AI-Based User Account Misappropriation Detection with Adaptive Analysis

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Solution Overview

Problem

Existing electronic networks face challenges in accurately, efficiently, and securely identifying user account misappropriation attempts without undue hardship on computing systems and excessive manual input.

Innovation Solution

A system utilizing artificial intelligence (AI) to analyze data transmissions, determine misappropriation likelihood, and generate user account reports with misappropriation attributes, reducing the need for manual input and conserving computing resources.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual input is used for each data transmission request to detect misappropriation attempts, then detection accuracy may be improved, but system efficiency and productivity deteriorate due to undue hardship on computing systems and waste of time

Engineering Contradiction:

Improvedetection accuracyVSAvoidsystem efficiency

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The AI engine performs self-service by automatically analyzing data transmission requests and determining misappropriation likelihood without requiring manual input. The system uses its own computational resources to process transactions, generate reports, and update authentication requirements autonomously, eliminating the need for human intervention while maintaining detection capabilities.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical processing with an AI-based automated system. The AI engine substitutes human analysis with machine learning algorithms that process data transmission requests, evaluate misappropriation risk, and generate reports automatically, thereby improving system efficiency while maintaining detection accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If AI engine is applied to analyze all data transmission requests, then detection accuracy and efficiency are improved, but computing resource consumption increases

Engineering Contradiction:

Improvedetection accuracyVSAvoidcomputing resource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The AI engine applies partial action by selectively analyzing data transmission requests based on risk assessment. Rather than processing every request with full computational depth, the system evaluates transactions and applies enhanced analysis only when misappropriation likelihood is detected, conserving computing resources while maintaining high detection accuracy for suspicious activities.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system changes processing parameters dynamically based on the nature of each data transmission request. The AI engine adjusts computational intensity, analysis depth, and resource allocation according to the detected misappropriation likelihood, using fewer resources for low-risk transactions and reserving intensive processing for high-risk cases.

Inventive Principle:

Principle #35Parameter changes

3Speed

If real-time analysis of data transmission requests is performed, then response time is improved, but computing system burden increases

Engineering Contradiction:

Improveresponse timeVSAvoidcomputing system burden

Core Design Contradiction:

SpeedVSDevice complexity

Solution Approach 1:

The AI engine performs preliminary action by pre-establishing authentication requirements and misappropriation detection criteria before transactions occur. The system proactively updates authentication requirements based on historical data and patterns, so that when transactions occur, the analysis can proceed quickly using pre-defined parameters rather than performing complex real-time calculations.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms where the AI engine continuously learns from past transactions and updates its detection algorithms. This feedback loop allows the system to improve its efficiency over time, reducing computing burden by optimizing its analysis based on actual transaction patterns and misappropriation behaviors observed in the network.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12386932B2Systems, methods, and apparatuses for detecting user account misappropriation attempts using artificial intelligence in an electronic network

Publication Date: 2025.08.12 BANK OF AMERICA CORP

AI summary

Systems, computer program products, and methods are described herein for detecting user account misappropriation attempts using artificial intelligence (AI) in an electronic network. The present invention is configured to access a user account database, wherein the user account database comprises at least one user account data; identify a current data transmission associated with a user account, wherein the user account is associated with the user account database; apply the current data transmission to a user account misappropriation AI engine; determine, by the user account misappropriation AI engine, a misappropriation likelihood of the current data transmission; generate a user account report based on the current data transmission and the misappropriation likelihood, the user account report comprising a user account identifier associated with the user account; and generate, based on the misappropriation likelihood, a misappropriation attempt attribute for the current data transmission.