1. Introduction LINAT, the Life Insurance Need Analysis Tool, can function effectively without the use of Machine Learning (ML) by leveraging a structured rule-based approach and prompt engineering. This document outlines how LINAT can achieve its intended functionalities through deterministic algorithms, decision trees, predefined rules, structured databases, and AI prompt training.

2. User Need Analysis and Personalization Instead of ML, LINAT can implement:

• Predefined decision trees to categorize users based on factors such as age, income, dependents, and financial goals.
• Scoring mechanisms to evaluate and match user responses with suitable insurance plans.
• Prompt engineering to dynamically guide users through structured responses.

Approach:

• Design hierarchical prompts that analyze user needs based on predefined categories.
• Use a multi-turn conversational approach, where each response refines the user’s requirement.

Example Prompt Structure:

• "Please provide your age, annual income, and financial goal (Wealth Creation, Family Protection, Retirement Planning, or Child’s Future Security)."
• If the user selects "Child’s Future Security," the chatbot follows up with:
  • "Are you looking for an education fund, marriage fund, or both?"
• The chatbot refines suggestions based on responses.

3. Intelligent Decision-Making LINAT can rely on:

• IF-THEN rule-based logic to guide users through insurance need analysis.
• Predefined profiling tables that store structured insurance needs based on user demographics and financial situations.
• Structured prompt engineering to simulate decision trees dynamically.

Example Prompt:

• "Based on your responses, you may need a term insurance plan with a coverage amount of ₹X lakh. Would you like to proceed with a detailed cost estimation?"

4. Natural Language Processing (NLP) for Chatbot Conversations Without ML-based NLP, LINAT can implement: