Inspiration
The inspiration for Spend Wise came from the need for a more personalized and accessible financial management tool. We recognized that many people struggle with managing their finances due to a lack of knowledge, motivation, or suitable tools. By integrating AI and focusing on user accessibility, we aimed to create an app that not only assists with financial management but also educates and empowers users to achieve their financial goals. We wanted to provide a comprehensive tool that caters to diverse needs, including those of people with dyslexia or those who prefer voice interaction over traditional text input.
What it does
Spend Wise is a mobile application dedicated to providing personalized financial management. It links to the user's bank account to track revenue and expenses, offering a comprehensive overview of their financial status. The app features AI-assisted financial chart explanations, goal setting, and personalized financial advice delivered through a friendly chatbot. Additionally, it aims to offers accessibility services like light/dark mode and features designed for people with dyslexia. Onboarding includes multiple-choice questions to understand user goals, and a mascot character is included to guide and interact with users throughout their financial journey.
How we built it
We built Spend Wise using a robust stack of technologies to ensure scalability and performance. For the frontend, we utilized Flutter and Dart, which allowed us to create a seamless and responsive user interface on Web and Android platforms. The development environment was set up in Visual Studio Code, providing an efficient and flexible coding experience. MongoDB was chosen as our database due to its flexibility in handling complex financial data structures and ease of scalability. This setup allowed us to mimic bank account transactions and track user revenue and expenses securely. The integration of these tools enabled us to build a comprehensive, user-friendly application with a strong foundation for future enhancements.
Challenges we ran into
One of the major challenges we faced was integrating the AI to provide accurate and personalized financial advice. Additionally, implementing a real time responsive expense tracker either through bank API or scraping was an initial goal that ended up being cut due to time and scope. Another challenge that would occur was creating a seamless and secure method for linking bank accounts, which required us to prioritize user data privacy and security.
Accomplishments that we're proud of
We are particularly proud of successfully implementing tailor made goal oriented on boarding that will be unique and an amazing experience for each individual user's goal. We are particularly proud of creating a successful MongoDB database and developing a fully functional Flutter app despite being new to this technology stack. Our team managed to learn and implement these tools effectively within a short timeframe, resulting in a seamless and robust application.
What we learned
Through this project, we learned a great deal about the complexities of financial management and the importance of personalized advice. We gained insights into AI development, particularly in training models to interpret and respond to user data accurately. We also learned the significance of user-centered design, especially when it comes to accessibility. Moreover, the project taught us the value of teamwork and iterative development, as continuous feedback and collaboration were essential in refining our app.
What's next for Spend Wise
The next steps for Spend Wise include implementing the speech-to-text and text-to-speech features to enhance accessibility further as the true uniqueness of Spend Wise is to be user friendly and not non intimidating for those wanting to get into personal finance. We also want to implement a tailor made AI to truly accommodate the goal oriented setting we made for each user. We would also want to enhance it's capabilities with more advanced financial planning tools and predictive analytics through accurate chart reading. We plan to integrate real bank APIs to replace the hypothetical database and provide a more realistic experience.
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