Most banks spend thousands of dollars in an effort to attract new customers into their business. But even after a successful attainment, most banks don't really have a means of 'preventing' a customer from leaving.
What it does
Conservar provides banks with the opportunity of detecting customers who might potentially be on the verge of leaving them hence allowing them to do something to entice them into staying. The detection is based off of two categories: Transactional History Conservar processes each customer's data and provides an aggregate of their transactional history. Accounts with an extreme drop in activity or below a certain set threshold, are flagged as a potential defector. Characteristics Exploration Through machine learning, Conservar is able to relate a customer's current credit score to their location in order to provide banks with predections of their the customer is most likely to stay or not. Along with this, this tool can also be used by banks to target a certain customer audience.
How I built it
Challenges I ran into
Machine learning pretty much. This was our first time doing it and we really wanted to challenge ourselves and my oh my were we challenged..
Accomplishments that I'm proud of
Getting the machine learning portion working.
What I learned
Tensorflow, SKLearn and some data analysis methods
What's next for Conservar
Improve the machine learning algorithm by not only adding more attributes, but also collecting more data in general. The end goal is to have the platform even find different potential earning means for businesses and not just banks.