Abstract: A method, computer program, and computer system are provided for determining similar nodes in a federated learning environment. Data corresponding to a dataset associated with a node in the federated learning environment is retrieved by the node. A frequency distribution associated with the dataset is calculated and transmitted to an aggregator. One or more frequency distributions associated with one or more other nodes in the federated learning environment are received from the aggregator. Based on the received frequency distributions associated with the one or more other nodes, a similarity between the node and a subset of the one or more other nodes is identified.

Abstract: An AI-based rule generation system generates an ontology from user-provided information and further enables generating rules that govern processes via drag-and-drop operations by automatically generating code in the backend. The rule generation system after generating the ontology, provides access to the entities of the ontology via a drag-and-drop GUI which also includes operators required to generate the rules. The user can drag-and-drop the entity elements and the operator elements as needed onto a whitespace in addition to providing the requisite values in order to generate a rule flow. The rule flow is validated and published to an execution server for use by downstream processes. The rule generation system further includes custom functions in addition to enabling distributed knowledge base processes for generating the rules.

Abstract: A system for providing automated and domain specific contextual processing for context based verification may classify a plurality of extracted parameters from a set of digitized training document to assign a document similarity score with respect to a set of reference documents. The system may automatically detect a domain for the set of digitized training documents based on the document similarity score. The system may load a domain based neural model for the detected domain to generate a plurality of pre-defined contextual parameters. The system may receive a set of input documents and perform a contextual processing of the received set of documents based on the pre-defined contextual parameters to obtain an output in form of a plurality of filtered snippets, each bearing a corresponding rank. The context based verification may be performed based on the plurality of filtered snippets and the corresponding rank.

Abstract: An Artificial Intelligence (AI)-based automated process is monitored via a process monitoring system that identifies components used in the execution of the sub-processes of the automated process. Various metrics are selected for collection prior to or during the execution of the AI-based automated process. The values of the metrics are collected as step outputs corresponding to the sub-processes. A final output generated upon the execution of the automated process is also collected. The step outputs can be used to determine the reason why the automated process produced a certain final output.

Abstract: A message analysis system controls the transmission of outgoing messages based on the contents of the outgoing messages so that the proliferation of restricted data or confidential content can be monitored or prevented. An outgoing message is detected via various user actions executed during the creation of the outgoing message. The contents of the outgoing message are extracted to determine if the outgoing message includes restricted data or content based on a comparison of the message contents with contents of an index which includes prior tagged messages and their message counts. If the contents of the outgoing message match a prior received message, an associated message count is updated and the outgoing message is transmitted or restricted based on the comparison of the updated message count with a corresponding count threshold.

Abstract: An Artificial Intelligence (AI) based data transformation system receives a process document and automatically generates processor-executable code which enables automatic execution of a process as detailed within the process document. Various structural elements of the process documents are identified and the data from the document is clustered based on common parameters which can include the structural elements or textual data from the process document. The contextual information including conditional and non-conditional statements along with the entities and entity attributes are also obtained. The domain knowledge is superimposed on the contextual information to generate flows that represent procedures which make up the process to be automated. Platform specific code for the automatic execution of the process is automatically generated from the flows.

Abstract: An Artificial Intelligence (AI) based data transformation system receives a process document and automatically generates processor-executable code which enables automatic execution of a process as detailed within the process document. Various structural elements of the process documents are identified and the data from the document is clustered based on common parameters which can include the structural elements or textual data from the process document. The contextual information including conditional and non-conditional statements along with the entities and entity attributes are also obtained. The domain knowledge is superimposed on the contextual information to generate flows that represent procedures which make up the process to be automated. Platform specific code for the automatic execution of the process is automatically generated from the flows.

Abstract: An Artificial Intelligence (AI)-based automated process is monitored via a process monitoring system that identifies components used in the execution of the sub-processes of the automated process. Various metrics are selected for collection prior to or during the execution of the AI-based automated process. The values of the metrics are collected as step outputs corresponding to the sub-processes. A final output generated upon the execution of the automated process is also collected. The step outputs can be used to determine the reason why the automated process produced a certain final output.

