Client Authentication Risk Scoring for Contact Center Verification
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Solution Overview
Problem
Current methods for client authentication in contact centers, particularly in the financial industry, are ineffective due to repetitive and irrelevant questioning, lack of integrated risk assessment, and limited information exchange between departments, leading to poor client experience and inefficient identity verification.
Innovation Solution
A web application utilizing a machine learning classification model with a self-training semi-supervised SVM model to assess client risk levels, generate tailored authentication questions, and provide insights, enhancing agent decision-making across different departments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional authentication methods are used in contact centers, then client identity verification is performed, but repetitive and irrelevant questioning occurs leading to poor client experience
Solution Approach 1:
The system tailors authentication questions to each specific client by analyzing their profile data, risk indicators, and interaction history. Instead of using generic authentication questions for all clients, the system dynamically selects and generates questions that are locally optimized for each individual client's context, making authentication more relevant and less repetitive.
Solution Approach 2:
The system performs preliminary risk assessment and client analysis before the authentication process begins. By pre-evaluating client profiles, transaction histories, and risk indicators, the system prepares personalized authentication question sets in advance, allowing agents to skip irrelevant questions and focus only on those necessary for each specific client's authentication needs.
2Reliability
If comprehensive client information is collected for authentication, then authentication accuracy improves, but information exchange barriers between departments persist
Solution Approach 1:
The system creates a universal client profile that consolidates information from multiple departments and sources into a single accessible repository. This multi-functional profile serves authentication purposes, risk assessment needs, and information sharing requirements simultaneously, allowing any department to access relevant client information without siloed data storage, thereby improving both authentication accuracy and inter-departmental information exchange.
3Productivity
If risk assessment is integrated into authentication process, then authentication efficiency improves, but system complexity increases
Solution Approach 1:
The system segments the authentication process into distinct functional modules: risk indicator collection, risk scoring calculation, question generation, and authentication verification. Each module handles a specific aspect of the authentication process independently, making the overall complex system manageable through clear separation of concerns. This modular segmentation allows the system to integrate comprehensive risk assessment while maintaining operational simplicity through defined module interfaces.
Data Source
AI summary
Methods, systems, and techniques for facilitating client authentication are disclosed, comprising: receiving an identifier of a client; retrieving client information based on the identifier of the client; assessing a plurality of risk indicators for the client from the client information; determining a risk level for the client based on the plurality of risk indicators; and outputting the risk level for display on a user device.