David J Berglund

US Patent:

20220335516, Oct 20, 2022

Filed:

Apr 16, 2021

Appl. No.:

17/233251

Inventors:

- Jacksonville FL, US
Zachary Jasinski - Milwaukee WI, US
Matthew Petersen - Apex NC, US
Eric Bond - Whitefish Bay WI, US
Daniel Wakeman - Jacksonville FL, US
David Berglund - Ponte Vedra FL, US

International Classification:

G06Q 40/02
G06F 40/20
G06N 20/00

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.

US Patent:

20220335517, Oct 20, 2022

Filed:

Apr 16, 2021

Appl. No.:

17/233300

Inventors:

- Jacksonville FL, US
Zachary Jasinski - Milwaukee WI, US
Matthew Petersen - Apex NC, US
Eric Bond - Whitefish Bay WI, US
Daniel Wakeman - Jacksonville FL, US
David Berglund - Ponte Vedra FL, US

International Classification:

G06Q 40/02
G06F 40/20
G06N 20/00

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.

US Patent:

20220335518, Oct 20, 2022

Filed:

Apr 16, 2021

Appl. No.:

17/233340

Inventors:

- Jacksonville FL, US
Zachary Jasinski - Milwaukee WI, US
Matthew Petersen - Apex NC, US
Eric Bond - Whitefish Bay WI, US
Daniel Wakeman - Jacksonville FL, US
David Berglund - Ponte Vedra FL, US

International Classification:

G06Q 40/02
G06F 40/20
G06N 20/00
G06F 9/54

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.