AI Message Generation for Automated Client Inquiry Responses
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for responding to client inquiries in large commercial entities, such as banks, are often time-consuming and expensive due to the need for human intervention, despite the availability of information through self-service options.
Innovation Solution
Implementing an AI algorithm that uses machine learning techniques to automate the generation of responsive messages by analyzing client inquiries, extracting relevant information, determining if human intervention is required, and generating timely and accurate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human beings respond to client inquiries manually, then accuracy and customer service quality are maintained, but the process becomes time-consuming and expensive
Solution Approach 1:
The system enables automated self-service response generation where the AI algorithm independently analyzes client inquiries, retrieves relevant information, and generates responses without requiring human intervention for routine inquiries, thus reducing response time while maintaining accuracy through machine learning validation
Solution Approach 2:
The patent replaces the mechanical human response process with an automated AI-based system that uses machine learning algorithms to analyze inquiries, extract entities, and generate responses, eliminating the time-consuming manual workflow while preserving response quality through intelligent automation
2Reliability
If human beings respond to client inquiries manually, then complex or sensitive inquiries can be handled with judgment, but operational costs increase
Solution Approach 1:
The system segments inquiries into different categories (routine vs. complex) and applies appropriate handling methods: automated AI processing for routine inquiries and human intervention only for complex cases, thereby reducing operational costs while maintaining response quality through selective automation
Solution Approach 2:
The AI algorithm acts as an intermediary between client inquiries and human agents, pre-processing and filtering inquiries to identify those requiring human attention, thus reducing the volume of inquiries humans must handle and lowering operational costs while maintaining quality through intelligent triage
3Loss of energy
If self-service options are provided on the website, then operational costs are reduced, but some clients prefer human interaction
Solution Approach 1:
The system provides a universal response mechanism that can handle both automated and human-interaction preferences: the AI generates responses that can be directly sent or reviewed by human agents based on client preference, making the system adaptable to different client needs while maintaining cost efficiency through predominant automation
Solution Approach 2:
The system dynamically adjusts its response generation mode based on inquiry characteristics and client preferences: fully automated for routine inquiries, hybrid with human review for sensitive matters, and human-only for complex cases, thereby accommodating client versatility preferences while optimizing operational costs through adaptive automation
Data Source
AI summary
A method for automating a process of generating messages that are responsive to client inquiries by using an AI algorithm that implements a machine learning technique to ensure accuracy and timeliness in the responses is provided. The method includes: receiving a first message that includes an inquiry that relates to an account associated with a user; applying an AI algorithm for analyzing the first message in order to extract information that relates to the inquiry; determining, based on a result of the analysis, whether generating a response to the inquiry requires human intervention; when human intervention is not required, retrieving information that is responsive to the inquiry from a memory; generating a second message that includes the information that is responsive to the inquiry; and transmitting the second message to the user.