AI Registration Monitoring With False Positive Identification
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Solution Overview
Problem
Existing security-focused AI models generate false positives during user registration, leading to user account blocks and loss of potential customers due to time-consuming manual verification processes.
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, and applying the trained model to user registration processes.
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 leading to legitimate user blocks
Solution Approach 1:
The system divides the monitoring task into two separate AI models: a security-focused model for detecting fraudulent registrations and a false positive identification model for identifying legitimate users incorrectly flagged as fraudulent. This segmentation allows each model to specialize in its specific function, improving overall accuracy while maintaining security detection capability.
Solution Approach 2:
The false positive identification model acts as an intermediary between the security model's output and the final registration decision. When the security model flags a user as potentially fraudulent, the intermediary model evaluates whether this is a true positive or a false positive before blocking the registration, thereby reducing incorrect rejections of legitimate users.
2Measurement precision
If manual verification is performed for blocked users, then false positives can be corrected, but time loss and operational effort increase
Solution Approach 1:
The false positive identification model performs preliminary automated verification before manual review is needed. By pre-evaluating flagged registrations and identifying likely false positives, the system reduces the number of cases requiring manual verification and accelerates the overall verification process for legitimate users.
Solution Approach 2:
The system enables automated self-verification through the dual-model AI approach, where the false positive identification model autonomously evaluates and corrects obvious false positives without human intervention. This self-service capability handles routine cases automatically, reserving manual verification only for complex or ambiguous situations.
3Device complexity
If a single AI security model is used, then device complexity is minimized, but the ability to distinguish true positives from false positives deteriorates
Solution Approach 1:
The monitoring system is segmented into two specialized AI models with distinct functions: the security model focuses exclusively on detecting fraudulent patterns, while the false positive identification model focuses on identifying legitimate users among flagged cases. This functional segmentation improves identification accuracy by allowing each model to optimize for its specific task rather than attempting to handle both functions in a single model.
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.