Describe your project

The goal of the PolicyGenie project is to develop a humanoid agent driven by AI that will completely change the way consumers engage with insurance marketplaces like PolicyBazaar. The service fosters trust through transparent communication while making the process of choosing and acquiring insurance coverage easier, helping users fill out forms and comprehend their claims.
1.In-Scope:
Automated Form-Filling: By utilizing Named Entity Recognition (NER), the AI will automatically fill out insurance forms by extracting pertinent information from user interactions. This process minimizes human mistake and saves consumers time.

Assistance with Claims: PolicyGenie will assist users with the claims procedure by pre-validating data, making sure users provide accurate information in order to increase the acceptance rate of their claims.

User Education: To promote openness and trust, the agent will function as a virtual advisor, teaching users about different policy kinds, specifics of the coverage, and the filing process for claims.
2.Out-Scope:
Underwriting Decisions: Insurance providers retain authority over the underwriting and policy approval procedures, and PolicyGenie will not be involved in them.

Legal or Financial Advice: No particular legal or financial advice will be given by the solution. It will instruct users on insurance concepts, but in complex instances, it will not take the place of professional assistance.
3. Future Opportunities:
Advanced Claims Processing: By linking with insurers' systems, PolicyGenie versions in the future could manage real-time claim processing, increasing efficiency and decreasing user wait times.
AI-Powered Fraud Detection: PolicyGenie could assist insurers by flagging suspicious activities and potential fraud, enhancing the security and integrity of the insurance ecosystem.

Challenges we ran into

1. Natural Language Understanding (NLU) for Complex Queries
   A primary obstacle is guaranteeing that PolicyGenie comprehends user inquiries concerning intricate insurance terminologies, plans, and claims. The system must correctly understand the purpose of users whose inquiries may be phrased in a variety of ways.
   Solution: To overcome this, we will focus on fine-tuning the underlying NLP model using domain-specific data from the insurance sector. This will involve training the model with diverse sets of user queries and real-world insurance data to improve the system’s ability to comprehend and respond to nuanced questions.
2. Accurate Information Extraction for Form-Filling
   The success of automated form-filling relies on the precise extraction of user data, such as names, addresses, and policy details. Incorrect extraction of this information can lead to errors in forms, causing delays in processing claims or policy applications.
   Solution: Implement a robust Named Entity Recognition (NER) model to extract relevant information from user interactions. Regular testing with real-life data and integrating validation mechanisms will ensure the data extracted is accurate and contextually appropriate.
3. Handling Ambiguous User Input
   Users may sometimes provide ambiguous or incomplete information, leading to challenges in determining their exact needs or preferences. This could result in incorrect policy recommendations or form submissions.
   Solution: By incorporating a dialogue management system that can ask follow-up questions for clarification, PolicyGenie will ensure it gathers all the necessary details before offering advice or completing forms. Continuous monitoring of user feedback will also help in refining this process.