  Non Performing Asset(NPA) has been in a serious attention by banks over the
past few years. NPA cause a huge loss to the banks hence it becomes an
extremely critical step in deciding which loans have the capabilities to become
an NPA and thereby deciding which loans to grant and which ones to reject. In
this paper which focuses on the exact crux of the matter we have proposed an
algorithm which is designed to handle the financial data very meticulously to
predict with a very high accuracy whether a particular loan would be classified
as a NPA in future or not. Instead of the conventional less accurate
classifiers used to decide which loans can turn to be NPA we build our own
classifier model using Entropy as the base. We have created an entropy based
classifier using Shannon Entropy. The classifier model categorizes our data
points in two categories accepted or rejected. We make use of local entropy and
global entropy to help us determine the output. The entropy classifier model is
then compared with existing classifiers used to predict NPAs thereby giving us
an idea about the performance.
