Personalized AI Chatbot Using Financial Profiles and Authentication
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Solution Overview
Problem
Existing automated chatbots, including those using AI/ML algorithms, lack personalization for individual users, particularly in sensitive contexts like financial institutions, leading to generic responses that may be disconcerting and less useful.
Innovation Solution
A personalized AI/ML chatbot system comprising a central server and user device, which includes a server and device processor, memory, and authentication engines, allows access to user-specific financial records to create a personalized profile, authenticate users, and recommend tailored financial services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generic AI/ML algorithms are used for automated chatbots, then the system can handle a large variety of users, but the responses become impersonal and less useful for individual users
Solution Approach 1:
The system performs preliminary actions by collecting user data, creating user profiles, and training personalized AI/ML models before the user interacts with the chatbot. This advance preparation enables the chatbot to provide personalized responses from the first interaction, resolving the contradiction between handling various users and providing personalized service.
Solution Approach 2:
The system applies local quality by creating customized AI/ML models for each individual user based on their specific data and preferences, rather than using a single generic model for all users. This allows the chatbot to adapt its responses to match each user's unique characteristics while still maintaining the ability to serve multiple users.
2Ease of operation
If personalized user data is incorporated into the chatbot, then the responses become more relevant to individual users, but the system complexity increases
Solution Approach 1:
The system segments the chatbot functionality into distinct components: a data collection module, a user profile creation module, a personalized model training module, and an inference module. This segmentation allows each component to handle specific tasks independently, managing system complexity while enabling personalization.
Solution Approach 2:
The system introduces intermediary components including user profiles that store personalized information and training datasets that bridge raw user data and the AI/ML model. These intermediaries organize and structure data flow, making the personalization process more manageable and less complex.
3Adaptability or versatility
If user-specific financial information is accessed to create personalized profiles, then the chatbot can provide tailored financial service recommendations, but data security and privacy requirements increase
Solution Approach 1:
The system extracts only the necessary personalization data from user financial information while leaving sensitive details secure. It creates simplified user profiles containing only the information needed for personalization (preferences, behavior patterns) without storing or processing complete financial records, thus enabling personalization while maintaining security.
Solution Approach 2:
The system creates a secure, isolated environment for processing sensitive financial data during model training, where data is handled in controlled conditions with appropriate security measures. This inert environment protects user privacy while still allowing the system to learn from financial information for personalization.
Data Source
AI summary
Apparatus and methods for an artificial intelligence/machine learning (“AI/ML”) chatbot personalized to a user are provided. The apparatus and methods may include a personalized AI/ML chatbot on a server receiving one or more financial records about a user. The personalized chatbot may analyze the one or more records. The personalized chatbot may determine one or more financial services to present to the user based on the financial records. The personalized chatbot may provide links to the recommended financial services. The personalized chatbot may be trained on responses from the user.