Wealth Bridge - FinTech Consumer Facing Apps

Founded By

Tejas Ramanujam, Nidhi Gelli, Akshara Akella, Shivank Singh

Inspiration

We were inspired to build Wealth Bridge off a simple idea - we wanted to make financial mentoring more available for all people, regardless of where they were in life. All our lives, we had seen what poor wealth management had done to people we knew - we took our shot at changing that. Driven by both our passions and our genuine excitement, we pursued our idea of a platform to do so, complete with a data creation suite and AI-integration.

What it Does

Our website takes the client's financial information and gives an analysis of the information, while also returning the data in a visual format for easy analysis. We take multiple pieces of data, ranging from a client's age, to their investments, to their liabilities. Through the collection of this data, we are able to assign each client specific scores in several different financially-adjacent fields, culminating in a final score that describes the client's current financial health.

How we built it

We built our website using streamlit (python), as well as libraries like matplotlib, numpy, and pandas. These libraries allowed us to pursue data processing in a simple format - even with our lack of experience in front-end creation, we were able to create a solid and well-made platform in a matter of hours.

Challenges we ran into

We ran into many challenges - first and foremost our inexperience with software creation as a whole. Through lots of discussion and crawling through documentation, and hours and hours of Youtube videos, we were able to absorb the tips of tricks of learning Streamlit. We also ran into challenges displaying the data within Streamlit. Although Streamlit is known for its data visualization methods, we ran into several difficulties simply formatting our data in ways that would be accepted by the system. In the end, I am very proud of how we managed to overcome our difficulties - and I'm certainly proud that I was able to learn so much from this event.

Accomplishments that we're proud of

Our team was able to efficiently implement Open AI API into our product in order to give variable guidance to our clients, which is essential to create a personalized financial advising platform. We also delved deep into the open-source Streamlit library integrated in Python. This allowed us to create engaging data visuals using progress charts, tables, and graphs. We were also able to use this to take in user information complete the calculations necessary.

What we learned

We learned a lot about software engineering processes during this event - in particular, we severely underestimated the time we would take for requirements engineering in the project. Our personal thoughts were that it would speed by quickly, hardly in notice - but we ended up spending most of our time perfecting the design for our product.

What's next for Wealth Bridge

Wealth Bridge has a lot of possibilities for expansion, especially in the personal finance sector. We would particularly like to be able to collect more data with better efficiency, allowing us to give more complex guidance. For example, the ability to collect spending habits and the personal concerns of our clients would enable us to create a more detailed budget. Furthermore - we could also work on incorporating advice from industry professionals into our platform. This would allow for a stronger base for our finance scores, as well as a richer overall product - there's just so much room to grow.