fiNavi

Inspiration

Need for Accessibility

• Financial advisors typically seek high-income clients, widening economic disparity between wealth classes
• The growing digitalization of banking services excludes key groups such as seniors

Need for Education

• Rates of financial literacy amongst adults in developing countries are extremely low
• Financial literacy is not prevalent within school curricula, resulting in uneducated youths

What it does

Teaches --> provides a beginner-friendly platform for all levels of financial literacy to engage with and guides users with comprehensive financial explanations

Recommends --> points users to potential courses of action for improvements in a wide variety of goals such as budgeting

Reads Transaction History --> reads transaction history in a spreadsheet format to gain insight into purchasing patterns

How we built it

Backend --> wrote a Flask server with Vertex AI Gemini API that takes in POST requests from the frontend, ultimately returning the AI-generated response

Frontend --> built a react app with a display component and an input component that sends POST requests to the backend

Challenges we ran into

User Privacy

We wanted to strike a balance between the personalization of the AI and user privacy as it raises ethical concerns when a bot possesses unlimited control over transaction history. To mitigate this, we decided on the implementation of an opt-in/opt-out system that would give the user a choice based on their preferences.

Accomplishments that we're proud of

We were extremely proud of being able to produce a finished product despite neither of us having a proper tech background. Additionally, our flexibility throughout our learning journey demonstrated an unmatched level of learning and resilience that we hope to carry onto future hackathons. :)

What we learned

Putting together a formal project with our programming knowledge is relatively new ground for us, making fiNavi a huge leap. We were able to learn a lot about formal project organization as well as navigating Google Developer tools.

What's next for fiNavi

Integration into Banking Applications --> adoption by existing banking services seamlessly with current user interfaces

Deeper Model Training --> train the model with a combination of new and existing datasets to help improve its knowledge base and recommendations

Quality Improvements --> implement speech to text to improve accessibility for senior users and an insight tab to provide overall investment portfolio insights

Built With

• css
• flask
• html
• javascript
• openpyxl
• python
• react
• vertex