Abstract: An example operation may include one or more of identifying a workflow specification including a number of entities and state elements, selecting two or more entities designated as a subset of entities among the entities, where the subset of entities share one or more of the state elements of the workflow specification among the state elements, and creating a first smart contract identifying the one or more state elements.

Abstract: A blockchain configuration may be used to store smart contracts. One example method of operation may include one or more of identifying a metric configuration associated with a smart contract stored in a blockchain, logging an event which is part of the metric configuration, determining whether the event supports requirements of the smart contract, determining whether a smart contract policy in the smart contract matches a system policy, and updating the smart contract on the blockchain when the requirements of the smart contract are supported by the event and the smart contract policy matches the system policy.

Abstract: An example operation may include one or more of identifying a workflow specification including a number of entities and state elements, selecting two or more entities designated as a subset of entities among the entities, where the subset of entities share one or more of the state elements of the workflow specification among the state elements, and creating a first smart contract identifying the one or more state elements.

Abstract: Techniques, systems, and articles of manufacture for collaborative analytics with edge devices. A method includes identifying multiple items of data pertaining to a user from one or more user actions implemented across multiple user devices, identifying one or more rules associated with one or more user preferences, exchanging the multiple items of data across the multiple user devices, and applying the one or more rules to the multiple items of data across the multiple user devices to generate an output via at least one of the multiple user devices.

Abstract: A blockchain configuration may be used to store smart contracts. One example method of operation may include one or more of identifying a metric configuration associated with a smart contract stored in a blockchain, logging an event which is part of the metric configuration, determining whether the event supports requirements of the smart contract, determining whether a smart contract policy in the smart contract matches a system policy, and updating the smart contract on the blockchain when the requirements of the smart contract are supported by the event and the smart contract policy matches the system policy.

Abstract: Provided are techniques for providing personalized recommendations. One or more transactions are received from one or more customer interaction channels. The received one or more transactions are stored in an incremental data store. One or more predictive rules are generated based on the received one or more transactions and based on one or more transactions previously stored in the incremental data store. In real-time, one or more personalized recommendations specific to a user and to the received one or more transactions are generated using the one or more generated predictive rules.

Abstract: Methods, systems, and articles of manufacture for financial transaction analytics are provided herein. A method includes generating a profile of a customer, the customer profile comprising multiple commercial-related parameters pertaining to the customer based at least on past transaction information of the customer; determining the identity of a merchant from a message pertaining to a transaction of the customer; determining location information of the customer from the message pertaining to the customer transaction; and generating at least one commercial offer personalized for the customer based on (i) the customer profile, (ii) the merchant identified from the message, and (iii) the customer location determined from the message.

Abstract: Techniques, systems, and articles of manufacture for collaborative analytics with edge devices. A method includes identifying multiple items of data pertaining to a user from one or more user actions implemented across multiple user devices, identifying one or more rules associated with one or more user preferences, exchanging the multiple items of data across the multiple user devices, and applying the one or more rules to the multiple items of data across the multiple user devices to generate an output via at least one of the multiple user devices.

Abstract: A method, system and software for using customer community analytics for: (i) discovering relationships (for example, financial transactional relationships) between customers and other entities (such as companies); (ii) representing the customers, other entities and their relationships in the form of a machine readable graph; and (iii) using the graph to perform graph-based functions. These functions may include: up-selling, cross-selling, making personalized recommendations, discovering a financial products supply chain and/or facilitating social interactions between and among customers and other entities.

Abstract: A method, system and software for using customer community analytics for: (i) discovering relationships (for example, financial transactional relationships) between customers and other entities (such as companies); (ii) representing the customers, other entities and their relationships in the form of a machine readable graph; and (iii) using the graph to perform graph-based functions. These functions may include: up-selling, cross-selling, making personalized recommendations, discovering a financial products supply chain and/or facilitating social interactions between and among customers and other entities.