Abstract: An AI-based rule generation system generates an ontology from user-provided information and further enables generating rules that govern processes via drag-and-drop operations by automatically generating code in the backend. The rule generation system after generating the ontology, provides access to the entities of the ontology via a drag-and-drop GUI which also includes operators required to generate the rules. The user can drag-and-drop the entity elements and the operator elements as needed onto a whitespace in addition to providing the requisite values in order to generate a rule flow. The rule flow is validated and published to an execution server for use by downstream processes. The rule generation system further includes custom functions in addition to enabling distributed knowledge base processes for generating the rules.

Abstract: A claims preprocessor processes claim data to identify claims that are to be adjudicated. Each claim includes at least one claim exception. The claims preprocessor further prioritizes the claim exception of each identified claim based on the claim data. A robotic process automator then orchestrates adjudication of the identified claims based on claim data. Further, a rules engine adjudicates the identified claims based on pre-defined rules. Subsequently, a fall out handler determines if any of the identified claims are incorrectly adjudicated and identify an issue associated with incorrect claims adjudication on determining that any of the identified claims are incorrectly adjudicated. A self learner then provides feedback to rules engine based on a decision tree and information received from fall out handler, the feedback being usable to resolve the issue. The information received from fall out handler is indicative of issue associated with incorrect claims adjudication.

Abstract: A description of a resource associated with a service of an entity can be captured. The service can be associated with one or more resources, a constraint, and a demand. The resource can be associated with one or more characteristics including a utility, a limited availability, and a consumption rate. The entity can be an organization or a system. An initial allocation problem associated with the resource can be formulated as a two phase problem. The first phase can be an optimization problem and the second phase can be a restricted allocation problem. The initial allocation problem can be associated with reconfiguring a previously established allocation of a baseline scenario. The optimization problem can be solved optimally or approximately to establish a favorable allocation. The favorable allocation can minimizes the reconfiguration cost of the reconfiguring. The baseline scenario can be a normal operation of the service.

Abstract: A data validation service includes providing a user interface to a subscriber of the service via a computer device of the subscriber, receiving, via the user interface, a data validation rule specified by the subscriber and an address of a database subject to the data validation, and generating a configuration file that includes the address of the database and an address of a location of executable code corresponding to the data validation rule. The data validation service also includes transmitting the configuration file and remote methods to the computer device over the network. The remote methods are configured to execute the data validation rule with respect to the data and compile results of the execution.

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: A decision support tool which takes a high-level view of supply and demand in the realm of labor resources and produces globally-optimal recommendations for assignment of practitioners to open seats. These assignments preferably optimize a utility function composed of different business metrics and can be customizable for different organizations and priorities.

Abstract: A description of a resource associated with a service of an entity can be captured. The service can be associated with one or more resources, a constraint, and a demand. The resource can be associated with one or more characteristics including a utility, a limited availability, and a consumption rate. The entity can be an organization or a system. An initial allocation problem associated with the resource can be formulated as a two phase problem. The first phase can be an optimization problem and the second phase can be a restricted allocation problem. The initial allocation problem can be associated with reconfiguring a previously established allocation of a baseline scenario. The optimization problem can be solved optimally or approximately to establish a favorable allocation. The favorable allocation can minimizes the reconfiguration cost of the reconfiguring. The baseline scenario can be a normal operation of the service.

Abstract: A data validation service includes providing a user interface to a subscriber of the service via a computer device of the subscriber, receiving a data validation rule specified by the subscriber and an address of a database subject to the data validation, and generating a configuration file that includes the address of the database. The service also includes transmitting the configuration file and a thin client application to the computer device over a network, the thin client application configured to read the configuration file and pull data from the database. The service further includes receiving the data from the computer device via the network, performing the data validation by executing the data validation rule with respect to the data, and compiling results of the data validation and providing the results to the computer device.

Abstract: A data validation service includes providing a user interface to a subscriber of the service via a computer device of the subscriber, receiving, via the user interface, a data validation rule specified by the subscriber and an address of a database subject to the data validation, and generating a configuration file that includes the address of the database and an address of a location of executable code corresponding to the data validation rule. The data validation service also includes transmitting the configuration file and remote methods to the computer device over the network. The remote methods are configured to execute the data validation rule with respect to the data and compile results of the execution.

Abstract: A decision support tool which takes a high-level view of supply and demand in the realm of labor resources and produces globally-optimal recommendations for assignment of practitioners to open seats. These assignments preferably optimize a utility function composed of different business metrics and can be customizable for different organizations and priorities.