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

IPC: G06Q40/03 ,  G06F9/54 ,  G06F40/20 ,  G06N20/00

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.

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

IPC: G06Q40/02 ,  G06F40/20 ,  G06N20/00

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.

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

IPC: G06Q40/02 ,  G06F40/20 ,  G06N20/00

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.

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

IPC: G06Q40/00 ,  G06Q40/03 ,  G06F9/54 ,  G06F40/20 ,  G06N20/00

CPC classification number: G06Q40/03 ,  G06F9/547 ,  G06F40/20 ,  G06N20/00

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.

公开(公告)号：US11978065B2

公开(公告)日：2024-05-07

申请号：US18194682

申请日：2023-04-03

Applicant: Fidelity Information Services, LLC

Inventor： Jeffrey Paul Kubler ,  Shane Saunders ,  Joseph Fredric Sutthoff ,  Joe Leung ,  Lauren Morrison ,  Matthew Petersen ,  David Raynault ,  Philip J. Ponsaran

IPC: G06Q30/018 ,  G06F9/54 ,  G06F16/23 ,  G06N5/04 ,  G06N20/00 ,  G06Q20/20 ,  G06Q40/02 ,  H04L67/306 ,  H04L67/50 ,  G06F3/0483

CPC classification number: G06Q30/0185 ,  G06F9/547 ,  G06F16/2365 ,  G06F16/2379 ,  G06N5/04 ,  G06N20/00 ,  G06Q20/201 ,  G06Q20/209 ,  G06Q40/02 ,  H04L67/306 ,  H04L67/535 ,  G06F3/0483

Abstract: Disclosed herein are systems and methods for managing transaction disputes. Techniques include accessing a transaction pattern model associated with a user account; detecting deviation of a transaction from the transaction pattern model; transmitting, based on the detected deviation, transaction data to a user device; receiving an input at the user device in response to the transmitted data; generating, based on the received input, dispute data; storing the dispute data; monitoring data extracts associated with the user account; detecting, based on the monitoring, a data extract indicating a posted state of a transaction associated with the dispute data; and updating the dispute data based on the data extract indicating the posted state.

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

IPC: G06Q40/00 ,  G06Q40/03 ,  G06F9/54 ,  G06F40/20 ,  G06N20/00

CPC classification number: G06Q40/03 ,  G06F9/547 ,  G06F40/20 ,  G06N20/00

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.

公开(公告)号：US20240265404A1

公开(公告)日：2024-08-08

申请号：US18625304

申请日：2024-04-03

Applicant: Fidelity Information Services, LLC

Inventor： Jeffrey Paul Kubler ,  Shane Saunders ,  Joseph Fredric Sutthoff ,  Joe Leung ,  Lauren Morrison ,  Matthew Petersen ,  David Raynault ,  Philip J. Ponsaran

IPC: G06Q30/018 ,  G06F3/0483 ,  G06F9/54 ,  G06F16/23 ,  G06N5/04 ,  G06N20/00 ,  G06Q20/20 ,  G06Q40/02 ,  H04L67/306 ,  H04L67/50

CPC classification number: G06Q30/0185 ,  G06F9/547 ,  G06F16/2365 ,  G06F16/2379 ,  G06N5/04 ,  G06N20/00 ,  G06Q20/201 ,  G06Q20/209 ,  G06Q40/02 ,  H04L67/306 ,  H04L67/535 ,  G06F3/0483

Abstract: Disclosed herein are systems and methods for managing transaction disputes. Techniques include accessing a transaction pattern model associated with a user account; detecting deviation of a transaction from the transaction pattern model; transmitting, based on the detected deviation, transaction data to a user device; receiving an input at the user device in response to the transmitted data; generating, based on the received input, dispute data; storing the dispute data; monitoring data extracts associated with the user account; detecting, based on the monitoring, a data extract indicating a posted state of a transaction associated with the dispute data; and updating the dispute data based on the data extract indicating the posted state.

公开(公告)号：US20230289824A1

公开(公告)日：2023-09-14

申请号：US18194682

申请日：2023-04-03

Applicant: Fidelity Information Services, LLC

Inventor： Jeffrey Paul Kubler ,  Shane Saunders ,  Joseph Fredric Sutthoff ,  Joe Leung ,  Lauren Morrison ,  Matthew Petersen ,  David Raynault ,  Philip J. Ponsaran

IPC: G06Q30/018 ,  G06Q40/02 ,  G06Q20/20 ,  G06F16/23 ,  G06F9/54 ,  G06N5/04 ,  G06N20/00 ,  H04L67/306 ,  H04L67/50

CPC classification number: G06Q30/0185 ,  G06Q40/02 ,  G06Q20/209 ,  G06Q20/201 ,  G06F16/2379 ,  G06F9/547 ,  G06N5/04 ,  G06N20/00 ,  H04L67/306 ,  G06F16/2365 ,  H04L67/535 ,  G06F3/0483

Abstract: Disclosed herein are systems and methods for managing transaction disputes. Techniques include accessing a transaction pattern model associated with a user account; detecting deviation of a transaction from the transaction pattern model; transmitting, based on the detected deviation, transaction data to a user device; receiving an input at the user device in response to the transmitted data; generating, based on the received input, dispute data; storing the dispute data; monitoring data extracts associated with the user account; detecting, based on the monitoring, a data extract indicating a posted state of a transaction associated with the dispute data; and updating the dispute data based on the data extract indicating the posted state.

公开(公告)号：US11651378B2

公开(公告)日：2023-05-16

申请号：US16904878

申请日：2020-06-18

Applicant: Fidelity Information Services, LLC

Inventor： Jeffrey Paul Kubler ,  Shane Saunders ,  Joseph Fredric Sutthoff ,  Joe Leung ,  Lauren Morrison ,  Matthew Petersen ,  David Raynault ,  Philip J. Ponsaran

IPC: G06Q30/018 ,  G06Q40/02 ,  G06Q20/20 ,  G06F16/23 ,  G06F9/54 ,  G06N5/04 ,  G06N20/00 ,  H04L67/306 ,  H04L67/50 ,  G06F3/0483

CPC classification number: G06Q30/0185 ,  G06F9/547 ,  G06F16/2365 ,  G06F16/2379 ,  G06N5/04 ,  G06N20/00 ,  G06Q20/201 ,  G06Q20/209 ,  G06Q40/02 ,  H04L67/306 ,  H04L67/535 ,  G06F3/0483

Abstract: Disclosed herein are systems and methods for managing transaction disputes. Techniques include accessing a transaction pattern model associated with a user account; detecting deviation of a transaction from the transaction pattern model; transmitting, based on the detected deviation, transaction data to a user device; receiving an input at the user device in response to the transmitted data; generating, based on the received input, dispute data; storing the dispute data; monitoring data extracts associated with the user account; detecting, based on the monitoring, a data extract indicating a posted state of a transaction associated with the dispute data; and updating the dispute data based on the data extract indicating the posted state.

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

IPC: G06Q40/02 ,  G06F40/20 ,  G06N20/00 ,  G06F9/54

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.