Bank of America Corporation

Richard Scot

Stephen Thomas Shannon

James J Siekman

Nathan Alexander Dalpini

Rebecca Lynn Pietro

Stephen Jack Williams

John Shelton

Abelardo Espinoza

Vani Reddy Nareddy

Michael Robert Young

Pratap Dande

James Alexander

Tomas Mata Castrejon, Iii

Peter George Kurrasch

Kevin P Thomsen

John Raymond Omernik

Penelope York

Elvin Crabbe, Jr

George Albero

Michael Ogrinz

Jinna Zevulun Kim

Elena Kvochko

Monika Kapur

Gilbert M Gatchalian

Daniel Joseph Serna

Vijaya L Vemireddy

Marcus Raphael Matos

Matthew K Bryant

Kelly Renee-Drop Keiter

Sanjay Arjun Lohar

Neal Aaron Slensker

Robert Nyeland Huggins

Erik Dahl

Benjamin F Tweel

Dinesh Kumar Agrawal

Steven Nathan Greene

Basil C Doyle

Jason Todd Greeter

Terry G McConnell

Graham Wyllie

Kesha Hamilton

Systems, computer program products, and methods are described herein for automatically classifying data based on data usage and accessing patterns in an electronic network. The present invention is configured to receive at least one query log comprising a plurality of data identifiers; generate a data identifier total based on each data identifier of the plurality of data identifiers; determine a data classification for each data identifier based on the data identifier total, wherein the data classification comprises at least one of an important classification or an unimportant classification; and generate a data catalogue comprising at least one data identifier associated with the important classification.

Systems, methods, and apparatuses for automatically classifying data based on data usage and accessing patterns in an electronic network

Arrangements for detecting unauthorized activity based on input method analysis and monitoring are provided. In some aspects, identity information associated with a user may be received and be stored. An input may be received from a computing device of the user. An input pattern of the received input may be determined. Using a machine learning model, the input pattern of the received input may be compared to input patterns of humans and input patterns of machines. Based on the comparison, it may be determined whether the user is a human user or a non-human user. Responsive to determining that the user is a non-human user, a request may be transmitted to the user to provide increased authentication credentials. Responsive to determining that the user is a human user, an identity of the user may be verified by comparing the input pattern of the received input to the stored identity information.

Unauthorized activity detection based on input analysis and monitoring

Arrangements for providing frictionless unauthorized activity detection and user authentication are provided. In some aspects, user data, such as transaction data may be received and used to train a machine learning model. In some examples, the machine learning model may be executed to generate one or more expected user patterns. In some arrangements, a request for transaction may be received. The request for transaction may include transaction details. In response, the system may request current geo-location data of a user. In some examples, the transaction details and geo-location data may be analyzed (e.g., compared to the expected user patterns) to generate an authentication output. The authentication output may then be transmitted to one or more systems to process the requested transaction, prevent transaction processing, or the like.

Secure user authentication using machine learning and geo-location data

Embodiments of the invention are directed to systems, methods, and computer program products for providing intelligent proactive identification of, and protection against, malfeasant data collection, user device control, or user account access. The invention utilizes a continuous data analysis technique in order to recognize patterns in device data and network communications indicative of remote device control requests, and responds to these identified data points by implemented an automated solution to protect users against unauthorized access of their devices or accounts.

System and methods for proactive protection against malfeasant data collection

Aspects of the disclosure relate to a decision model to open and close enterprise facilities. A computing platform may receive one or more attributes associated with an operation of an enterprise facility. Subsequently, the computing platform may identify, based on the one or more attributes, a business operation rule for the enterprise facility. Then, the computing platform may determine, based on the one or more attributes and the business operation rule, an operational parameter for the enterprise facility. Then, the computing platform may compare the operational parameter to a threshold parameter, where the threshold parameter may be indicative of whether to keep the enterprise facility open for business. Then, the computing platform may, upon a determination that the operational parameter fails to exceed the threshold parameter, determine to temporarily close the enterprise facility. Then, the computing platform may redistribute user traffic to one or more open customer resource facilities.

Machine learning based decision model to open and close enterprise facilities

Systems, computer program products, and methods are described herein for tracking resources using non-fungible tokens. The present invention is configured to receive a request for a non-fungible token (NFT) for a resource, wherein the resource is a physical object; initiate a non-fungible token (NFT) generator on the resource; generate a unique digital signature for the resource by generating a scanned image of the resource and identifying one or more features of the resource from the image; generate an NFT comprising a unique digital signature for the resource; determine a physical condition of the resource; store the physical condition of the resource in a first metadata layer associated with the NFT; and record the NFT for the resource on a distributed ledger.

