Bank of America Corporation

Nancy T Carrier

Linda Haddad

Rosemary Hill

Susan J Moss

Kathleen D Schaumburg

Andrew Yardley Vlasic

Selvakumar Albert

Kyle Kreth

Jeremiah Thompson

Brandon Sloane

David Neil Joffe

A prepaid electronic funds transferring system, components, and method is provided for users that do not have checking or savings accounts. A user has a digital currency store such as, for example, in a non-checking non-savings prepaid account associated with a secure unified prepaid banking card and/or smartphone app. Interest may be provided by an issuer on funds in the digital currency store. Debit and credit transactions may be processed. NFC and UWB geo-locating can be used to find lost cards and/or the mobile device on which the app is loaded. Electronic transactions may be received and performed without requiring a recipient to download an app. Secure semiconductors may facilitate wireless and non-wireless communications with ATMs and POS machines. QR codes may identify accounts, constitute private/public encryption keys, or other relevant info. A cold storage or hot storage system may be implemented for the digital currency store.

Secured, unified, multifunctional, digital currency store with machine-readable card and/or mobile app

A proxy-based method for improving digital security during a user's travel is provided. The method may include determining a bundle of merchant category classification (MCC) codes. This bundle preferably reflects a baseline travel condition. The baseline travel condition is associated with a user mobile device. The method may also include dynamically updating a characteristic associated with the bundle of MCC codes based on updated travel conditions. The method may also include determining an occurrence of an anomalous user mobile device activity. The determination may be based on a comparison of the updated characteristic associated with the bundle of MCC codes and the baseline travel conditions; and in response to a determination of anomalous user mobile device activity, increasing a security level associated with the mobile device associated with the user.

Multi-MCC code bundle for use in increasing travel security

Aspects of the disclosure relate to using machine learning techniques for generating automated suspicious activity reports (SAR). A computing platform may generate a labelled transaction history dataset by combining historical transaction data with historical report information. The computing platform may train a convolutional neural network using the labelled transaction history dataset. The computing platform may receive new transaction data and compress the new transaction data using lossy compression. The computing platform may input the compressed transaction data into the convolutional neural network, which may cause the convolutional neural network to output a suspicious event probability score based on the compressed transaction data. The computing platform may determine whether the suspicious event probability score exceeds a predetermined threshold and, if so, the computing platform may send one or more commands directing a report processing system to generate a SAR, which may cause the report processing system to generate the SAR.

Neural network host platform for generating automated suspicious activity reports using machine learning

Embodiments of the invention are directed to classifying requests associated with personal data at or before a point of entry to a trusted computing network. The invention provides for determining whether a request associated with personal data requires classification (for example, whether the request is impacted by regulations or other requirements necessitating classification/categorization). The determination may be based on what entity is requesting the data, the origin of the request, whose data is being requested, the type of action associated with the request and/or the data elements associated with the request. In addition, once the request has been determined to require classification the specific classification is determined and assigned to the request. The classification may be determined based on the rules associated with the regulation or other requirement(s) necessitating the classification/categorization. The classification may identify rules for processing the request along with routing for the request.

Entry point classification of requests requiring access to data

Classification of personal data in incoming or outgoing data files in-line or pre-firewall. The invention determines which data owners and/or data associated with the data owners requires classification (e.g., which individuals/customers and/or data is applicable to internal or external regulations) and, subsequently determines the classifications and identifies the classifications in the data file the data owners and data within the data file so that the data can be routed according to the identified classifications. In specific embodiments machine-learning processing is used to learn, determine and/or predict which data owners and/or data associated with the individual/customers requires classification and the classifications to assign to those data owners and/or data elements.

Pre-firewall data classification

Embodiments of the present invention relate to systems, methods and computer program products for evaluating customers' ability to manage revolving credit. To do so, for example, embodiments of the present invention review aggregated transaction data taken from a large number of revolving-credit accounts, and detect credit-usage patterns at an account level. The detected credit-usage patterns are then applied in risk models to evaluate individual customers' ability to manage revolving credit.

Growing community of inventors

Charlotte, NC, United States of America

Nancy T Carrier