Gene AI in LINAT

AI & Machine Learning in LINAT

Need Matrix

Achieving LINAT Functionalities Without Machine Learning

Data Flow & Processing in LINAT

Functional Requirement

1. Phase 1: Define System Architecture ◦ Identify core components: AI chatbot, knowledge base, UI, backend. ◦ Choose technology stack: ▪ AI: OpenAI API, GPT-based chatbot ▪ Backend: Python (FastAPI/Django), Node.js ▪ Frontend: React.js, Tailwind CSS ▪ Database: PostgreSQL, Firebase, Vector DB for knowledge retrieval ◦ Ensure security, compliance, and scalability.

2. Phase 2: AI-Powered Decision System

    1. Dividing AI Models for Need Analysis
    ▪ Categorize by benefit type (Family Protection, Retirement Planning, Wealth Creation, Child’s Education, etc.).
    ▪ Assign rule-based logic for each category.
   
    2. Implementing RAG-Based AI Chatbot
    ▪ Build a chatbot with retrieval-augmented generation (RAG) to combine AI and rule-based decision-making.
    ▪ Train on financial needs analysis data.
    ▪ Integrate external sources (insurance product databases, premium calculators, etc.).
   

3. Phase 3: Knowledge Base Development ◦ Structure the knowledge base with: ▪ Insurance policy details ▪ Financial planning principles ▪ Product comparisons ▪ FAQs and regulatory information ◦ Store information in a vector database for efficient retrieval. ◦ Connect to AI chatbot for dynamic responses.

4. Phase 4: User-End System Development

    1. UI/UX Design
    ▪ Interactive chatbot-driven interface.
    ▪ Forms for collecting user financial needs and risk appetite.
    ▪ Policy comparison tool for better decision-making.
   
    2. Session Retrieval System
    ▪ Implement login-based and session ID/email-based retrieval.
    ▪ Store user inputs for analysis and future recommendations.
   
    3. Automated Email & Policy Redirection
    ▪ Generate and send policy recommendations via email.
    ▪ Redirect users to policy purchase process.
   

5. Phase 5: Backend System Development

    1. Database and Analytics
    ▪ Store user sessions securely.
    ▪ Implement analytics to improve recommendations.
   
    2. AI Model Integration
    ▪ Connect chatbot and need analysis system to backend.
    ▪ Ensure real-time updates and model improvements.
   
    3. Compliance and Security
    ▪ Encrypt sensitive user data.
    ▪ Follow data protection regulations (e.g., GDPR, IRDAI guidelines).
   

Next Steps

1. Assign development tasks to the team.
2. Create project timelines and milestones.
3. Begin with AI model development and knowledge base setup.
4. Parallelly build the UI and backend components.
5. Test the system with sample user scenarios before launch. Core Components of the AI-Powered Life Insurance Need Analysis System (LINAT)
6. AI Chatbot (LINAT - Life Insurance Need Analysis Tool) ◦ AI-driven chatbot for user interaction and need analysis. ◦ Uses a hybrid approach: Rule-based + AI-powered (RAG model). ◦ Interprets user inputs, financial goals, and insurance needs. ◦ Refers to a knowledge base to recommend suitable plans. ◦ Provides options for policy comparisons and premium calculations. ◦ Routes users to policy issuance flow when they choose to buy.
7. Knowledge Base (Insurance Product & Financial Data Repository) ◦ Stores life insurance products, benefits, features, and calculations. ◦ Categorizes insurance needs: Wealth creation, retirement, child education, health, protection, etc. ◦ Supports RAG-based retrieval to provide accurate and relevant policy recommendations. ◦ Includes dynamic product matrices to update insurance plans periodically. Core Functions of the Knowledge Base: ◦ Stores Life Insurance Products – Features, benefits, exclusions, and premium details. ◦ Categories Insurance Needs – Wealth creation, retirement, child education, health, protection, etc. ◦ Uses RAG-Based Retrieval – AI retrieves the most relevant plans based on user input. ◦ Dynamic Product Matrices – Allows periodic updates of new policies, premium changes, and regulatory modifications. ◦ Supports API Integrations – Fetches real-time insurance plans from insurers and financial data sources.
8. External API Integrations for Market Plan Retrieval ◦ To ensure the knowledge base remains updated with the latest life insurance plans, we integrate with third-party APIs and open data sources. These APIs fetch: ▪ Latest Insurance Plans & Riders (Term, ULIP, Endowment, Annuities, etc.) ▪ Premium Rates & Quotations (Based on age, sum assured, tenure, etc.) ▪ Regulatory Compliance Updates (IRDAI guidelines, policy mandates) ▪ Competitive Market Comparison (Fetch plans from different insurers)

Phase 1: Define System Architecture

• ◦ Identify core components: AI chatbot, knowledge base, UI, backend.
• ◦ Choose technology stack:
  • ▪ AI: OpenAI API, GPT-based chatbot
  • ▪ Backend: Python (FastAPI/Django), Node.js
  • ▪ Frontend: React.js, Tailwind CSS
  • ▪ Database: PostgreSQL, Firebase, Vector DB for knowledge retrieval
• ◦ Ensure security, compliance, and scalability.

Phase 2: AI-Powered Decision System

1. Dividing AI Models for Need Analysis

• ▪ Categorize by benefit type (Family Protection, Retirement Planning, Wealth Creation, Child’s Education, etc.).
• ▪ Assign rule-based logic for each category.

1. Implementing RAG-Based AI Chatbot

• ▪ Build chatbot with retrieval-augmented generation (RAG) to combine AI and rule-based decision-making.
• ▪ Train on financial needs analysis data.
• ▪ Integrate external sources (insurance product databases, premium calculators, etc.).

Phase 3: Knowledge Base Development

• ◦ Structure the knowledge base with:
  • ▪ Insurance policy details
  • ▪ Financial planning principles
  • ▪ Product comparisons
  • ▪ FAQs and regulatory information
• ◦ Store information in a vector database for efficient retrieval.
• ◦ Connect to AI chatbot for dynamic responses

Phase 4: User-End System Development

• UI/UX Design

  ▪ Interactive chatbot-driven interface.

  ▪ Forms for collecting user financial needs and risk appetite.

  ▪ Policy comparison tool for better decision-making.

• Session Retrieval System

  ▪ Implement login-based and session ID/email-based retrieval.

  ▪ Store user inputs for analysis and future recommendations.

• Automated Email & Policy Redirection

  ▪ Generate and send policy recommendations via email.

  ▪ Redirect users to policy purchase process.

Phase 5: Backend System Development

• Database and Analytics

  ▪ Store user sessions securely.

  ▪ Implement analytics to improve recommendations.

• AI Model Integration

  ▪ Connect chatbot and need analysis system to backend.

  ▪ Ensure real-time updates and model improvements.

• Compliance and Security

  ▪ Encrypt sensitive user data.

  ▪ Follow data protection regulations (e.g., GDPR, IRDAI guidelines).

1. Assign development tasks to the team.