Abstract: A system and method to allow an authorized searcher to conduct a search of a current primary version of a document being developed in an application as well as versions of the document which were used in the development of the current primary version. In an exemplary system, instructions cause a processor to grant a search request to search, via a search index in a cloud storage, stored selected versions of the document. The stored selected versions of the document are historical versions of the current primary version of the document which has been selected from among the stored selected versions to be accessible, via the search index, to other searchers having a lower access authorization than the predetermined access authorization. The authorized searcher is provided with a capability to toggle between searching only the primary version of the document and searching the stored selected versions of the document.

Abstract: A customer risk trigger associated with a customer may be identified. A response to the customer risk trigger may be detected. First risk analysis data related to the customer risk trigger may be gathered, based on the response, from a first datastore. Second risk analysis data related to the customer risk trigger may be gathered, based on the response, from a second datastore. A customer risk profile to model risk attribute(s) of the customer may be gathered. The risk attributes may represent a risk correlation between the customer and a prohibited act. Customer risk visualization tool(s) configured to facilitate visual user interaction with the customer risk profile may be gathered. The customer risk visualization tools may be rendered in a display of the computing system. The customer risk visualization tools provide a customer-centric view of risk for various applications, including anti-money laundering applications.

Abstract: A customer risk trigger associated with a customer may be identified. A response to the customer risk trigger may be detected. First risk analysis data related to the customer risk trigger may be gathered, based on the response, from a first datastore. Second risk analysis data related to the customer risk trigger may be gathered, based on the response, from a second datastore. A customer risk profile to model risk attribute(s) of the customer may be gathered. The risk attributes may represent a risk correlation between the customer and a prohibited act. Customer risk visualization tool(s) configured to facilitate visual user interaction with the customer risk profile may be gathered. The customer risk visualization tools may be rendered in a display of the computing system. The customer risk visualization tools provide a customer-centric view of risk for various applications, including anti-money laundering applications.

Abstract: A customer risk trigger associated with a customer may be identified. A response to the customer risk trigger may be detected. First risk analysis data related to the customer risk trigger may be gathered, based on the response, from a first datastore. Second risk analysis data related to the customer risk trigger may be gathered, based on the response, from a second datastore. A customer risk profile to model risk attribute(s) of the customer may be gathered. The risk attributes may represent a risk correlation between the customer and a prohibited act. Customer risk visualization tool(s) configured to facilitate visual user interaction with the customer risk profile may be gathered. The customer risk visualization tools may be rendered in a display of the computing system. The customer risk visualization tools provide a customer-centric view of risk for various applications, including anti-money laundering applications.

Abstract: Computer-implemented systems and methods are disclosed for automatically aggregating, analyzing, and presenting probabilities associated with data items. Data items may be associated with probabilities or risks, and the data items may have various characteristics. A grouping of data items may be determined based on these characteristics, and probabilities within groups of data items may be aggregated and analyzed. Aggregated probabilities may be used to determine incremental probabilities for individual data items, to assess cumulative risk associated with a group of data items, and to analyze probabilities associated with a particular data item group. User interfaces may be generated to facilitate selection and grouping of data items, selection of risk models, and analysis of aggregate probabilities.

Abstract: Computer-implemented systems and methods are disclosed for automatically aggregating, analyzing, and presenting probabilities associated with data items. Data items may be associated with probabilities or risks, and the data items may have various characteristics. A grouping of data items may be determined based on these characteristics, and probabilities within groups of data items may be aggregated and analyzed. Aggregated probabilities may be used to determine incremental probabilities for individual data items, to assess cumulative risk associated with a group of data items, and to analyze probabilities associated with a particular data item group. User interfaces may be generated to facilitate selection and grouping of data items, selection of risk models, and analysis of aggregate probabilities.

Abstract: Computer-implemented systems and methods are disclosed for automatically aggregating, analyzing, and presenting probabilities associated with data items. Data items may be associated with probabilities or risks, and the data items may have various characteristics. A grouping of data items may be determined based on these characteristics, and probabilities within groups of data items may be aggregated and analyzed. Aggregated probabilities may be used to determine incremental probabilities for individual data items, to assess cumulative risk associated with a group of data items, and to analyze probabilities associated with a particular data item group. User interfaces may be generated to facilitate selection and grouping of data items, selection of risk models, and analysis of aggregate probabilities.

Abstract: A contextual computer workspace feature provides users with a mechanism to capture and output a state of a contextual computer workspace so that the user may continue working on a computer-based task as though no interruptions occurred. A state of a contextual computer workspace, including the window views and content thereof, is captured. Upon capturing a state of a contextual computer workspace, an indication to access the contextual computer workspace is received. The state of the contextual computer workspace is restored and the restored state is then output to communicate the contextual computer workspace to the user.