Inspiration
We realized how often people leave money on the table by not using the best credit card for purchases. But that’s just the tip of the iceberg. Many users fall victim to scam websites, accidentally overspend, or simply don’t understand where their money is going each month. With so many credit card reward programs, sketchy merchants, and confusing spending patterns, there’s a real need for a tool that simplifies everything. That’s what inspired us to create FinGuard — a tool to help users shop smarter, stay safer online, and gain awareness of their spending habits to build a healthier financial future.
What it does
FinGuard is a browser extension and web dashboard that helps users spend smarter, stay safer, and build better financial habits.
Here’s what it does:
- Recommends the best credit card for each purchase based on the user's linked credit cards, including rewards, points, and active discounts
- Warns users about scam or unsafe websites using site reputation checks
- Tracks spending by category
- Provides a financial dashboard showing income, expenses, savings, and cashback earned
- Analyzes spending patterns to help users understand their habits and avoid overspending
How we built it
Browser Extension
- Frontend: The extension was built using React, CSS, and JavaScript. React was used to handle the dynamic UI components, ensuring smooth and responsive interactions.
- Card Recommendations: We fetch card-specific recommendations from MongoDB, where we store information about different credit cards, including rewards, points, and active discounts, to provide the best suggestions for each purchase.
- Site Scam Checker (Nudge): We used IPQS (IP Quality Score) for our Nudge feature, which checks the reputation of websites and alerts users if a site is unsafe. This feature warns users about scammy or potentially malicious websites by showing a “nudge” message when the site is flagged.
Web Dashboard (React + TailwindCSS + Node.js)
- Frontend: Built with React and TailwindCSS, offering a dynamic and responsive UI that allows users to interact with real-time financial data and insights efficiently. TailwindCSS was chosen for its utility-first approach to styling, allowing rapid customization and consistent design.
- Backend: Node.js was used for the backend, handling user authentication, data processing, and interactions with external APIs.
- RAG Model (Gemini): We integrated Gemini to use a RAG (Retrieval-Augmented Generation) model that analyzes user transaction data. This helps us provide personalized financial insights, answer user queries about spending, and offer suggestions based on transaction history.
- Insights Recharge: We used Insights Recharge to provide advanced data analysis, allowing users to gain deep insights into their spending patterns and make smarter financial decisions.
- Database: MongoDB was chosen for its flexibility and scalability in storing user profiles, transaction data, and card details.
Nudge
Our project leverages the behavioral economics principle of "nudging" to promote financial well-being. Recognizing that people often struggle with financial planning due to inertia or overwhelming choices, we designed a user-friendly application that subtly guides users toward healthier financial habits.
By implementing intuitive default options such as automatic enrollment in savings plans, our app encourages consistent saving without restricting individual choice. Additionally, we utilized framing techniques to present financial information positively, making users more comfortable with beneficial financial decisions.
We also included clear visual cues and personalized notifications to reinforce productive behaviors, like budgeting or timely bill payments. Through these subtle yet impactful adjustments, users naturally adopt better financial habits without feeling pressured or coerced.
Overall, our application demonstrates the effectiveness of gentle nudges in empowering users to achieve greater financial security and confidence.
Accomplishments that we're proud of
Seamless Integration of Multiple Components:
Successfully integrated the browser extension with the web dashboard, allowing users to manage their financial data, get card recommendations, and receive scam site alerts in one unified platform.Real-Time Credit Card Recommendations:
Built a robust recommendation engine that fetches the best credit card options for users based on their browsing activity, utilizing MongoDB for data storage and providing personalized suggestions that maximize rewards and discounts.Scam Detection and Nudge Alerts:
Implemented a Nudge feature using IPQS, which accurately flags unsafe websites and warns users in real-time, improving their online shopping security.Advanced Data Insights:
Integrated the Gemini RAG model to provide personalized insights and detailed analyses of user spending habits, helping users avoid overspending and make more informed financial decisions.Impact on Financial Habits:
Users have reported improved financial habits, such as smarter spending and increased savings, thanks to the card recommendations, spending limits, and insights into their financial behavior provided by FinGuard.
What we learned
Through building FinGuard, we gained valuable insights into real-time data analysis, which is crucial for providing personalized and timely credit card recommendations. We realized how important it is to balance user privacy with functionality, ensuring that sensitive financial data is stored securely without compromising on ease of use. The integration of the Gemini RAG model taught us how AI can effectively analyze transaction data to offer meaningful insights, helping users make informed financial decisions. We also discovered that a user-centric design is key to adoption and engagement—an intuitive interface allows users to easily navigate their financial journey. Additionally, we learned that continuous improvement is essential; user feedback is invaluable for refining features and making the platform better over time.
What's next for FinGuard
Looking ahead, we are focused on expanding the credit card database to offer a wider range of recommendations, making FinGuard even more valuable for users with different financial needs. We also plan to enhance the Gemini RAG model, improving the accuracy of financial insights and predictive capabilities, so users can better optimize their spending. The development of a mobile app is on the horizon, giving users access to their financial data and recommendations on-the-go. We also aim to integrate more payment platforms and credit card providers to broaden the scope of FinGuard’s recommendations. As we continue to listen to user feedback, we will add new features such as bill payment tracking and goal-based financial planning. Finally, we plan to expand globally, offering localized support for different countries, currencies, and financial systems, so more users worldwide can benefit from FinGuard.
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