System for tracking resources using non-fungible tokens

A device configured to receive a data transfer initiation request from a first user and to identify a user profile that is associated with a first user identifier for the first user. The device is further configured to obtain an account number and a routing number for the first user from the user profile. The device is further configured to obtain routing instructions for a service provider based on a service provider identifier. The device is further configured to generate a data transfer request that includes the account number for the first user, the routing number for the first user, a second user identifier for a second user, a data type identifier, and a data transfer type identifier. The device is further configured to send the data transfer request to the service provider in accordance with the routing instructions for the service provider.

Secure data transfer request routing for peer-to-peer services

A system for predicting an anomalous request comprises a processor associated with a server. The processor is configured to generate a first set of data objects associated with a first user profile. The processor is configured to compare the first set of the data objects to approved data and audit data to generate a second set of data objects with a set of anomalous data indicators for the first user profile. The processor is further configured to process the second set of the data objects through an anomaly learning model to determine a predictive degree of approval associated with the user profile. The processor is further configured to determine to how to process the user profile based on the predictive degree of approval. The processor is further configured to assign a profile indicator to the user profile.

System and method for predicting anomalous requests and preventing anomalous interactions in a network

Systems, computer program products, and methods are described herein for tracking resources using non-fungible tokens. The present invention is configured to electronically receiving, over a distributed computing network from a computing device of a user, a request for a non-fungible token (NFT) for a resource; initiating a non-fungible token (NFT) generator on the resource in response to receiving the request; generating a unique digital signature for the resource; generating, using the NFT generator, the NFT, wherein the NFT comprises at least the unique digital signature for the resource; and record the NFT for the resource on a distributed ledger.

Aspects of the disclosure relate to use of supervised, unsupervised, semi-supervised, or reinforcement-based machine learning algorithm(s) to perform pattern recognition and/or cluster detection on historical transactional data in order to predict events that may occur in the future and are candidates for automated delivery of items from a source to a customer in a contactless manner, via a secure delivery device, to minimize health risk(s) to the customer. The items may be securely delivered in an automated manner such as, for example, by use of a drone or autonomous vehicle, which may have advanced sensors to facilitate various aspects of the delivery. The items may be contained in a secure carrier coupled to the delivery device and may be unlocked locally or remotely using mechanical or digital means. Novel logical systems, architectures, machines, platforms, delivery devices and components thereof, and methods are disclosed.

Recommending and initiating contactless events based on machine learning

A system for predicting an anomalous request comprises a processor associated with a server. The processor is configured to parse a user profile from a plurality of user profiles to generate a first set of data objects associated with the first user profile. The processor is configured to compare the first set of the data objects to approved data and audit data to generate a second set of data objects with a set of anomalous data indicators for the first user profile. The processor is further configured to process the second set of the data objects through an anomaly learning model to determine a predictive degree of approval associated with the user profile. The processor is further configured to determine to approve, flag or disapprove the user profile based on the predictive degree of approval. The processor is further configured to assign a profile indicator to the user profile.

A system for predicting an anomalous request comprises a processor associated with a server. The processor is configured to parse a user profile from a plurality of user profiles to generate a first set of data objects associated with the first user profile. The processor is configured to compare the first set of the data objects to approved data, audit data, disapproved data to generate a second set of data objects with a set of anomalous data indicators for the first user profile. The processor is further configured to process the second set of the data objects through an anomaly learning model to determine a predictive degree of approval associated with the user profile. The processor is further configured to determine to approve, flag or disapprove the user profile based on the predictive degree of approval. The processor is further configured to assign a profile indicator to the user profile.

Aspects of the disclosure relate to a cognitive automation based compliance management system. A computing platform may receive, via a network, an alert notification indicative of a change to a legal framework applicable to an enterprise organization. Subsequently, the computing platform may, in response to the alert notification, detect a task that may be impacted by the change. Then, the computing platform may, upon detecting the task, identify at least one task parameter associated with the task. Then, the computing platform may compare the at least one task parameter with the change to determine whether the task is impacted by the change. Subsequently, the computing platform may, upon a determination that the task is impacted by the change, recommend, via the network, one or more actions associated with the task.

Cognitive automation based compliance management system

Growing community of inventors

Huntersville, NC, United States of America

Richard Scot