WealthWise

MongoDB Stats
Modal Stats

Inspiration

Transform your financial insight with our WealthWise app, specifically designed for students! Ever felt puzzled about your finances, unsure whether you're broke or just temporarily cash-tight due to investments? Our app is your solution!

What it does

WealthWise lets you input your spending and investing amounts, and our AI provides personalized advice based on your credit, spending, and investing scores. It helps you make smart financial decisions and avoid those 'broke but not broke' moments.

How we built it

Next.js and Next UI: These tools provided the framework and UI components to build a responsive and visually appealing front-end for WealthWise. Next.js offered server-side rendering and static site generation to enhance performance and SEO, while Next UI delivered a comprehensive kit of design elements for a cohesive user interface.
Auth0: This service was integral for implementing robust authentication and authorization processes, ensuring that user data remains secure and access is controlled and monitored.
TypeScript: Adopting TypeScript enabled us to leverage strong typing and compile-time checks, reducing runtime errors and improving the maintainability of our codebase.
.tech Domain: Choosing a .tech domain highlighted our focus on technology and innovation, providing a relevant and brand-aligned internet presence.
Flask: Flask's simplicity and flexibility as a backend framework allowed us to quickly develop and deploy server-side applications, providing the necessary API endpoints for our front-end.
ModalAI for ML: The Large Language Model was a critical component, offering personalized financial insights by analyzing user data. It enabled the core functionality that differentiates WealthWise in the market.
MongoDB: We used MongoDB for its scalability and flexibility in handling diverse data types, facilitating efficient data storage, retrieval, and management for user profiles and transaction data.
VerbWire: In order to ensure the privacy of our users we stored credit card information on the block chain, making sure that those details were securely uploaded.

Together, these technologies formed the backbone of WealthWise, enabling us to deliver a comprehensive, secure, and user-friendly financial insight app tailored for students.

Challenges we ran into

Modal Integration: One of the significant hurdles we encountered was the slow response time of our machine learning model. Optimizing an AI to deliver real-time, personalized financial advice without lag was crucial but challenging, given the complexity and depth of data analysis required.
Limited Documentation for Modal: Working with Modal presented its unique set of challenges, primarily due to the limited documentation available, and lack of prior experience with the tool. This required us to experiment and troubleshoot more than usual, which was time-consuming and at times, a test of patience.
Design Creation: Developing the user interface designs from scratch was difficult to say the least. Ensuring that the design was not only aesthetically pleasing but also user-friendly and intuitive required multiple iterations and constant feedback integration.
Card Hover Animation: Implementing the card hover animation was trickier than anticipated. Achieving the desired visual effect with smooth performance across all devices required meticulous CSS tweaking and testing.

Accomplishments that we're proud of

Mastering Next UI: We successfully harnessed the power of Next UI to create a sleek, intuitive, and responsive user interface. Our ability to leverage this framework allowed us to deliver a professional-quality front-end that stands out in terms of design and user experience.
Proficiency in TypeScript: Our team achieved a deep understanding and effective utilization of TypeScript in this project. The adoption of TypeScript significantly improved our code's reliability and maintainability, reducing bugs and enhancing development efficiency.
Advanced Implementation in Next.js: We effectively leveraged Next.js to optimize our application for performance and SEO, utilizing its server-side rendering and static generation features to enhance user experience and application speed.
LLM with Modal: We're particularly proud of integrating a language learning model (LLM) using Modal, which was a cornerstone in enabling our app to provide intelligent financial insights. Despite the challenges with limited Modal documentation, we successfully implemented this feature, enhancing the app's core functionality.
Predictive Analytics: Our team developed a robust system to make accurate predictions based on users' past transactions. Learning to analyze historical data and predict future financial trends was a significant achievement that added substantial value to WealthWise.
Fine-Tuning LLM: We didn't just implement an LLM; we fine-tuned it to suit our specific needs in the financial domain. This accomplishment allowed us to provide users with more accurate, contextually relevant advice, setting our application apart from generic financial tools.
Utilizing the Blockchain: Securely uploading credit card information into the block chain was something we thought would be a great addition to our project, and actually fulfilling that task was extremely rewarding. The blockchain has amazing security capabilities, and being able to use that in a project of this scale was fundamental towards creating a better finished product.

What we learned

Throughout the development of WealthWise, our team gained invaluable insights and deepened our understanding of how to leverage various technologies in a cohesive manner to build an effective solution. We learned the importance of integrating front-end and back-end technologies seamlessly, ensuring that each component contributes optimally to the user experience and app functionality. The use of TypeScript enhanced our code quality, making our application more robust and maintainable. We also developed a deeper appreciation for data privacy and security, understanding the critical role they play in user trust and regulatory compliance. Additionally, the process honed our skills in machine learning, emphasizing the need for precise models that can deliver personalized financial advice.

What's next for WealthWise

Building on our current foundation, the next steps for WealthWise involve expanding its features and refining its technology stack to offer more comprehensive services. Expanding past the front end we built during the limited time we plan to work on more pages, and improve our design to be more user friendly. We plan to enhance the AI model to provide even more personalized and predictive financial insights, leveraging user feedback and advanced analytics to fine-tune its accuracy. Improving our backend with more robust frameworks and considering microservices architecture for better scalability is also on our roadmap. Furthermore, we aim to integrate additional APIs and services to broaden the app's capabilities in financial tracking and advice. Community engagement and user feedback will play a pivotal role in shaping the future updates and features, ensuring WealthWise continually evolves to meet the needs of its users.