PolicyGen
PolicyGen is an AI-powered platform that simplifies insurance access, offering personalized recommendations and 24/7 guidance for medical and life insurance through a user-friendly interface.
Created on 2nd October 2024
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PolicyGen
PolicyGen is an AI-powered platform that simplifies insurance access, offering personalized recommendations and 24/7 guidance for medical and life insurance through a user-friendly interface.
Describe your project
PolicyGen, our AI-Powered insurance guidance platform revolutionizes insurance accessibility through a user-friendly interface. The solution features an intelligent questionnaire system for medical and life insurance, driven by a Gemini AI model fine-tuned on a demo insurance database. Users receive personalized recommendations and can ask follow-up questions via a conversational AI feature. We prioritize education by clearly explaining insurance terms and policy details, ensuring users understand their options. Our focus on these core features allows us to deliver a high-quality MVP that addresses key challenges in insurance selection and understanding.
However, several elements remain out of scope for our current solution. We do not integrate with real insurance systems or handle actual policy purchases and transactions. Legal advice beyond basic explanations is excluded, and our platform currently focuses only on medical and life insurance, with other types slated for future development. Additionally, multi-language support, mobile app development, advanced user account management, and integration with external financial planning tools are beyond our present scope. This targeted approach enables us to perfect our core functionalities within the hackathon's constraints.
Looking ahead, our platform has significant potential for expansion. Future opportunities include broadening coverage to additional insurance types, integrating with real insurance databases for live quotes, and partnering with providers for seamless policy applications. We also plan to implement advanced machine learning for improved recommendations and develop features to assist users throughout the claims process, simplifying this traditionally complex procedure. By focusing on user needs and leveraging AI to simplify every aspect of insurance, our goal is to significantly enhance insurance understanding and accessibility, leading to better financial decisions and improved insurance utilization.
Challenges we ran into
Data Availability: One of the most significant challenges was the lack of access to real, usable insurance databases. We aimed to fine-tune our AI model with actual policies that users could potentially purchase, which would have enhanced the accuracy and relevance of our recommendations. However, finding a comprehensive and accessible database proved difficult. To overcome this, we utilized a demo database while actively researching publicly available insurance policy information. We focused on understanding the common features and terms used in medical and life insurance, drawing from various online resources and insurance company websites. This research helped us simulate realistic scenarios, enabling us to create a more informed recommendation system. After receiving feedback from our mentor, we improved the demo dateset by extracting important parameters from insurance policy wording files. This process enabled us to create a more realistic dataset, which further helped in refining our model and enhancing its responses.
Personalized Questions for Different Insurance Types: Another challenge was developing tailored questionnaires for the distinct needs of medical and life insurance. Each type of insurance requires different information and considerations, and creating relevant questions for both posed a difficulty. To address this, we conducted research to identify the key factors and typical questions associated with each insurance type. We then created specific question sets designed for medical and life insurance, ensuring that users received targeted inquiries relevant to their needs. Additionally, we collaborated as a team to iterate on these questions, validating their effectiveness through user feedback. This approach not only streamlined the questionnaire process but also enhanced user engagement by making the experience more relevant to their specific insurance interests.
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