The data flow in LINAT ensures a structured and secure handling of user inputs, processing them through various system layers to generate a need-based insurance recommendation. The key stages are as follows:

1. User Input Collection

• The user interacts with LINAT via the AI-powered chatbot.
• The chatbot collects responses based on the selected insurance need (e.g., Family Protection, Retirement, Key Man Insurance).
• The inputs include essential risk parameters such as age, income, dependents, liabilities, financial goals, and business-related factors (if applicable).

2. Data Storage & Structuring

• User responses are stored temporarily in session memory for real-time processing.
• The data is structured into the Need Matrix, categorizing each input based on predefined risk factors.
• If the user opts to save the session, the structured data is stored securely in the database with a unique session ID.

3. Risk Profiling & Analysis

• The system processes user inputs against predefined rules, weighted risk factors, and AI models.
• Each risk parameter is assigned a weight, and a Need Score is calculated.
• The system references the insurance product database to find the best-matching plan based on the Need Score.

4. Plan Recommendation & Reporting

• Based on the analysis, LINAT recommends the most suitable insurance plan.