3.1. AI Chatbot (LINAT - Life Insurance Need Analysis Tool):

• 3.1.1 User Interaction:
  • The chatbot must be able to engage users in natural language conversations to gather information about their life insurance needs.
  • The chatbot should support both free-text input and structured questions.
• 3.1.2 Need Analysis:
  • The chatbot must be able to accurately assess user financial goals, such as family protection, retirement planning, wealth creation, and child education.
  • The chatbot must identify user risk tolerance and financial constraints.
• 3.1.3 Plan Recommendation:
  • The chatbot must be able to provide personalized insurance recommendations based on user needs and data retrieved from the Knowledge Base and external APIs.
  • The chatbot must present recommended plans in a clear, concise, and easy-to-understand format.
  • The system shall implement retrieval-augmented generation (RAG) for the chatbot using vector database integration and OpenAI's natural language processing to improve upon knowledge base retrieval and ensure accurate responses.
• 3.1.4 Comparison and Calculation:
  • The chatbot must allow users to compare multiple insurance plans side-by-side.
  • The chatbot must be able to perform premium calculations and provide projections on returns and benefits.
• 3.1.5 Policy Issuance Routing:
  • The chatbot must be able to route users to the appropriate policy issuance platforms when they decide to purchase a plan.
• 3.1.6 Fraud Prevention:
  • The Chatbot shall analyze user inputs, and compare them against stored information.
  • The Chatbot shall cross-reference provided user information with external sources, and identify any points of conflict for manual review.

3.2. Knowledge Base (Insurance Product & Financial Data Repository):

• 3.2.1 Data Storage:
  • The Knowledge Base must store comprehensive information about life insurance products, including features, Risk Parameters, benefits, exclusions, and premium details.
  • The Knowledge Base shall make use of dynamic product matrices to update insurance plans, and allow periodic updates of new policies, premium changes, and regulatory modifications.
• 3.2.2 Categorization:
  • The Knowledge Base must categorize insurance needs into different categories, such as wealth creation, retirement, child education, and health protection.
• 3.2.3 Retrieval:
  • The Knowledge Base must support efficient retrieval of relevant insurance plans based on user input and AI-driven search algorithms.
• 3.2.4 API Integration:
  • The Knowledge Base must support API integrations to fetch real-time insurance plans from insurers, financial services, and aggregators.
• 3.2.5 Data Management:
  • The system shall maintain Dynamic Product Matrices, allowing periodic updates of new policies, premium changes, and regulatory modifications.

3.3. External API Integrations for Market Plan Retrieval:

• 3.3.1 API Connectivity:
  • The system must be able to connect to external APIs provided by insurance companies, financial services, and regulatory bodies.
• 3.3.2 Data Fetching:
  • The system must be able to fetch real-time data on insurance plans, premium rates, regulatory compliance updates, and market comparisons.
• 3.3.3 Data Processing:
  • The system must be able to process and format the data retrieved from external APIs for use in the Knowledge Base and AI Chatbot.
• 3.3.4 Open Source Data:
  • The system shall utilize open-source financial reports to improve upon insurance recommendations and provide additional projections.

3.4. User Interface (Frontend System for Web & Mobile):

• 3.4.1 Chatbot Interface:
  • The UI must provide an intuitive and engaging chatbot interface for user interactions.