AI Malfeasant Detection Engine for Electronic Network Threat Indexing
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Solution Overview
Problem
Existing systems face challenges in accurately and efficiently detecting malfeasant user activities in electronic networks, especially when limited data is available and the user is remote from entities that would fulfill their requests, due to difficulties in verifying user capabilities and resources.
Innovation Solution
A system utilizing a malfeasant identification AI engine that identifies resource transmission requests, collects relevant data, applies machine learning algorithms to determine malfeasant attributes, and generates threat indices to flag potential malfeasant activities, thereby enhancing detection accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual verification methods are used to detect malfeasant user activity, then detection accuracy may be maintained through human judgment, but productivity decreases due to labor-intensive processes and increased loss of time
Solution Approach 1:
The patent replaces manual verification processes with an automated AI-based system that uses machine learning models to analyze user activity data, device information, and transaction patterns. This substitution eliminates the need for human operators to manually review each transaction while maintaining or improving detection accuracy through consistent application of trained detection algorithms.
Solution Approach 2:
The system enables self-service detection by automatically collecting and analyzing data without requiring manual intervention. The AI engine autonomously processes user activity data, compares it against learned patterns of malfeasant behavior, and generates detection results, allowing the system to serve itself in the detection process rather than relying on external human verification.
2Measurement precision
If extensive data collection and manual analysis processes are implemented, then detection thoroughness improves, but loss of time increases due to processing delays
Solution Approach 1:
The patent replaces time-consuming manual data analysis with automated AI processing that can evaluate extensive datasets instantaneously. The machine learning engine simultaneously processes multiple data sources including user activity logs, device information, and transaction records, delivering thorough detection results in real-time rather than through sequential manual review processes.
Solution Approach 2:
The system performs preliminary data collection and preprocessing automatically before detection is needed. By continuously gathering and organizing user activity data, device information, and transaction patterns in advance, the system prepares structured datasets that the AI engine can rapidly analyze when detection is required, reducing actual processing time while maintaining thoroughness.
3Productivity
If automated AI-based detection systems are implemented, then productivity increases through automation, but device complexity increases due to sophisticated algorithms and data processing requirements
Solution Approach 1:
The patent divides the complex detection system into distinct functional modules: data collection components that gather user activity information, AI engine components that perform pattern recognition and classification, and decision components that generate detection results. This segmentation allows each module to be independently optimized and managed, reducing overall system complexity while maintaining high productivity through automated processing.
Solution Approach 2:
The AI-based detection system is designed to handle multiple types of user activities, device configurations, and transaction patterns through a single unified platform. The machine learning engine can adapt to various detection scenarios without requiring separate specialized systems, thereby increasing productivity across diverse use cases while managing complexity through a universal architecture.
4Ease of operation
If limited data is available for verification, then ease of operation improves by reducing data requirements, but measurement precision deteriorates due to insufficient information for accurate detection
Solution Approach 1:
The patent introduces an AI-based intermediary system that bridges the gap between limited available data and accurate detection requirements. The machine learning engine acts as a mediator that can infer missing information by analyzing patterns in the available data and comparing against learned models of malfeasant behavior, thereby maintaining detection accuracy even when complete data sets are not available.
Solution Approach 2:
The system dynamically adjusts detection parameters and data requirements based on the quality and quantity of available information. When data is limited, the AI engine modifies its analysis approach by focusing on the most discriminative features and adjusting confidence thresholds, allowing accurate detection to proceed with fewer data inputs while maintaining operational ease.
Data Source
AI summary
Systems, computer program products, and methods are described herein for threat indexing and implementing artificial intelligence (AI) to detect malfeasant user activity in an electronic network. The present invention is configured to identify a resource transmission request associated with a user account; collect resource account data of the user account; collect resource transmission request data associated with the resource transmission request; apply a malfeasant identification artificial intelligence (AI) engine to the resource account data and the resource transmission request data; and determine, by the malfeasant identification AI engine, a malfeasant attribute of the resource transmission request, wherein the malfeasant attribute comprises at least one of a positive malfeasant attribute or a negative malfeasant attribute.