GenAI Portfolio Generation Using Missing Asset Identification
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Solution Overview
Problem
Wealth advisors face challenges in leveraging redundant content from meetings with multiple clients, as current technologies do not provide a way to utilize this overlapping information effectively.
Innovation Solution
An apparatus and method that utilize a generative artificial intelligence (GenAI) model to identify goals from user conversations, associate these goals with other users, generate call scripts, and integrate them into digital calendars, thereby optimizing discussion content across different user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If wealth advisors manually review and reuse content from previous meetings with multiple clients, then the quality and personalization of wealth management discussions can be maintained, but the time and effort required increases significantly
Solution Approach 1:
The system automatically generates call scripts by copying and adapting content from previous meeting transcripts. The GenAI model identifies relevant discussions about assets and strategies from past meetings and creates personalized call scripts for future meetings, eliminating manual content creation while maintaining quality and personalization
Solution Approach 2:
The system performs self-service by automatically analyzing meeting transcripts, identifying redundant content, and generating call scripts without human intervention. The GenAI model autonomously processes the information and produces personalized content that advisors can directly use in their meetings
2Productivity
If wealth advisors discuss the same assets and strategies with multiple clients during separate meetings, then comprehensive coverage of investment options is achieved, but redundant content overlaps across meetings reduce efficiency
Solution Approach 1:
The system extracts redundant content from meeting transcripts by using GenAI to identify and separate common discussions about assets and strategies from client-specific information. This allows the system to pull out overlapping content and apply it efficiently across multiple call scripts, reducing redundancy while maintaining comprehensive coverage
Solution Approach 2:
The system segments meeting content into universal portions (applicable to multiple clients) and client-specific portions. By dividing the content in this way, the system can efficiently reuse the universal segments across multiple meetings while preserving the unique, personalized information for each client
3Adaptability or versatility
If wealth advisors create personalized call scripts for each client based on their unique needs and goals, then the quality of wealth management service is enhanced, but the complexity and time required for script preparation increases
Solution Approach 1:
The system achieves universality by creating a multi-functional GenAI model that can handle various aspects of call script generation simultaneously. The model processes meeting transcripts, identifies client goals, extracts relevant asset information, and generates personalized scripts all through a single integrated system, reducing complexity while maintaining personalization
Data Source
AI summary
An example operation may include one or more of storing a portfolio of assets of a user in memory, receiving contextual data of the user from a user device of the user, identifying an asset of interest that is not included in the portfolio of assets of the user based on execution of a generative artificial intelligence (GenAI) model on the portfolio of assets of the user and the received contextual data of the user, generating a different portfolio of assets based on the asset of interest that is not included in the portfolio of assets of the user, and displaying the different portfolio of assets via a user interface.