CONCLUSIONS
In
this paper, we have proposed an application of HMM in credit card fraud
detection. The different steps in credit card transaction processing are
represented as the underlying stochastic process of an HMM. We have used the
ranges of transaction amount as the observation symbols, whereas the types of
item have been considered to be states of the HMM. We have suggested a method
for finding the spending profile of cardholders, as well as application of this
knowledge in deciding the value of observation symbols and initial estimate of
the model parameters. It has also been explained how the HMM can detect whether
an incoming transaction is fraudulent or not. Experimental results show the
performance and effectiveness of our system and demonstrate the usefulness of
learning the spending profile of the cardholders. Comparative studies reveal
that the Accuracy of the system is close to 80 percent over a wide variation in
the input data. The system is also scalable for handling large volumes of
transactions.
SCOPE
FOR FUTURE ENHANCEMENTS
The project has covered almost all the requirements. Further
requirements and improvements can easily be done since the coding is mainly
structured or modular in nature. Improvements can be appended by changing the
existing modules or adding new modules. One important development that can be
added to the project in future is file level backup, which is presently done
for folder level.
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