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公开(公告)号：US11645712B2
公开(公告)日：2023-05-09
申请号：US17233251
申请日：2021-04-16
Applicant: FIDELITY INFORMATION SERVICES, LLC
Inventor： Benjamin Wellmann , Zachary Jasinski , Matthew Petersen , Eric Bond , Daniel Wakeman , David Berglund
Abstract: Systems and methods for entity risk management are disclosed. A system for entity risk management may include a memory storing instructions and at least one processor configured to execute instructions to perform operations including: establishing a connection between the system and a data source, the data source being remote from the system and associated with a first entity; receiving first document data from the data source; normalizing the first document data; classifying the normalized document data; extracting model input data from the classified document data; applying a machine learning model trained to predict risk levels using second document data to the extracted model input data to predict a risk level associated with the first entity; generating analysis data based on the predicted risk level; and based on the analysis data, transmitting an alert to a management device communicably connected to the system.
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Patent Agency Ranking
2.发明申请公开(公告)号：US20220335518A1
公开(公告)日：2022-10-20
申请号：US17233340
申请日：2021-04-16
Applicant: FIDELITY INFORMATION SERVICES, LLC
Inventor： Benjamin Wellmann , Zachary Jasinski , Matthew Petersen , Eric Bond , Daniel Wakeman , David Berglund
Abstract: Systems and methods for providing selective access to model output data are disclosed. A system for providing selective access to model output data may include a memory storing instructions and at least one processor configured to execute instructions to perform operations including: receiving, through an application programming interface (API) and from a requestor device, an API request for data, the API request identifying a requestor entity associated with the requestor device; determining a data type based on the API request; determining an authorization level of the requestor; accessing first model output data corresponding to the data type and the authorization level, the first model output data having been generated by a machine learning model trained to predict a risk level based on document data; and transmitting the first model output data to the requestor device.
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公开(公告)号：US20220335517A1
公开(公告)日：2022-10-20
申请号：US17233300
申请日：2021-04-16
Applicant: FIDELITY INFORMATION SERVICES, LLC
Inventor： Benjamin Wellmann , Zachary Jasinski , Matthew Petersen , Eric Bond , Daniel Wakeman , David Berglund
Abstract: Systems and methods for activity risk management are disclosed. A system for activity risk management may include a memory storing instructions and at least one processor configured to execute instructions to perform operations including: accessing document data associated with at least one of a transaction or an individual; normalizing the document data; classifying the normalized document data; extracting model input data from the classified document data; applying a machine learning model to the extracted model input data to score the document data, the machine learning model having been trained to generate a favorability output indicating a favorability of the transaction or individual; and generating analysis data based on the scored document data.
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公开(公告)号：US20220335516A1
公开(公告)日：2022-10-20
申请号：US17233251
申请日：2021-04-16
Applicant: FIDELITY INFORMATION SERVICES, LLC
Inventor： Benjamin Wellmann , Zachary Jasinski , Matthew Petersen , Eric Bond , Daniel Wakeman , David Berglund
Abstract: Systems and methods for entity risk management are disclosed. A system for entity risk management may include a memory storing instructions and at least one processor configured to execute instructions to perform operations including: establishing a connection between the system and a data source, the data source being remote from the system and associated with a first entity; receiving first document data from the data source; normalizing the first document data; classifying the normalized document data; extracting model input data from the classified document data; applying a machine learning model trained to predict risk levels using second document data to the extracted model input data to predict a risk level associated with the first entity; generating analysis data based on the predicted risk level; and based on the analysis data, transmitting an alert to a management device communicably connected to the system.
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公开(公告)号：US20230135192A1
公开(公告)日：2023-05-04
申请号：US18147868
申请日：2022-12-29
Applicant: FIDELITY INFORMATION SERVICES, LLC
Inventor： Benjamin Wellmann , Zachary Jasinski , Matthew Petersen , Eric Bond , Daniel Wakeman , David Berglund
Abstract: Systems and methods for activity risk management are disclosed. A system for activity risk management may include a memory storing instructions and at least one processor configured to execute instructions to perform operations including: classifying document data by identifying at least one marker in the document data, the at least one marker being associated with a document type; selecting an extraction model based on the document type; extracting model input data from the classified document data using the extraction model; applying a machine learning model to the extracted model input data to score the document data, the machine learning model having been trained with document data of a same document type as the document type associated with the at least one marker; and generating, based on the applying, a favorability output based on an amount of risk associated with the document data.
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公开(公告)号：US11842391B2
公开(公告)日：2023-12-12
申请号：US17233300
申请日：2021-04-16
Applicant: FIDELITY INFORMATION SERVICES, LLC
Inventor： Benjamin Wellmann , Zachary Jasinski , Matthew Petersen , Eric Bond , Daniel Wakeman , David Berglund
Abstract: Systems and methods for activity risk management are disclosed. A system for activity risk management may include a memory storing instructions and at least one processor configured to execute instructions to perform operations including: accessing document data associated with at least one of a transaction or an individual; normalizing the document data; classifying the normalized document data; extracting model input data from the classified document data; applying a machine learning model to the extracted model input data to score the document data, the machine learning model having been trained to generate a favorability output indicating a favorability of the transaction or individual; and generating analysis data based on the scored document data.
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