Deciding upon insurance plans can be a daunting task, especially on a limited budget. As the number of items you wish to insure grows, deciding which insurance policies to purchase becomes more complicated. This problem is an NP-complete problem, and can be solved somewhat efficiently using dynamic programming. By combining computing power with human creativity, we can help users make optimal decisions that drastically impact their daily lives.

What it does

The user can select a list of items they want to insure, including an importance rating and cost. The user then provides their total budget to spend on insurance policies. The application than utilizes the knapsack algorithm to display the optimal combination of items you can insure with your given budget. Making the ultimate decision on what insurance plans are most important can be a difficult task, and thus our application supports real-time chat and feedback from insurance agents. An agent can sign in to review and adjust the importance ratings of your insurance items, live chatting with the user in the process.

How we built it

Our front-end is built using Angular and the data is stored in the Firebase real-time database to provide instantaneous updates to users. We've laid out our database to make the interaction between the user and the insurance agents as seamlessly as possible.

Challenges we ran into

We have a diverse set of skills among our team. There is always a time when a person is stuck on a particular area, let it be front-end rendering, back-end logic or database queries. Luckily, Google is always there for us, and our team members compliment one another. Team members who initially decided to work on different parts of the project were able to come together to help each other work through problems. For example, when we ran into a problem regarding the speed of the optimization algorithm we were using, we were able to use a combination of ideas to come up with an efficient yet high accuracy solution

Accomplishments that we're proud of

Every bit of our project.

What we learned

We are more comfortable with the technology used and was able to apply classroom knowledge into solving real life problems.

What's next for AlgoFinance

Not sure, but we can start with taking a nap.

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