1. Why ML is Necessary for Need Analysis?

Unlike traditional rule-based systems, LINAT leverages ML to:

• Provide personalized life insurance recommendations based on user data.
• Continuously improve its suggestions by learning from user interactions.
• Adapt to changing financial behaviors and risk preferences.

2. Core ML Functionalities in LINAT

a) AI-Powered Need Analysis & Plan Matching

• ML models analyze user financial inputs, liabilities, and dependents to identify the best insurance plans.
• Dynamic User Need Matrix (ML-based approach) maps user responses to appropriate insurance solutions.
• Example: Two users with the same income but different spending habits receive different plan recommendations.

b) Premium Estimation & Affordability Scoring

• ML calculates optimal coverage amounts based on actuarial models and affordability scoring.
• Example: If a user earns ₹75,000 per month, ML suggests a premium range that fits their budget.

c) Personalized Insurance Plan Matching

• Uses Retrieval-Augmented Generation (RAG) to fetch contextually relevant policy details.
• Factors in historical user preferences to prioritize suitable plans.
• Example: If users in a similar profile prefer a specific plan, ML prioritizes that plan for new users.

d) Risk Profiling & Behavioral Analysis

• Natural Language Processing (NLP) + ML detects user intent (e.g., risk-averse vs. high-growth mindset).