Fidelity Information Services, LLC

David Berglund

Susheel Nesargi

Glen M Casey

Benjamin Wellmann

Matthew Petersen

Daniel Wakeman

Eric Bond

Zachary Jasinski

A smart forms solution that enables transactions institutions to provide configuration parameters in a streamlined manner so that developers can construct end-to-end solutions in an automated manner includes performing, by a processor, operations including: receiving data from a form in a webpage; validating the received data; saving the validated data; determining a location to send the saved data; sending the saved data to the determined location; invoking an API at the determined location with the saved data; and using the saved data to perform a manual operation.

Smart forms for automated configuration of solutions

Systems and methods are disclosed for generating a dynamic customized script to facilitate communication between a user and a service provider. The method includes receiving, via an interactive voice response (IVR) system, a request from a mobile device associated with at least one user. The contextual information associated with at least one user is processed, in real-time, based, at least in part, on the request. A dynamic customized script specific to the request is generated, in real-time, based, at least in part, on the processing of the contextual information. The request is routed, via the IVR system, to an agent from a pool of agents of the service provider. A presentation of the dynamic customized script is generated in a user interface of a device associated with the agent, wherein the dynamic customized script is step-by-step guidance to the agent for handling the request of at least one user.

Systems and methods for facilitating communication between a user and a service provider

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.

Systems and methods for analyzing documents using machine learning techniques

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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Ponte Vedra, FL, United States of America

David Berglund