Inspiration
You've heard of customized diet plans, customized workouts, even customized skincare. It's time we brought you a customized financial plan that makes sure financial literacy reaches everyone.
Above all, our focus is to inform. We aim to bring knowledge and empowerment to women, minorities, and communities that are left on the fringes of financial literacy. With a group that carries the perspectives of first generation immigrants, first generation low-income college students, and women of color, our focus naturally gravitated towards broadening the circle of knowledge around finance to communities that historically have not been exposed to this guidance. So, we expose our users to retirement accounts, considering the gender pay gap, saving habits, and stock trading.
The impact of our solution actually goes beyond just addressing the financial literacy gap. We, further, consider the longer life expectancy of women by helping them make smarter financial decisions throughout their life, like picking the right retirement plan and promoting a habit of saving. We, also, address the gender pay gap in our advice, since we want to provide a thorough plan for an individual.
An example of a user we pictured for our solution could be any mother from the Bronx's community of first generation South Asian immigrants. This user comes from a culture that historically has not provided financial literacy to a woman and lives in an environment where resources are sparse. Who is looking out for her as she navigates her financial identity? We hope $heMoney does.
What does $heMoney do
Our solution aims to provide guidance based on a comprehensive financial assessment of an individual using algorithmic and machine learning analysis. We are implementing our solution in the form of an App - $heMoney - that will provide the user with customized feedback.
The first step of using $heMoney is completing our financial assessment; this survey collects information about an individual's current situation and knowledge of their own financial identity. Based on their responses (which can include “I don’t know” to indicate a lack of knowledge), we produce a threefold personalized plan of suggestions.
The plan includes:
Investment Account Suggestions This section suggests what retirement savings account is best suited for the individual being analyzed. It chooses between a 401k or a Roth IRA. It, also, considers whether you are eligible for a HSA and suggests it accordingly. An explanation of the factors that determine the suggestion being made is provided too. Details of the formulas used are not explained obviously.
**Incentivized Savings” The app aims to sync the user’s monthly bills and compares it to the previous months. If the user is spending comparatively less, they get rewarded with a certain number of points which can be redeemed to get feminine products sourced from a charity or purchase stocks. This way we are incentivizing users to save money while also raising revenue for businesses.
Pay Gap Assessment Using machine learning, the app uses information like your region and position in a company to estimate what your pay should be. The model we used is mentioned below. Using the estimated pay, we determine if the pay you reported is indicating a gender pay gap.
Further, the user has the option to generate an AI generated certified report that outlines the data found indicating a gender pay gap and negotiates a pay raise. This option will be a paid service.
HOW TO Guide The final section of the plan covers information and materials for further knowledge. This section will be a paid service.
Overall, we expose our users to retirement accounts, considering the gender pay gap, saving habits, and stock trading.
How we built it
Our process included:
- Brainstorming We did individual brainstorming, deliberated about 15 ideas, and used the huge notepads provided to finalize two ideas that had us excited.
- Planning Picking $heMoney as our most impactful idea, we started doodling the layout and decided what inputs and outputs we want. Additionally, we deep dived into research to establish the formulas for our algorithms and the factors that we wanted to consider.
- Prototyping We used Figma to design a mockup of our app.
- Executing We used Swift to code our app and python to write our machine learning algorithm.
The aspects of entrepreneurship integrated in our solution are explained below in the challenges portion.
Formulas for Investment Account Suggestions Algorithms:
Roth IRA FV=(.7PV×(1+r)^n) Where: FV = Future Value of the Roth IRA account PV = initial investment (contributions) r = .06 n = Number of years the money is invested (Retirement - age) Post tax Health Care
401k FV=.7((PV×(1+r)^n)+M ) Where: FV = Future Value of the 401(k) account PV = initial investment (contributions) r = .06 n = Number of years the money is invested (Retirement - age) M = Total employer matching contributions over the investment period which is assumed Pre Tax
HSA Account FV=PV×(1+r)^n Where: FV = Future Value of the HSA account PV = Present Value or initial investment (contributions) r = Annual interest rate (expressed as a decimal) n = Number of years the money is invested
Resources:
Challenges we ran into
Some of the challenges we faced include:
Integrating Python algorithms in Swift This was something that felt tricky and we had to approach Jesse to ask for some guidance. Turns out, we felt intimidated too quickly, because it was quite easy.
Navigating Figma It was daunting to utilize a software that none of us had used before, especially under time pressure. However, we divided work and allocated two group members to it to maximize time.
Monetizing our solution With a heavy social focus, our entrepreneurship focus needed fleshing out. We developed multiple avenues of monetary input through developing two paywalls, revenue by publishing the app on the Apple store, and using advertisements in the app. The two paywalls are for requesting the HOW TO Guide and for requesting the AI generated certified report addressing the pay gap.
Accomplishments that we're proud of
We’re extremely proud of everything we’ve accomplished.
From bonding as a group to learning new platforms, we overcame challenges and deliberated ideas together. We poured our heart and all of our effort into this solution. We’re so excited to share it.
What we learned
Three of the key things we learnt were:
- We learnt about Fidelity Investments’ work with HSA and mobile development by talking to Dylan.
- We began to learn Figma after talking to Jesse and experimenting.
- One of the biggest things we had to learn was using JSON and AWS for our machine learning model. John Davis really helped us explore that.
What's next for $heMoney
Our Ambitious Goals to help Ambitious Women:
Expand to different countries We could expand our impact by increasing our outreach. We would update the questions we ask and the data we use to make suggestions based on a different country's policies. This is admittedly a very ambitious goal but would make a great deal of impact by helping women in environments where they need more help.
HOW TO Guide We would want to flesh out our guide to be a valuable and enticing purchase. We would aim to work with a financial advisor to produce a personalized how to guide that details how to implement the suggestions that our plan outlines.
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