Inspiration
In today's fast-paced world, college students and recent grads often find themselves stepping into a new phase of life with high aspirations but limited knowledge about managing their finances. The transition from student life to the working world comes with a host of financial challenges, from paying off student loans to budgeting for everyday expenses and setting long-term financial goals. This inspired us to create FinGuard, an interactive platform that empowers young adults with the knowledge and tools they need to make informed financial decisions and pave their path to a secure and prosperous future.
What it does
FinGuard is a comprehensive platform tailored to guide recent college graduates and young professionals on their financial journey. Upon entering the platform, users can easily create an account, granting them access to our powerful financial tools. The centerpiece of FinGuard is our Loan Eligibility Predictor, which empowers users to gauge their eligibility for loans by inputting various factors like income, marital status, education, and loan terms. The tool provides an interactive graph displaying loan eligibility, ensuring that users can make informed decisions about their financial future. With FinGuard, we're on a mission to enhance financial awareness among students and recent graduates, to promote smart financial choices, and open the door to financial success.
How we built it
- UX/UI: Figma
- Front-end: React.js, CSS
- Back-end: Flask(using Python)
- ML model: Scikit learn, Pandas, Numpy, Matplotlib, Seaborn, and dataset using Kaggle
Challenges we ran into
- One of the greatest challenges we faced was integrating the backend with the machine learning model and the front end. Since all of us were experimenting with new technologies, we initially struggled to get a cohesive product together that was up to our expectations. But through many youtube videos, online tutorials, we were able to understand how the integration process works.
Accomplishments that we're proud of
- Collaborating together as a team
- Completing the project within the given time frame
- Being able to implement most of the technical features
- Creating a hi-fi design as well as a functioning application
- Experimenting with a machine learning model that has true real world application
What we learned
- Efficient time management and collaboration
- Prioritization
- Problem-solving abilities
- Prototyping and creating an MVP
- We also learned the importance of delegating tasks early and making sure that everyone understands exactly what needs to be done and what the mission and vision of the project is. Doing so allows for smooth operations and efficient workflow.
What's next for FinGuard
- We have a grand vision for FinGuard, not only in terms of technical potential but also entrepreneurial potential. We see FinGuard as being the go to for schools and colleges and other institutions. Not only will this allow them to distribute financial aid to students but also allow them to know exactly how they can pay it back. All while teaching them important financial lessons through our daily financial education tips. Technically, we want to expand our models to give personalized financial advice based on observing a users spending history and improving their overall financial strength. We also want to partner with banks and credit unions to make our ML model more accurate by using their wealth of credit score data to analyze more accurately who can qualify for what sort of loans.
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