AI Authentication Questions for Faster Call Center Verification
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Solution Overview
Problem
Existing customer authentication methods in call centers are time-consuming and resource-intensive, requiring extensive questioning and training, and pose challenges in protecting non-public information (NPI) and enabling document upload and customization of questions.
Innovation Solution
An AI platform utilizing generative AI and machine learning to generate customized questions based on customer interactions, ensuring secure handling of NPI and facilitating document upload and authentication processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional customer authentication methods are used with multiple questions, then customer authenticity can be verified, but the authentication process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent changes the parameter of question selection from static pre-defined sets to dynamic AI-generated questions based on customer behavior patterns, transaction context, and risk assessment. This allows the system to adapt the number and type of questions based on authentication confidence levels, reducing time for low-risk customers while maintaining security for high-risk cases
Solution Approach 2:
The AI system autonomously generates and selects authentication questions without requiring customer service agent intervention. The system self-adjusts the authentication process by analyzing customer responses in real-time and dynamically determining whether additional questions are needed, eliminating manual time consumption while maintaining verification accuracy
2Reliability
If customer service agents receive extensive training for enhanced customer service and interaction, then authentication capability improves, but training resources and time increase
Solution Approach 1:
The patent replaces the mechanical training process with an AI-based automated question generation and analysis system. Instead of training agents to ask and evaluate authentication questions, the system uses machine learning models to automatically generate context-aware questions, analyze customer responses, and make authentication decisions, eliminating the need for extensive agent training while maintaining or improving authentication capability
3Productivity
If AI is used to reduce the number of authentication questions, then authentication speed increases, but protecting non-public information (NPI) becomes more challenging
Solution Approach 1:
The AI system applies different levels of data protection to different types of information processed during authentication. Sensitive NPI is encrypted and processed through secure AI models with restricted access, while non-sensitive transaction context can be analyzed more openly. This localized security approach allows faster processing of less sensitive data while maintaining strict protection for critical information
Solution Approach 2:
The patent introduces an intermediary secure processing layer between the AI model and NPI data. The AI system interacts with encrypted representations of sensitive information rather than raw data, and uses secure enclaves or trusted execution environments to process authentication logic without exposing NPI. This intermediary layer enables AI-driven speed improvements while maintaining NPI protection
4Productivity
If AI generates customized questions based on customer interactions, then authentication efficiency improves, but system complexity increases
Solution Approach 1:
The AI platform is segmented into modular functional components: customer behavior analysis module, question generation module, response analysis module, and decision module. Each component performs a specific function and can be independently trained, deployed, and maintained. This modular architecture reduces overall system complexity by breaking down the complex AI system into manageable, interchangeable units that can be developed and updated separately
Data Source
AI summary
Systems and methods for supporting artificial intelligence (“AI”) customer service interactions between a customer service agent operating on an AI platform at a call center and a customer using an AI customer device are provided. Methods may include activating an AI session between the customer service agent and the customer. Methods may include authenticating the AI session between the customer service agent and the customer. Methods may include receiving a selection of AI questions from the AI platform and receiving answers to those AI questions via the AI customer device. Methods may include processing a co-browsing AI session request. Methods may include initiating the AI session between the customer service agent and the customer based on authentication of the answers to the AI questions.