20240037655. MACHINE LEARNING BASED AUTOMATED MANAGEMENT OF CUSTOMER ACCOUNTS simplified abstract (Bank of America Corporation)

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MACHINE LEARNING BASED AUTOMATED MANAGEMENT OF CUSTOMER ACCOUNTS

Organization Name

Bank of America Corporation

Inventor(s)

Siten Sanghvi of Westfield NJ (US)

Morgan S. Allen of Waxhaw NC (US)

Matthew E. Carroll of Charlotte NC (US)

Tamara S. Kingston of Peoria AZ (US)

Stephen T. Shannon of Charlotte NC (US)

MACHINE LEARNING BASED AUTOMATED MANAGEMENT OF CUSTOMER ACCOUNTS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240037655 titled 'MACHINE LEARNING BASED AUTOMATED MANAGEMENT OF CUSTOMER ACCOUNTS

Simplified Explanation

The abstract of the patent application describes a system for automated management of a user account using machine learning. The system analyzes the historical activity of a user and identifies patterns. It then detects any deviations from these patterns and identifies anticipated transactions. The system retrieves user-defined preference rules associated with the anticipated transaction and determines if these rules apply to the attributes of the transaction. If the rules apply, the system triggers an action associated with the transaction.

  • The system uses machine learning to analyze user activity and identify patterns.
  • It detects deviations from the patterns and identifies anticipated transactions.
  • User-defined preference rules are retrieved from a data repository.
  • The system determines if the preference rules apply to the attributes of the anticipated transaction.
  • If the rules apply, an action associated with the transaction is triggered.

Potential applications of this technology:

  • Automated management of user accounts in various industries such as banking, e-commerce, and social media.
  • Fraud detection and prevention by identifying deviations from user activity patterns.
  • Personalized user experiences by applying user-defined preference rules to transactions.

Problems solved by this technology:

  • Manual monitoring and management of user accounts can be time-consuming and prone to errors.
  • Detecting deviations from user activity patterns and identifying anticipated transactions can help prevent fraudulent activities.
  • Applying user-defined preference rules can enhance user satisfaction and customization.

Benefits of this technology:

  • Improved security by detecting and preventing fraudulent activities.
  • Enhanced user experience through personalized and customized transactions.
  • Time and cost savings by automating the management of user accounts.


Original Abstract Submitted

aspects of the disclosure relate to machine learning based automated management of a user account. a computing platform may determine, via a computing device and based on historical user activity of a user, a pattern of the user activity. subsequently, the computing platform may detect a deviation from the pattern of the user activity. then, the computing platform may identify an anticipated transaction of the user. then, the computing platform may retrieve, from a repository of user data, one or more user-defined preference rules associated with the anticipated transaction. then, the computing platform may determine whether the one or more preference rules apply to one or more attributes of the anticipated transaction. subsequently, the computing platform may trigger, based on a determination that the one or more preference rules apply to the one or more attributes of the anticipated transaction, an action associated with the anticipated transaction.

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