Abstract: Provided are techniques for providing personalized recommendations. One or more transactions are received from one or more customer interaction channels. The received one or more transactions are stored in an incremental data store. One or more predictive rules are generated based on the received one or more transactions and based on one or more transactions previously stored in the incremental data store. In real-time, one or more personalized recommendations specific to a user and to the received one or more transactions are generated using the one or more generated predictive rules.

Abstract: A method, an apparatus and an article of manufacture for detecting a fraudulent mobile money transaction. The method includes determining a reputation score for at least one customer based on historical data, determining multiple patterns related to fraudulent mobile money transaction activities by using the reputation score and the historical data, automatically detecting at least one of the patterns in mobile money transactions incoming from at least one other customer, and classifying the at least one detected pattern as a fraudulent mobile money transaction or a genuine mobile money transaction.

Abstract: An embodiment of the invention provides a method and system for analyzing a plurality of reports. More specifically, a change detection module predicts results of future reports based on past reports and identifies a first report that deviates from its predicted results. A dependency analysis module connected to the change detection module at least one report sharing a dependency with the first report by performing a dependency analysis and/or a usage analysis. The dependency analysis labels the first report and at least one second report as sharing a dependency if the second report deviates from its predicted results. The usage analysis labeling the first report and at least one report analyzed by an analyst as sharing a dependency if the report analyzed by the analyst is analyzed in response to the identification of the first report.

Abstract: An embodiment of the invention provides a method and system for analyzing a plurality of reports. More specifically, a change detection module predicts results of future reports based on past reports and identifies a first report that deviates from its predicted results. A dependency analysis module connected to the change detection module at least one report sharing a dependency with the first report by performing a dependency analysis and/or a usage analysis. The dependency analysis labels the first report and at least one second report as sharing a dependency if the second report deviates from its predicted results. The usage analysis labeling the first report and at least one report analyzed by an analyst as sharing a dependency if the report analyzed by the analyst is analyzed in response to the identification of the first report.

Abstract: A method and a system for dynamically specifying exceptions and exception handlers for an application written in any computer language, which has exception handling facility, are described. The present decouples the exception handling logic from the application and thereby can be used to alter the behavior of the program/module/application as well as to define new types of exceptions at runtime. The decoupling of the exception handler logic eliminates the language dependence of the application and further helps in customizing the behavior of the application when they are being deployed in new environments. Moreover dynamic exception handling is not operating system dependent.

Abstract: A system and method for managing data resources includes a policy definition layer, a policy deployment layer and a policy execution layer. The policy definition layer provides for creating, editing and visualizing policies through a user interface using intuitive simple language constructs. The policy deployment layer converts the created policies into a relational format that can be directly stored in a policy database. The policy execution layer executes the deployed policies on occurrence of an event. A resource abstraction and notification layer provides the interfacing of the resources with the policy definition layer, policy deployment layer and the policy execution layer.

Abstract: The current invention provides a system for managing resources like application modules, databases, information repositories etc., monitoring transactions and transmitting data among systems. The system comprises a policy definition layer, a policy deployment layer and a policy execution layer. The policy definition layer provides for creating, editing and visualizing policies through a user interface using intuitive simple language constructs. The policy deployment layer converts the created policies into a relational format that can be directly stored in a policy database. The policy execution layer executes the deployed policies on occurrence of an event. A resource abstraction and notification layer provides the interfacing of the resources with the policy definition layer, policy deployment layer and the policy execution layer.

Abstract: The invention describes a methodology and a system for dynamically specifying exceptions and exception handlers for an application written in any computer language, which has exception handling facility. The methodology described in this invention decouples the exception handling logic from the application thereby can be used to alter the behavior of the program/module/application as well as to define new types of exceptions at runtime. The decoupling of the exception handler logic eliminates the language dependence of the application and further helps in customizing the behavior of the application when they are being deployed in new environments. Moreover dynamic exception handling is not operating system dependent.