AI Engine Training for Real-Time False-Positive Reduction
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Solution Overview
Problem
Existing security-focused AI models generate false positives during user registration, leading to user frustration and potential loss of customers due to unnecessary blocking, as current methods lack real-time detection and resolution of false positives.
Innovation Solution
A system and method for training an AI engine that includes a false positive identification model, utilizing a neural network and Bayesian machine learning algorithm, to identify and reduce false positives in real-time by correlating data from AI security models and a false positive database, allowing for immediate user registration decisions based on accurate classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a security-focused AI model is used to monitor user registration, then security detection capability is improved, but false positive rate increases causing user blocking
Solution Approach 1:
The system segments the security monitoring process into two distinct AI models: a primary security-focused AI model for initial threat detection, and a secondary false positive identification model for reviewing borderline cases. This segmentation allows each model to specialize in its function, improving overall accuracy while reducing false positives in final decisions
Solution Approach 2:
The false positive identification model acts as an intermediary between the security model's positive results and final user blocking. When the security model identifies potential threats, the intermediary model reviews these cases to distinguish true positives from false positives before enforcement actions are taken
2Ease of operation
If users contact organization personnel for false positive appeals, then false positive resolution is achieved, but time loss increases
Solution Approach 1:
The system implements self-service by enabling automated false positive identification and resolution through the secondary AI model. Users experience immediate automated review and resolution of false positives without needing to contact human personnel, making the process both easier and faster
Solution Approach 2:
The false positive identification model performs preliminary review and classification of security model results before final decisions are made. This preliminary action automatically resolves many false positive cases before they would require human intervention, reducing both time loss and operational complexity
3Device complexity
If a single AI security model is used for user registration monitoring, then device complexity is minimized, but measurement precision of fraud detection decreases
Solution Approach 1:
The system divides fraud detection into two specialized AI models with distinct functions: the security model focuses on identifying potential threats using various security features, while the false positive identification model specializes in reviewing borderline cases and applying different classification criteria, achieving higher overall precision
Solution Approach 2:
Different parts of the detection system have specialized qualities optimized for their specific functions. The security model is optimized for broad threat detection with sensitivity to various fraud patterns, while the false positive model is optimized for nuanced classification of borderline cases, creating locally optimized detection quality throughout the system
Data Source
AI summary
A system and method for training an artificial intelligence engine for real-time monitoring to eliminate false positives is disclosed. The system includes at least one processor, a communication interface coupled to the processor, and a memory device storing executable code. Executing the executable code causes the processor to receive data from an AI security model, receive data from a false positive database, and correlate both sets of data. The correlated data is used to generate a training dataset and a test dataset used to train a false positive identification model. After evaluating the false positive identification model, an AI engine is applied to user registration. The AI engine includes an AI security model and the false positive identification model. Additionally, a system for evaluating the security of user registration utilizing the false positive identification model is disclosed.