## Inspiration

The field of Insurtech is one that has long been overlooked by many. However, with the increase of economic loss annually, and the divide between household and insurance company, this field is becoming more important than ever. Studies show that the annual natural disaster caused economic loss tallies up to several hundred billion US dollars. Yet only a third of this economic loss has been insured. As many of this loss affects families and households, we felt strongly that, especially in a year like 2020, we need to make something that could streamline the disaster insurance process, as well as something that lets households know that they need disaster insurance. That's where Insurguard comes in.

## What it does

Our solution, Insurguard, is a Web Application that enables users to compare, contrast, and even apply for natural disaster insurance plans all in one simple and streamlined platform. In addition, Insurguard will notify users, recommending them to apply for disaster insurance, if their household is detected to be in harm's way.

Using a proprietary web scraping algorithm, Insurguard will present the user with a multitude of insurance plans for every natural disaster type imaginable. Users can compare and contrast plans until they find one that is best suited to them.. Once users find a favorable insurance plan, they can apply and pay for it right through the app. Additionally, Insurguard will constantly check if the user's household is within the path of a natural disaster. If it is, it will instantly send them a notification, advising them to apply for disaster insurance as soon as possible!

## How we built it

For our tech stack, we used Python for the backend, Flask servers for the middle, and Reactjs on the frontend. We used API calls and Web Scraping on the python backend to retrieve our information, and then we create API endpoints with our Flask server. We used HTTP Requests to allow these to process, and we called them with our Reactjs frontend. We used user information in the request body, and then processed and displayed the response.

## Challenges we ran into

We had a ton of issues in accordance with time. It was difficult to fit everything into 36 hours of work, especially for such a complex web application. Other than that, finding the website to scrape insurance quote information was also very difficult. We had difficulties finding a website directly linked to pricing natural disaster insurance, and hopefully, in the future, it will be much easier.

## Accomplishments that we're proud of

We found what it took to work as a team. Coming into this project, we were not familiar with each other at all. We were able to learn about each other's strengths and weaknesses and use them to our advantage. Everyone took on their own role, and it allowed our team to flourish with almost no problems at all working together.

## What we learned

We learned a lot about flask and python. It helped us in solidifying an understanding of our open API calls. Other than that, we became really well verses in Reactjs, and most importantly working as a team. Our teamwork skills have improved exponentially, and it is no doubt due to the fact that we were strangers before, and now we are great friends.

## What's next for Insurguard

Going forward, the team plans to implement an insurance quote system right into the app. Using satellite imaging, Insurguard will be able to use computer vision to estimate the financial loss. The estimate will then be directly sent to the user's insurance company, which after review, will directly compensate the user through the app itself.
## Inspiration

We were interested in disaster relief for those impacted by Hurricances like Dorian and Maria for people that don't know what areas are affected and for first responders that don't know what infrastructure works are damaged and can't deliver appropriate resources in time.

## What it does

This website shows the location of the nearest natural disasters.

## How we built it

We used Amazon API and Google Cloud and Maps API and Python and Java Script.

## Challenges we ran into

We have not been to a hackathon before so we weren't sure about how in-depth or general our problem should be. We started with an app that first responders can use during a natural disasters to input vitals

## Accomplishments that we're proud of

A website that can map the GPS locations of flood data that we are confident in and a uniquely trained model for urban flooding.

## What we learned

We learned about Google Cloud APIs, AWS S3 Visual Recognition Software and about how to operate in a hackathon.

## What's next for Crisis Apps
## Inspiration

As students, we realized that insurance is a topic that is often seen as unapproachable and difficult to learn about. Getting insurance and finding the right one for a certain situation can seem scary and hard to understand, so we wanted to build a platform where a user could punch in appropriate information and find the most suitable information for them.

## What it does

**1.** Glass Wings creates an equal platform where anyone can share their information about what kind of insurance they have bought or encountered based on the environmental factors of their property

**2.** Glass Wings can predict the type of insurance and cost of it a user can expect based on the property they are trying to buy.

**3.** Glass Wings uses real-time data from actual users, raising awareness about insurance for individuals while simultaneously calculating insurance quickly and easily.

## How we built it

We built this platform with Python Django, then utilised AWS in order to train our model to predict the right insurance based on our crowd-sourced data. Not only is this trustworthy because it is based on real-time user verified data, but an individual can get a sense of how much everyone else is paying so that they don't feel they are being ripped off by a company.

## Challenges we ran into

AWS's SageMaker and ML is not an easy topic to learn overnight. Using new technologies and a new concept was a huge learning curve, which made it a challenge for us to build the product we envisioned.

## Accomplishments that we're proud of

We are tackling real-life issues. Environment is a hot topic right now because more and more people are becoming aware about climate change and the circumstances we are living in, and I believe that we are hopping on the right trends and tackling the appropriate issues.

## What we learned

The team learned a lot about insurance. Especially as students in pure tech, we weren't too aware about the finance and insurance industry. We realized that these are real-life problems that everyone faces (we will too eventually!) so we understood that this is a problem that everyone should be more aware about.

Not only this, we got to learn a good amount of new technologies such as Django and also ML techniques with AWS.

## What's next for Glass Wings

Improve our ML model. Although we did train our set with some mock data, we would love to crowd source more data for more accurate and interesting information.