Describe your project

1. What is in-scope in the solution?
Simplification of Policy Selection: InsurAI provides real-time guidance, explaining policy terms in user-friendly language.
Form-Filling Automation: AI automatically completes insurance forms with user data, minimizing errors and reducing claim rejection rates.
Educational Support: It educates users by breaking down complex insurance and financial concepts, promoting financial literacy and trust.
Multi-channel Access: Users can interact with the AI agent via voice, video, or chat for maximum accessibility.

2. What is out of scope in the solution?
Manual Claim Processing: InsurAI focuses on automating form-filling, but the actual manual review of claims by insurance companies remains outside the solution’s scope.
Legal and Regulatory Advice: While InsurAI explains policies, it does not offer legal or regulatory advice. Users are encouraged to consult a professional for specific legal concerns.
Underwriting or Risk Assessment: The platform does not perform underwriting or evaluate risk for policies but can assist in providing recommendations based on pre-defined criteria.

3. What are the future opportunities for this solution?
Expansion into Other Financial Services: InsurAI can be extended to other areas like loan applications or retirement planning, simplifying complex processes.
Integration with Blockchain: Future iterations could incorporate blockchain technology for more secure and transparent insurance claims and document handling.
Advanced Analytics: Implementing predictive analytics could allow InsurAI to proactively suggest better insurance coverage based on user data and trends.
Customization for Global Markets: Expanding InsurAI to serve different languages and regional insurance regulations could allow it to serve a global customer base.

Challenges we ran into

One of the primary challenges in building our project, was efficiently using APIs to simplify complex insurance terms and processes. Initially, integrating external APIs for parsing and simplifying policy terms led to some issues with response times and accuracy.

Specific Issues:
API Response Delays: Using APIs to fetch and process policy details resulted in slower response times, which affected the real-time experience we aimed to provide.
Data Mapping Accuracy: The data retrieved from the APIs sometimes did not map accurately to user-friendly explanations, leading to inconsistencies in the information provided to users.

How We Overcame It:
Optimizing API Calls: To resolve the response delay, we implemented batching for API requests and pre-fetched commonly queried policy data. This reduced wait times and improved the overall speed of the platform.
Custom Layer for Data Simplification: Instead of relying solely on the API outputs, we built a custom logic layer that processes API data, ensuring it’s simplified and mapped correctly before delivering it to users.