This notebook demonstrates the application of our relational learning algorithm to predict if a customer of a bank will default on his loan. We train the predictor on customer metadata, transaction history, as well as other successful and unsuccessful loans.
Author: Dr. Johannes King, Dr. Patrick Urbanke
This notebook features a textbook example of predictive analytics applied to the financial sector. A loan is the lending of money to companies or individuals. Banks grant loans in exchange for the promise of repayment. Loan default is defined as the failure to meet this legal obligation, for example, when a home buyer fails to make a mortgage payment. A bank needs to estimate the risk it carries when granting loans to potentially non-performing customers.
FastProp is our unique take on propositionalization. In real-world benchmarks against popular propositionalization libraries, we find FastProp is between 34x to 179x faster than the current state of the art.
ZEISS is working on delivering highly reliable machines and support processes. GetML allows building new predictive maintenance applications in a fraction of the time.