Abstract: A smart data signals platform for artificial intelligence/machine learning (AI/ML)-based modeling and simulation is structured to pre-process input data by structuring previously unstructured data that relates to a structured data item and linking the structured data to the data item to generate an enriched dataset. The enriched dataset is used to generate a trigger signal by evaluating at least an aspect to the enriched dataset against at least one criterion that corresponds to a loss hypothesis. The trigger signal is used to automatically monitor subsequently received structured data, access corresponding unstructured data and generate an analysis dataset for one or more machine learning models. The one or more machine learning models generate a computer-based prediction based on the analysis dataset. The prediction can include a resource degradation indicator.

Abstract: A smart data signals platform for artificial intelligence/machine learning (AI/ML)-based modeling and simulation is structured to pre-process input data by structuring previously unstructured data that relates to a structured data item and linking the structured data to the data item to generate a validated enriched dataset. The validated enriched dataset is used to generate a trigger signal by evaluating at least an aspect to the enriched dataset against at least one criterion that corresponds to a loss hypothesis. The trigger signal is used to automatically monitor subsequently received structured data, access corresponding unstructured data and generate an analysis dataset for one or more machine learning models. The one or more machine learning models generate a computer-based prediction based on the analysis dataset. The prediction can include a resource degradation indicator.

Abstract: A smart data signals platform for artificial intelligence/machine learning (AI/ML)-based modeling and simulation is structured to pre-process input data by structuring previously unstructured data that relates to a structured data item and linking the structured data to the data item to generate a validated enriched dataset. The validated enriched dataset is used to generate a trigger signal by evaluating at least an aspect to the enriched dataset against at least one criterion that corresponds to a loss hypothesis. The trigger signal is used to automatically monitor subsequently received structured data, access corresponding unstructured data and generate an analysis dataset for one or more machine learning models. The one or more machine learning models generate a computer-based prediction based on the analysis dataset. The prediction can include a resource degradation indicator.

Abstract: A method and system for checking whether customer orders for transactions of financial instruments conform to business logic rules. Executable rule files are created and stored in a repository. New executable rule files can be created by scripting the new business logic rules in a script file which is converted into a corresponding source code file written in a computer programming language. The source code file is compiled to create an individual executable rule file. A rule selection repository contains identification of groups of selected executable rule files. The invention determines the category of the customer order and reads, from the rule selection repository, a group of executable rule files that correspond to the identified category of the customer order. The selected executable rule files are executed to check the conformance of the customer order. Execution results are stored in a status repository for subsequent retrieval and analysis.

Abstract: A method and system for checking whether customer orders for transactions of financial instruments conform to business logic rules. Executable rule files are created and stored in a repository. New executable rule files can be created by scripting the new business logic rules in a script file which is converted into a corresponding source code file written in a computer programming language. The source code file is compiled to create an individual executable rule file. A rule selection repository contains identification of groups of selected executable rule files. The invention determines the category of the customer order and reads, from the rule selection repository, a group of executable rule files that correspond to the identified category of the customer order. The selected executable rule files are executed to check the conformance of the customer order. Execution results are stored in a status repository for subsequent retrieval and analysis.

Abstract: A method and system for checking whether customer orders for transactions of financial instruments conform to business logic rules. Executable rule files are created and stored in a repository. New executable rule files can be created by scripting the new business logic rules in a script file which is converted into a corresponding source code file written in a computer programming language. The source code file is compiled to create an individual executable rule file. A rule selection repository contains identification of groups of selected executable rule files. The invention determines the category of the customer order and reads, from the rule selection repository, a group of executable rule files that correspond to the identified category of the customer order. The selected executable rule files are executed to check the conformance of the customer order. Execution results are stored in a status repository for subsequent retrieval and analysis.

Abstract: A method and system for checking whether customer orders for transactions of financial instruments conform to business logic rules. Executable rule files are created and stored in a repository. New executable rule files can be created by scripting the new business logic rules in a script file which is converted into a corresponding source code file written in a computer programming language. The source code file is compiled to create an individual executable rule file. A rule selection repository contains identification of groups of selected executable rule files. The invention determines the category of the customer order and reads, from the rule selection repository, a group of executable rule files that correspond to the identified category of the customer order. The selected executable rule files are executed to check the conformance of the customer order. Execution results are stored in a status repository for subsequent retrieval and analysis.