According to the 2017 survey, CareerBuilder, a leading job site, found some startling statistics related to debt, budgeting and making ends meet.
- 78 percent of U.S. workers live paycheck to paycheck to make ends meet
- Nearly one in 10 workers making $100,000+ live paycheck to paycheck
- More than 1 in 4 workers do not set aside any savings each month
- Nearly 3 in 4 workers say they are in debt today - more than half think they will always be
- More than half of minimum wage workers say they have to work more than one job to make ends meet
We wanted to build an Ai financial assistant that allows individuals to spend wisely and reach their monthly saving milestones.
What it does
SpendWise is an AI service that connects to a user's bank account, trains on their past transaction data and predicts expenses and savings potential for the future.
SpendWise also delivers proactive notifications based on its forecasts allowing the user to change their spending patterns and save money on a daily basis. It also suggests more economical alternatives for meals, transportation, entertainment and shopping requirements using Bing's local business search API.
How we built it
The service was built using the following technologies and frameworks:
- Azure ML Studio
- Microsoft cognitive services - Bing Local Business Search API
- Tensorflow 2.0
Challenges we ran into
- Finding the best model and parameters for time series prediction.
- Creating a modular and scalable web service architecture.
- Cleaning and conditioning the data for training the models.
- improving accuracy and reducing error.
Accomplishments that we're proud of
Building a relevant solution that can be impactful for over 230 million Americans allowing them to achieve financial wellbeing is an inspiring pursuit. With this bold ambition in mind, we were able to build and deploy an end to end functional application leveraging the tools and services provided by Azure and Microsoft cognitive services very quickly.
What's next for SpendWise
The following features are in our development pipeline:
- Add more intelligence around the user's surroundings to recommend the most economical alternatives for all requirements.
- Optimize the models to predict and suggest maximum monthly savings.
- Allow user to update notification settings so that he/she is notified an unwise purchase/transaction.
- Suggest investment options based on savings accumulated.
Here is the link to the online presentation.