You're not aLoan

Inspiration

I have seen several family members fall victim to predatory payday loan vendors with outrageously high prices, I think that if there was more information about how payday loan vendors work and how they compare against traditional vendors these problems could be mitigated.

What it does

This app uses a users location (zip code) to find local payday vendors and finds what interest rates they offer, it then finds local banking institutions that offer loans as well. The app shows the start differences between these two sources and provides information about the dangers of payday loan vendors

How we built it

The app is written in python and is split into three parts: User interface, lending institutions, and payday vendors. The user interface is build primarily using Taipy, it then calls on the lending institutions, and payday vendors programs to gather data for the user. The lending institutions information is read from an online source that uses user location to find local banking institutions. The payday vendor data is gathered first by using the Google Places API to find local payday vendors, and then the websites of these payday vendors are read and analyzed to find what interest rates they offer.

Challenges we ran into

On the user interface side, the primary issue was working with an entirely new framework, however we greatly enjoyed working with Taipy and loved its intuitive nature.

On the lending institutions side, the biggest issue was finding a reliable and usable source of information about loans, but after a lot of searching we found one that would fit our project well.

There were plenty of problems gathering payday vendor information, but the biggest was getting specific interest rates from vendor websites. It took a lot of trial and error to create a method of reading percentages from HTML source that was efficient and effective.

Accomplishments that we're proud of

We are very proud of how quickly we were able to grasp Taipy, and how well we were able to read website information when gathering data on loan rates.

What we learned

We learned how to gather information from several unique sources on the internet, how to effectively distribute work among a team, how to pivot the teams direction based on a change of goals, and how to tailor a project based on challenges/restrictions.