Textual Behavioral Authentication for Real-Time Imposter Detection
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Solution Overview
Problem
In hybrid work environments, there is a need to authenticate the identity of agents in digital multi-channel contact centers to prevent unauthorized individuals from performing tasks assigned to authorized agents, which can hinder professional growth and may be illegal.
Innovation Solution
A computerized method and system using a Machine Learning (ML) textual behavioral identification model trained on historical interactions to generate profile-identity-data for agents, processing real-time textual responses to calculate an imposter-probability score, and taking actions when the score exceeds a threshold, utilizing NLP embeddings and algorithms like Dijkstra and Earth Movers Distance to authenticate agent identity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional authentication methods are used in hybrid work environments, then implementation is simple, but they cannot effectively detect imposter agents
Solution Approach 1:
The patent replaces traditional mechanical authentication systems (passwords, badges) with an AI-based textual behavioral identification model that analyzes language patterns, typing rhythms, and communication styles to detect imposter agents, thereby improving reliability without requiring complex hardware infrastructure
Solution Approach 2:
The system introduces an intermediary AI model that processes textual interactions between agents and customers, using natural language analysis to verify agent identity indirectly through behavioral patterns rather than direct authentication challenges
2Measurement precision
If behavioral analysis is performed on all agent interactions, then detection accuracy improves, but processing time increases
Solution Approach 1:
The system performs partial behavioral analysis by focusing on key textual indicators and critical interaction moments rather than analyzing every single word, achieving sufficient detection accuracy while maintaining real-time processing capabilities
Solution Approach 2:
The AI model performs preliminary training offline on historical interaction data to learn agent behavioral patterns, enabling faster real-time detection during actual interactions by applying pre-learned knowledge rather than analyzing everything from scratch
3Reliability
If the ML model is retrained frequently on recent interactions, then detection accuracy is maintained, but computational resources are consumed
Solution Approach 1:
The system implements periodic retraining of the ML model at scheduled intervals rather than continuous retraining, maintaining detection accuracy while significantly reducing computational resource consumption by training only when necessary
Solution Approach 2:
The system adjusts training parameters dynamically, using smaller learning rates and reduced batch sizes during periodic retraining compared to initial training, thereby maintaining model accuracy while consuming fewer computational resources during maintenance phases
Data Source
AI summary
A computerized-method for training and applying a Machine Learning (ML) textual behavioral-identification-model to authenticate an agent, in a digital multi-channel environment, is provided herein. The computerized-method may include: (i) training a ML-textual-behavioral-identification-model using retrieved textual responses of each agent in one or more historical-interactions which were conducted in a controlled environment, as a training dataset. The ML-textual-behavioral-identification-model may be configured to process the retrieved textual responses of each agent to generate a profile-identity-data for each agent to be used to authenticate identity of the agent; (ii) receiving a textual-response of each agent, when the agent starts an interaction with a customer; (iii) applying the textual-response of the agent to the ML-textual-behavioral-identification-module to authenticate an identity of the agent in real-time by calculating an imposter-probability score for the agent and; (iv) sending the imposter-probability score to a file-management-system to take one or more actions when the imposter-probability score is above a preconfigured-threshold.