Abstract
The mounting dependence on computers and the Internet has made online fraud—an increasing concern for both users and law enforcement agencies. People and organizations can often be seen getting involved in a fraud fiasco, resulting in loss of money, property, legal rights, reputation. Detection of fraud is crucial as it deals with protecting oneself from getting duped. The work presented in this paper provides an empirical study and analysis of supervised learning techniques, i.e., logistic regression, nearest neighbors, linear and RBF SVM, decision trees, random forest and naïve Bayes on a benchmark credit card transaction dataset. The performance results have been evaluated and compared to identify the best predictive technique. The techniques have been used to detect whether a given transaction is fraudulent or not.
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Kumar, A., Gupta, G. (2018). Fraud Detection in Online Transactions Using Supervised Learning Techniques. In: Chakraverty, S., Goel, A., Misra, S. (eds) Towards Extensible and Adaptable Methods in Computing. Springer, Singapore. https://doi.org/10.1007/978-981-13-2348-5_23
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DOI: https://doi.org/10.1007/978-981-13-2348-5_23
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