Thomson Reuters Enterprise Centre Gmbh

Refinitiv US Organization LLC

Johannes Schleith

Hella-Franziska Hoffmann

David Oliver

David Von Rickenbach

Daniella Tsar

Dorcas Mbwiti

Guillaume Mosching

Sanzio Monti

Kim Hau

Amit Shavit

Omar Bari

David Reed

Brian Romer

Isaac Kriegman

Song Qiaoqi

Yue Feng

Revogatus Apolinary Tesha

Brian Kent Cechmanek

Juan Dario Lara Moreno

Nick Jarema

Shiqi Li

Amir Hajian Forushani

Embodiments of the present disclosure support systems and methods providing functionality for performing anomaly detection and remediation. In an aspect, a dataset comprising transaction data stored in a plurality of data sources is obtained. One or more clustering algorithms are executed against the dataset to identify a plurality of clusters. At least one cluster of the plurality of clusters corresponds to a portion of the dataset comprising an anomaly. A classifier may be used to determine a cause of the anomaly and one or more actions to eliminate the anomaly may be initiated. Data derived from the dataset subsequent to the one or more actions may be output. For example, the output may be a report generated based on the dataset.

Systems and methods for detection and correction of anomalies priority

Methods and systems for providing computer-assisted guided review of unstructured data to generate a structured data output based on customizable template rules. In embodiments, an unstructured file is received, and a predefined template is selected. The predefined template includes a plurality of fields, each field corresponding to a field of the structured report. The predefined template also defines extraction rules for each field of the predefined template, and the extraction rules define parameters for identifying unstructured data relevant to the associated field. The extraction rules are applied to the unstructured file to identify data relevant to the field associated with the corresponding extraction rule, and the data identified as relevant is confirmed. Confirming the relevant data includes determining to refine the relevant data based on a condition, and modifying the extraction rule associated with the field to refine the relevant data.

Systems and method for generating a structured report from unstructured data

The disclosed methods and systems provide computer-assisted guided review of unstructured data to generate a structured data output based on customizable template rules. In embodiments, an unstructured file is received, and a predefined template is selected. The predefined template includes a plurality of fields, each field corresponding to a field of the structured report. The predefined template also defines extraction rules for each field of the predefined template, and the extraction rules define parameters for identifying unstructured data relevant to the associated field. The extraction rules are applied to the unstructured file to identify data relevant to the field associated with the corresponding extraction rule, and the data identified as relevant is confirmed. Confirming the relevant data includes determining to refine the relevant data based on a condition, and modifying the extraction rule associated with the field to refine the relevant data.

The present disclosure relates to systems and methods for enhanced mapping of transaction data to a target document, and for classifying line items of the mapped transaction data, using classification algorithms. Embodiments provide a system including a column mapping module to receive a target scheme specifying a target structure for the target document, receive transaction data having a source structure, and map at least one source column to at least one target column of the target columns based on application of classification algorithms to features identified from the source transaction data. The system also includes a row classification module to classify the rows of the mapped transaction data into classification categories. The system also includes a validation handler to receive validation input from a user, validating the column mapping and/or the row classification. The validating including accepting the recommendation or rejecting the recommendation and selecting a correct choice.

Systems and methods for enhanced mapping and classification of data

The present disclosure relates to methods and systems for ingesting content from data feeds and to generate an enhanced output of relevant content for presentation to a user to facilitate analysis of the relevant content. Content is received from data feeds, and filtered to identify relevant content with respect to a particular context. The relevant content is then processed, e.g., using natural language processes, to extract entities involved, and to also identify particular activities detailed in the relevant content. Activity-mining is applied to the identified relevant data to classify and assigned activity tags to the extracted entity. Based on the extracted and identified information, an enhanced output is generated for presentation to facilitate research operations. The enhanced output may include overlaid graphical annotations, indicators, and graphical controls over the relevant articles to provide a means for updating a database based on the relevant content.

System and methods for generating an enhanced output of relevant content to facilitate content analysis

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London, United Kingdom

Johannes Schleith