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Published in:   Vol. 3 Issue 1 Date of Publication:   June 2014

An Analysis of Data Mining Applications for Fraud Detection in Securities Market


Page(s):   9- 15 ISSN:   2278-2397
DOI:   10.20894/IJDMTA. Publisher:   Integrated Intelligent Research (IIR)

In recent securities fraud broadly refers to deceptive practices in connection with the offering for sale of securities. There are many challenges involved in developing data mining applications for fraud detection in securities market, including: massive datasets, accuracy, privacy, performance measures and complexity. The impacts on the market and the training of regulators are other issues that need to be addressed. In this paper we present the results of a Comprehensive systematic literature review on data mining techniques for detecting fraudulent activities and market manipulation in securities market. We identify the best practices that are based on data mining methods for detecting known fraudulent patterns and discovering new predatory strategies. Furthermore, we highlight the challenges faced in the development and implementation of data mining systems for detecting market manipulation in securities market and we provide recommendation for future research works accordingly.