Abstract: A set of operational risk rating algorithms is received. A set of operational risk rating weight factors is applied to the set of operational risk rating algorithms. An initial operational risk rating score of an entity associated with the selected domain category is generated based on the set of operational risk rating algorithms applied with the set of operational risk rating weight factors. A baseline operational risk rating score is retrieved based on the selected domain category. A deviation value is determined based on comparing the retrieved baseline operational risk rating score and the initial operational risk rating score.

Abstract: A method, in a data processing system comprising a processor and a memory, for assigning data to coded entities, the method comprising receiving one or more identifier codes from a client device, wherein the one or more identifier codes correspond to branch facilities, identifying data features associated with the one or more identifier codes by accessing a registry, retrieving risk data for the data features, determining a risk rating for each of the one or more identifier codes based on the risk data for the data features, and generating risk rating data based on the determined risk rating for each of the one or more identifier codes.

Abstract: A method for rendering a crime risk overlay on a software map, the method comprising receiving crime data including a plurality of data elements. Weights are assigned to the plurality of data elements of the crime data based on a correlation to a first crime type. A crime risk rating is determined for the first crime type based on the weighted elements. A crime risk forecast is generated based at least on the crime risk rating and the crime data, the generated crime risk forecast being for a target geographic location, a period of time, and the first crime type. The method further comprises generating a graphical overlay on a map generated by a geographic information system, the graphical overlay visually indicating the target geographic location of the generated crime risk forecast on the map and the generated crime risk rating for the first crime type.

Abstract: A method comprising receiving public information source data, converting the data to a plurality of risk factors by machine parsing data features, wherein the plurality of risk factors include risk indicators that contain scores and weights, building a risk scoring model including calculations. Scoring and weight data is received to configure the risk scoring model. The method further comprises determining a new rating configuration of the risk scoring model based on the scoring and weight data, identifying an impact on the plurality of risk factors based on the new rating configuration, the impact including a change in the scores and weights of the risk indicators that exceeds one or more risk factor thresholds, and generating an alert indicator that includes the one or more risk factor thresholds that have been exceeded.

Abstract: A default risk rating for a geographic region is determined based on a first set of data records extracted from a first set of data sources. Unstructured text description of operational activities associated with the geographic region is received. A second set of data records from a second set of data sources is selected, in which the second set of data sources originate from the geographic region. A subset of the second set of data records is determined based on the unstructured text description of operational activities. The default risk rating is adjusted based on the subset of the second set of data records.

Abstract: A method, in a data processing system comprising a processor and a memory, for processing natural language feeds to software, the method comprising receiving, by the data processing system, a natural language feed including data that is representative of parameters and quantitative data from government or regulatory body data sources, parsing, by the data processing system, the natural language feed with natural language processing for features that correspond to a production system, converting, by the data processing system, the parsed natural language feed into software functions, and calibrating, by the data processing system, the production system by updating a data library using the software functions and integrating information from the data library with the production system.

Abstract: A protocol user interface (UI) element on a graphical user interface is generated, in which the protocol UI element includes an input field and is assigned with a weight value. The weight value is determined at least based upon data entered into the input field of the protocol UI element. The weight value assigned to the protocol UI element is adjusted based on historical event data associated with a protocol corresponding to the protocol UI element. Data entered into the input field of the protocol UI element is parsed. A risk assessment score based on the adjusted weight value and parsed data is dynamically calculated, in which the risk assessment score indicates a likelihood that an adverse event will occur as a result of violating the protocol.