AI-Based User Account Misappropriation Detection with Risk Scoring

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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 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 time consumption and computational burden

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 intervention. The system uses its own computational resources to process transactions autonomously, eliminating the need for human operators to manually review each request while maintaining detection capabilities.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical analysis with an AI-based automated system. Instead of human operators manually examining data transmission requests, the AI engine uses machine learning algorithms to automatically assess misappropriation risk, substituting human cognitive processing with automated computational mechanisms that operate continuously without fatigue.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If comprehensive analysis of data transmission requests is performed to identify misappropriation attempts, then detection reliability is improved, but computing resource consumption and system complexity increase

Engineering Contradiction:

Improvedetection reliabilityVSAvoidcomputing resource consumption

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The AI engine dynamically adjusts analysis parameters based on the characteristics of each data transmission request. Instead of applying uniform comprehensive analysis to all transactions, the system modifies its analytical depth and computational intensity according to risk indicators, transaction type, and historical patterns, thereby maintaining high reliability while optimizing resource consumption.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system applies partial analysis only where necessary by focusing computational resources on high-risk transactions that exhibit suspicious patterns. For low-risk transactions, the system uses simplified assessment mechanisms, avoiding excessive computational action on all transactions uniformly and instead concentrating resources on areas where misappropriation risk is most likely to occur.

Inventive Principle:

Principle #16Partial or excessive action

3Productivity

If automated AI-based detection is implemented to improve efficiency, then productivity is improved, but measurement precision and detection accuracy may deteriorate without proper validation

Engineering Contradiction:

Improvesystem efficiencyVSAvoiddetection accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The AI engine incorporates feedback mechanisms that continuously learn from actual misappropriation outcomes and adjust its detection algorithms accordingly. By analyzing the results of its predictions and comparing them with confirmed misappropriation cases, the system refines its accuracy over time, ensuring that automated detection maintains high precision while achieving improved productivity.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary training and validation of its AI models using historical data and known misappropriation patterns before deploying automated detection. This preliminary action ensures the system is properly calibrated and accurate before full-scale operation, preventing precision deterioration while enabling efficient automated processing.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250272374A1Systems, methods, and apparatuses for detecting user account misappropriation attempts using artificial intelligence in an electronic network

Publication Date: 2025.08.28 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.