  Predicting the Credit Defaulter is a perilous task of Financial Industries
like Banks. Ascertaining non-payer before giving loan is a significant and
conflict-ridden task of the Banker. Classification techniques are the better
choice for predictive analysis like finding the claimant, whether he/she is an
unpretentious customer or a cheat. Defining the outstanding classifier is a
risky assignment for any industrialist like a banker. This allow computer
science researchers to drill down efficient research works through evaluating
different classifiers and finding out the best classifier for such predictive
problems. This research work investigates the productivity of LADTree
Classifier and REPTree Classifier for the credit risk prediction and compares
their fitness through various measures. German credit dataset has been taken
and used to predict the credit risk with a help of open source machine learning
tool.
