GenAI Wealth Advisor Assistant for Meeting Content Reuse
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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 allow for the efficient reuse of similar discussion topics and asset information.
Innovation Solution
An apparatus and method utilizing generative artificial intelligence (GenAI) to receive user conversations, identify goals, associate them with relevant users, generate call scripts, and integrate these scripts into digital calendars, thereby optimizing the discussion of similar content with different users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If wealth advisors manually prepare and discuss similar content with multiple clients during meetings, then personalized service quality is maintained, but time consumption and operational efficiency increase
Solution Approach 1:
The system creates synthetic meeting transcripts and asset recommendations by copying and adapting content from previous client meetings. The generative AI model generates new meeting content based on patterns from existing meetings, allowing advisors to reuse validated content structures while maintaining client-specific customization.
Solution Approach 2:
The system enables self-service content generation where the generative AI model automatically creates meeting transcripts, identifies redundant content, and generates asset recommendations without requiring manual preparation by the advisor. The system serves itself by processing historical data and producing ready-to-use meeting materials.
2Productivity
If wealth advisors manually identify and recommend assets for each client, then investment strategy precision is maintained, but productivity decreases due to repetitive manual analysis
Solution Approach 1:
The system replaces manual mechanical analysis of client portfolios with an automated generative AI model. The AI processes client data, identifies suitable assets, and generates recommendations by learning from historical meeting patterns and investment strategies, eliminating repetitive manual analysis while maintaining precision through trained algorithms.
Solution Approach 2:
The system incorporates feedback loops where meeting transcripts and asset recommendations are continuously refined based on client responses and market conditions. The generative AI model learns from actual client preferences and market performance data to improve the precision of future recommendations automatically.
3Adaptability or versatility
If meeting content is customized for each client individually, then service quality is improved, but device complexity and system requirements increase
Solution Approach 1:
The system uses a universal generative AI model that can handle multiple client types, asset classes, and meeting scenarios through a single integrated platform. The model adapts to different client profiles and investment goals by learning from diverse historical data, eliminating the need for separate specialized systems for each client segment.
Solution Approach 2:
The system adjusts content parameters dynamically based on client-specific factors such as risk tolerance, investment horizon, and financial goals. The generative AI model modifies language, tone, and asset selections by changing parameters based on input data, enabling customization without requiring complex manual configuration systems.
Data Source
AI summary
An example operation may include one or more of storing a portfolio of assets of a user in memory, receiving text from a conversation between the user on a first device and a second user on a second device, identifying an upcoming life event of the user based on execution of a generative artificial intelligence (GenAI) model on the received text from the conversation, determining a change to the portfolio of assets of the user based on the upcoming life event and existing assets within the portfolio of assets, and displaying the change to the portfolio of assets of the user via a user interface.