This thesis explores and models the relationships between offers of credit products,
credit scores, consumers' acceptance decisions and expected profits generated using
data that records actual choices made by customers and their monthly account status
after being accepted. Based on Keeney and Oliver's theoretical work, this thesis esti¬
mates the expected profits for the lender at the time of application, draws the iso-profit
curves and iso-preference curves, derives optimal policy decisions subject to various
constraints and compares the economic benefits after the segmentation analysis.
This thesis also addresses other research issues that have emerged during the explo¬
ration into profitability and acceptance. We use a Bivariate Sample Selection model to
test the existence of sample selection bias and found that acceptance inference may not
be necessary for our data. We compared the predictive performance of Support Vector
Machines (SVMs) vs. Logistic Regression (LR) on default data as well as on accep¬
tance data, without finding that SVMs outperform LR. We applied different Survival
Analysis models on two events of interest, default and paying back early. Our results
favoured semi-parametric PH-Cox models separately estimated for each hazard.