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Expenditure Averages Report and User Profile based upon data from banking history and information
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Savings Plan to detail recommendations on how to accomplish financial goals
Inspiration
Based on various reports, 4 out of 5 adults within America were never able to learn about financial literacy in their lifetime so far. Especially with rising prices in our world, such as the housing market and ever-growing cost of attending post-secondary education institutions, it has never been more important to understand and utilize one’s funds effectively. In fact, 70% of millennials graduate with debt, creating a large disadvantage as soon as they leave school. By recognizing this problem, it seemed like a great challenge to tackle and hopefully eliminate. In a topic such as financial ability, real personal information is very sensitive and difficult to base our work upon, so thankfully by being able to utilize the TD DaVinci API, we were able to effectively test and tailor our vision to include as much information as possible about the user.
What it does
Thrift is able to sort through various banking information pieces associated with a user's account and utilize a greedy optimization algorithm to breakdown the user's spending habits, financial status, and financial goals to enhance their saving methods and obtain their goals. A user is simply required to log into their "CashBoard" and they are able to see their user profile, a detailed spending report of their account, and trends detailing their saving and spending habits. With this information, their financial goals are also displayed, with a real-time tracker to determine if they are on-track with their savings for various goals. Finally, there is a module that gives an in-depth look at the steps that the user should be taking to obtain these financial goals and effectively save their money.
All of these recommendations are strictly based off of the user's financial status, income level and goals, and utilize algorithms to determine how the user should plan to save their finances. This ensures that the process is specialized upon each user and can be properly used by real users.
How we built it
Our biggest focus for this application was ensuring that we could effectively utilize a user's data to provide the best advice and plans for a banking client. To create an intuitive platform for users of all ages, we created a web-application to ensure that it would be useful for various demographics, and cater to various devices for accessibility. We effectively utilized the Greedy Optimization Algorithm to help interpret the user's data and draw conclusions based upon it. For the analysis of the data, we utilized Python, while using Django and React to relay this information to the consumer. In order to do our initial drafts of our UI, we utilized Figma to effectively visualize our ideas and prepare for actual implementation of the system.
In order to test our solution effectively and understand the banking profile, we utilized the TD DaVinci API to simulate user data (including transaction history, financial history, income status, etc.) and extrapolate results based upon the data. This was definitely a lifesaver in order to ensure that our solution was scalable and useful to create reports and recommendations based on each situation.
Challenges we ran into
When accomplishing this large challenge, we definitely ran into many problems along the way, and had to constantly change our perspectives to solve what was in our path. Our first major problem was that we had a decreased timeframe to feasibly create this idea. With various ideas coming into the event, it took a prolonged period for us to determine what problem we truly wanted to solve, leading us to start our hack with only 20 hours remaining for the hackathon. This made us think critically and pushed us to quickly understand user needs, prototype, test, and iterate to create an optimal solution.
Another challenge we had was being able to understand and manipulate the data to be useful for deriving a user profile, understanding user trends, and optimizing their savings to achieve their goals. Through multiple iterations of parsing and interpreting the data, we were able to find the optimal path of achieving the information needed to give accurate recommendations for the user.
Accomplishments that we're proud of
We are extremely proud that we were able to complete this massive undertaking and create a solution for users (especially ourselves) that could have a real impact in the real-world. After talking to various financial institutions and professional services firms, we realized that this solution doesn't currently exist and could help clients in various age groups and demographics.
We are also proud of our determination to accomplish our goal that we set out for ourselves. Even though this caused a lack of sleep, we were able to effectively plan, design, develop, and execute a vision that could have positive impact!
What we learned
Working in a sector that we are not super familiar in has been a great experience of learning and questioning concepts and processes within the banking sector. We were also able to utilize algorithms for analysis and optimization on a new set of data, which showed us how we can use algorithms for a variety of tasks and challenges.
Finally, this opportunity pushed us to the limit with a restricted time constraint and showed us what we were capable of if we focus and effectively execute our vision. This has been a great learning experience all around, especially the opportunity to discuss our idea with sponsors and like-minded hackers!
What's next for Thrift
Building off of other hackers' and sponsors' criticism, we hope to further improve our software and be able to fine-tune it to create the optimal product for the user. Once we continue to develop and create a full end-to-end experience, we will be able to implement it into the real-world to help people with their financial goals. A large focus for us in the future is ensuring that there is complete privacy regarding real user data and financial information.
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