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1.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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2.发明公开公开(公告)号：US20230206058A1
公开(公告)日：2023-06-29
申请号：US17564373
申请日：2021-12-29
Applicant: Fidelity Information Services, LLC
Inventor： Benjamin Wellmann , Gary Duma
IPC: G06N3/08
CPC classification number: G06N3/08
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing sequences of multi-modal entity data using convolutional neural networks. One of the methods includes receiving an input sequence of multi-modal feature vectors characterizing an entity over a time window, wherein each multi-modal feature vector in the input sequence corresponds to a different time interval during the time window; processing the input sequence of multi-modal feature vectors using a convolutional neural network to generate a latent sequence that comprises a plurality of latent feature vectors; processing the latent sequence of latent feature vectors using an aggregation neural network to generate an aggregated feature vector; and processing the aggregated feature vector using an output neural network to generate a prediction that characterizes the entity after the time window.
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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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4.发明公开公开(公告)号：US20230281541A1
公开(公告)日：2023-09-07
申请号：US18178132
申请日：2023-03-03
Applicant: Fidelity Information Services, LLC
Inventor： Benjamin Wellmann , Harry M. Stahl , David Berglund , Gary Michael Duma , John J. Cicinelli , Ravi Dangeti
IPC: G06Q10/0635 , G06Q40/12
CPC classification number: G06Q10/0635 , G06Q40/12
Abstract: Systems, devices, methods, and computer readable media for providing regulatory insight analysis are disclosed. In one implementation, the disclosed system may receive input data from a plurality of sources. Consistent with disclosed embodiments, the system may normalize the received input data. Further, the system may analyze the normalized input data, the analyzing comprising using logic for generating an output based on a first input including the normalized input data, a second input including calculation attributes, and a third input including one or more rules. The system may further be configured to store the output, continuously monitor the output as the output is stored, and generate one or more reports based on the stored output. Further, the system may receive, from a user and via a user interface, additional input data, a request to view the one or more generated reports, or a request for an additional output.
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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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Patent Agency Ranking
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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7.发明公开公开(公告)号：US20240221009A1
公开(公告)日：2024-07-04
申请号：US18148713
申请日：2022-12-30
Applicant: Fidelity Information Services, LLC
Inventor： Benjamin Wellmann , Luke Jurat , Edward S. Barker , Stephane Wyper
IPC: G06Q30/0202
CPC classification number: G06Q30/0202
Abstract: Systems and methods for developing temporal behavioral profiles are disclosed. A system for developing temporal behavioral profiles may include a memory storing instructions and at least one processor configured to execute instructions to perform operations including: receiving first transaction information; determining that the first transaction information shares a threshold amount of attributes with second transaction information; based on the determination that the first transaction information shares a threshold amount of attributes with the second transaction information, determining that the first transaction information and the second transaction information are associated with a single purchaser; constructing a temporal behavioral profile for the single purchaser based on the first transaction information and the second transaction information, the temporal behavioral profile being configured for predicting at least one future transaction associated with the single purchaser; and predicting the at least one future transaction associated with the single purchaser using the temporal behavioral profile.
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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：US20220215323A1
公开(公告)日：2022-07-07
申请号：US17140812
申请日：2021-01-04
Applicant: FIDELITY INFORMATION SERVICES, LLC
Inventor： Joel E. Dake , Damyn L. Gessler , Shane M. Patzlsberger , Shane W. Strunk , Benjamin Wellmann
Abstract: Some aspects of the present disclosure are directed to computer-implemented systems and methods for efficient ticket resolution. The methods may include: receiving a request to resolve an issue; analyzing, via natural language processing, the language in the request to determine the issue to be resolved; determining whether the issue meets a condition for automated resolution; if the condition is met: extracting, via an application programming interface and from the at least one user device, information needed to resolve the issue; and resolving the issue using the extracted information; and if the condition is not met: generating a ticket; assigning a work group to the ticket; determining whether a job aid associated with the issue exists; and forwarding at least one of: the job aid; received communications from the work group; and an estimated amount of time to resolution.
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10.发明公开公开(公告)号：US20240220822A1
公开(公告)日：2024-07-04
申请号：US18148058
申请日：2022-12-29
Applicant: Fidelity Information Services, LLC
Inventor： Benjamin Wellmann , Luke Jurat , Edward S. Barker , Stephane Wyper
Abstract: Systems and methods for predicting item group composition are disclosed. A system for predicting item group composition may include a memory storing instructions and at least one processor configured to execute instructions to perform operations including: receiving entity identification information and a timestamp associated with a transaction without receiving information distinguishing items associated with the transaction; determining, based on the entity identification information, a localized machine learning model trained to predict categories of items based on transaction information applying to all of the items associated with the transaction; and applying the localized machine learning model to a model input to generate predicted categories of items associated with the transaction, the model input including the received entity identification information and a timestamp but not including information distinguishing items associated with the transaction.
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