systems. A request to share data may be provided to a first system. The system may create sharing session data using smart contracts executed by a distributed network system. Sharing session data may be stored using a smart contract. A second system may obtain the sharing session data and verify the sharing session based on the execution of the smart contract. On verification of the sharing session, a variety of data may be shared between the systems identified in the sharing session data. The sharing session data may be established between two systems and/or a number of systems. Smart contracts may provide a variety of functions for authorizing the sharing of data between systems. Additionally, encrypted data may be stored and/or obtained using a smart contract.

Abstract: Systems as described herein may include authorizing the sharing of data and sharing data between a variety of systems. A request to share data may be provided to a first system. The system may create sharing session data on a distributed ledger accessible by a number of systems. Sharing session data may be stored using a transaction stored on a distributed ledger. A second system may obtain the sharing session account and verify the sharing session. On verification of the sharing session, a variety of data may be shared between the systems identified in the sharing session data. The sharing session data may be established between two or more systems. The distributed ledger may be maintained by the systems themselves and/or a distributed network system. In a variety of embodiments, encrypted data may be stored and/or obtained using the distributed ledger.

Abstract: Systems as described herein may include authorizing the sharing of data and sharing data between a variety of systems. A request to share data may be provided to a first system. The system may create sharing session data on a distributed ledger accessible by a number of systems. Sharing session data may be stored using a transaction stored on a distributed ledger. A second system may obtain the sharing session account and verify the sharing session. On verification of the sharing session, a variety of data may be shared between the systems identified in the sharing session data. The sharing session data may be established between two or more systems. The distributed ledger may be maintained by the systems themselves and/or a distributed network system. In a variety of embodiments, encrypted data may be stored and/or obtained using the distributed ledger.

Abstract: Systems as described herein may include authorizing the sharing of data and sharing data between a variety of systems. A request to share data may be provided to a first system. The system may create sharing session data using smart contracts executed by a distributed network system. Sharing session data may be stored using a smart contract. A second system may obtain the sharing session data and verify the sharing session based on the execution of the smart contract. On verification of the sharing session, a variety of data may be shared between the systems identified in the sharing session data. The sharing session data may be established between two systems and/or a number of systems. Smart contracts may provide a variety of functions for authorizing the sharing of data between systems. Additionally, encrypted data may be stored and/or obtained using a smart contract.

Abstract: A node associated with an organization may receive a storage identifier for new credit data associated with an individual. A distributed ledger and distributed data sources may be used to share the new credit data with a network of nodes. The node may update a smart contract with the storage identifier for the new credit data. The node may receive, from a particular device associated with the organization, a request for the new credit data. The node may obtain the storage identifier for the new credit data from the smart contract. The node may obtain the new credit data by using the storage identifier to search the distributed data sources. The node may provide the new credit data to the particular device. The node may perform actions to obtain additional new credit data from the distributed data sources or provide the additional new credit data to the distributed data sources.

Abstract: Aspects discussed herein relate to the storage of data in graph databases and detecting fraudulent behavior in the stored data. Fraud detection systems may use graph databases to store data, allowing for querying the graph database to obtain data using a variety of graph semantics such as nodes, edges, and properties. Graph databases in accordance with embodiments of the invention may include account nodes and attribute nodes, where nodes of the same type are not directly linked to each other. When a particular node is updated, an updated node may be created with a higher version number than the existing node. Each node may include an indication of the node being associated with fraudulent activity. Fraud indicators may be calculated based on the relationships between the nodes and fraud indicators for the nodes.

Abstract: A node associated with an organization may receive a storage identifier for new credit data associated with an individual. A distributed ledger and distributed data sources may be used to share the new credit data with a network of nodes. The node may update a smart contract with the storage identifier for the new credit data. The node may receive, from a particular device associated with the organization, a request for the new credit data. The node may obtain the storage identifier for the new credit data from the smart contract. The node may obtain the new credit data by using the storage identifier to search the distributed data sources. The node may provide the new credit data to the particular device. The node may perform actions to obtain additional new credit data from the distributed data sources or provide the additional new credit data to the distributed data sources.

Abstract: Systems as described herein may include authorizing the sharing of data and sharing data between a variety of systems. A request to share data may be provided to a first system. The system may create sharing session data using smart contracts executed by a distributed network system. Sharing session data may be stored using a smart contract. A second system may obtain the sharing session data and verify the sharing session based on the execution of the smart contract. On verification of the sharing session, a variety of data may be shared between the systems identified in the sharing session data. The sharing session data may be established between two systems and/or a number of systems. Smart contracts may provide a variety of functions for authorizing the sharing of data between systems. Additionally, encrypted data may be stored and/or obtained using a smart contract.

Abstract: Systems as described herein may include authorizing the sharing of data and sharing data between a variety of systems. A request to share data may be provided to a first system. The system may create sharing session data on a distributed ledger accessible by a number of systems. Sharing session data may be stored using a transaction stored on a distributed ledger. A second system may obtain the sharing session account and verify the sharing session. On verification of the sharing session, a variety of data may be shared between the systems identified in the sharing session data. The sharing session data may be established between two or more systems. The distributed ledger may be maintained by the systems themselves and/or a distributed network system. In a variety of embodiments, encrypted data may be stored and/or obtained using the distributed ledger.

Abstract: Various embodiments are directed to techniques for defining and optimizing the boundaries of geospatial areas predictive of various outcomes. A geographic area of interest is defined, and a model trained to predict the variable of interest within the geographic area of interest is trained using training data selected for the geographic area. The model is scored for each cell in a meshed grid defined over the geographic area of interest and, thereafter, a contour-finding algorithm is applied to the grid to define the optimized geographic area.