PolicyAdvisor.ai: An Intelligent Insurance Policy Recommendation System

Abstract

PolicyAdvisor.AI uses Machine Learning and a trained Random Forest Classifier model to recommend insurance policies, by analyzing the inputs of a user such as age, salary, number of dependents, and type of insurance required, and determining the most suitable policies that match their financial Profile. It also ranks and filters policies based on estimated premium, sum assured, and coverage suitability derived from real-world policy datasets. Using a seamless integration between a React.js frontend and a Flask backend server via FastAPI, the system processes user inputs and returns the top 3 personalized policy recommendations, allowing users to quickly identify the most relevant insurance plans in an increasingly complex financial environment where they can make informed decisions more efficiently. The system eliminates the manual overhead of comparing numerous insurance policies on a massive scale by automating the recommendation and ranking process based on predictive capabilities.

Introduction

The text describes PolicyAdvisor.AI, an AI-powered insurance recommendation system designed to simplify and personalize insurance selection for users overwhelmed by numerous policies. Traditional methods—static listings, agent advice, or rule-based filters—fail to account for individual profiles, dependents, or long-term affordability, often resulting in poor choices or overpayment.

Key features of PolicyAdvisor.AI:

• Uses a Random Forest Classifier and a ranking algorithm to analyze user inputs (age, income, dependents, insurance type) and recommend the top 3 personalized policies.
• Integrates React frontend and Flask/FastAPI backend for seamless data exchange and scalable, interactive dashboards.
• Evaluates policies based on sum assured, estimated premiums, and coverage compatibility.
• Includes an Explainable AI (XAI) layer to clarify why each policy is recommended and a Buying Excellence Guidance module to help users complete secure purchases.

Advantages over traditional systems:

• Surpasses static or agent-driven recommendations by considering complex user profiles.
• Automates insurance advisory while maintaining transparency and real-time scoring.
• Handles multiple users efficiently with real-world policy datasets, secure request processing, and modular architecture.

In essence: PolicyAdvisor.AI provides a personalized, AI-driven, transparent, and user-friendly system to guide individuals in selecting the most suitable insurance policies while minimizing risk and improving decision-making efficiency.

Conclusion

The Policy Advisor system successfully automates the insurance recommendation process using intelligent data-driven techniques. It provides accurate, consistent, and personalized policy suggestions without human intervention. By integrating machine learning and explainable AI, the system improves transparency and user trust. Overall, it enhances efficiency, scalability, and decision-making compared to traditional advisory methods. The PolicyAdvisor.AI system can be further enhanced with advanced features to improve accuracy, usability, and real-world applicability. Future improvements include integrating real-time insurance data from official APIs to ensure up-to-date policy information. The system can also incorporate deep learning models and user behaviour analytics to provide more personalized and dynamic recommendations.

References

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Copyright

Copyright © 2026 Dr. D. Anusha, P. Ajay Kumar Varma, P. Thirumala Naveen , P. Dilip Chand, G. Kiran Bhargav. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.