FRAUD ANALYTICS: A SURVEY ON BANK FRAUD AND FRAUD PREDICTION USING UNSUPERVISED LEARNING BASED APPROACH
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Abstract
Fraud in banks has been steadily growing over the past years and is a challenge to banks worldwide. The complexity involved in detection of such fraudulent activities further adds to the problem. A thorough examination of fraud and its possibilities is necessary to pinpoint and distinguish the few fraudulent cases within the vast volumes of banking data. In this paper we have discussed various scenarios in which fraud could take place and applied unsupervised learning approaches to detect fraudulent acts in areas such as credit cards, money laundering and financial statements. We have keenly analyzed various attributes which would be necessary in detection of culprits who may cause a loss to the banks/organizations. Our analysis assists in discovering anomalous behavior among peer groups to more consistently uncover frauds with lesser amount of false positives.
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