Automated Client Verification During Messaging for High-Risk Actions
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Solution Overview
Problem
Current messaging systems require agents to switch between messaging and verification systems to perform high-risk actions, leading to inefficiencies due to increased verification time.
Innovation Solution
An automated verification process using a machine learning model with a neural network to authenticate clients during a messaging session, determining high-risk needs and satisfying client requirements without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If agents manually verify clients through separate verification systems for high-risk actions, then verification security is maintained, but verification time increases and efficiency decreases
Solution Approach 1:
The patent merges the messaging system and verification system into a unified platform. The verification process is integrated directly within the messaging interface, allowing agents to perform verification without switching between separate systems. This integration maintains security requirements while eliminating the time loss associated with manual system switching and data entry.
Solution Approach 2:
The system implements automated verification processes that can operate without constant agent intervention. The integrated platform enables self-service verification capabilities where the system can automatically authenticate clients through multiple factors (biometric, device, behavioral) reducing the time agents spend on manual verification while maintaining security standards.
2Reliability
If agents switch between messaging and verification systems to perform high-risk actions, then verification can be completed, but agent productivity decreases due to increased operational complexity
Solution Approach 1:
By combining messaging and verification functionalities into a single integrated system, the patent eliminates the need for agents to switch between applications. The unified interface allows verification to be performed seamlessly within the existing messaging workflow, reducing operational complexity and improving agent productivity while ensuring verification completion.
Solution Approach 2:
The integrated messaging system incorporates multiple verification methods (biometric, device, behavioral, knowledge-based) within a single platform. This multi-functional approach allows the system to handle various verification requirements without requiring separate specialized tools, thereby improving agent productivity while maintaining reliable verification completion.
3Reliability
If manual verification processes are used for high-risk actions, then security requirements are met, but system complexity increases due to multiple separate systems
Solution Approach 1:
The patent consolidates multiple separate systems (messaging platform, verification system, authentication services) into a single integrated architecture. This merger maintains all necessary security requirements while reducing system complexity by eliminating the need for agents to navigate between multiple independent systems, thereby meeting security requirements with a more streamlined architecture.
Solution Approach 2:
While integrating systems, the patent employs modular design where different verification methods (biometric, device, behavioral, knowledge-based) are segmented as independent modules within the unified platform. This segmentation allows the system to maintain complex security capabilities while presenting a simplified interface to agents, effectively managing both security requirement fulfillment and system complexity.
Data Source
AI summary
A system and method for providing an automated verification of a client of a bank for performing a high risk action during a messaging session. The method includes corresponding with the client through an online messaging system to determine a need of the client, activating an automated verification process that authenticates the client if the need of the client is determined to be a high risk need, where the automated verification process employs a machine learning model that uses at least one neural network having nodes that have been trained to authenticate a person, determining that the client is authenticated using the automated verification process, and satisfying the need of the client.