| dc.description.abstract | Credit card banking has for a long time been one of the most profitable types of banking.
The largest cost for credit card companies is customers not paying their debt. Consequently,
to accurately model the risk a customer poses can provide large savings for credit
card companies.
This thesis aims to determine if it is possible to identify high risk credit card customers
within the first months of the customer relationship. Using a credit card dataset consisting
of customers first 18 months of data from between January 2013 and April 2017, machine
learning methods are used to develop classifiers that try to predict future delinquency.
Where previous work has incorporated many months of data to predict delinquency, we
use only data from the first and second month of the customer relationship to do the same.
Through a number of experiments, several models are developed. In addition to predicting
delinquency, the models are used to analyze behavior driving delinquency and to
model credit risk.
We find that the models can not accurately identify high risk customers based on only a
few months of data. The models developed reveal that the factors driving delinquency are
mostly intuitive. Using the developed models to predict the probability of delinquencies,
we find a strong correlation between the predicted probabilities and realized frequencies
of delinquency. | |
