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A Novel Text Mining Approach to Securities and Financial Fraud Detection of Case Suspects
Sundar Krishnan, Narasimha Shashidhar, Cihan Varol, ABM Rezbaul Islam
Pages - 1 - 16     |    Revised - 30-06-2022     |    Published - 01-08-2022
Volume - 11   Issue - 1    |    Publication Date - August 2022  Table of Contents
Digital Forensic Analytics, Digital Forensics, Supervised Learning, Hybrid Learning, Unsupervised Learning, Insider Trading, Pump and Dump, Legal Analytics, Forensic Accounting, Financial Forensics, Legal Case Evidence, eDiscovery, Financial Fraud, Electronic Stored Information.
Securities or stock fraud is a type of financial fraud involving securities or asset markets that can result in criminal charges and jail time. Detecting securities fraud from a large volume of electronic evidence without automation, statistical methods, and analytics can be a mammoth exercise for investigation teams due to the ever-increasing volumes of electronic data as case evidence. In this study, the authors propose a machine learning and neural network-based approach consisting of various analytical sub-approaches and automation that can assist financial forensic investigators, legal teams, paralegals, digital forensic investigators, and auditors in financial fraud case investigations such as “insider trading fraud” and “pump and dump fraud”. This comprehensive approach can help reduce investigation time, cost and rework when identifying internal trading fraud and pump and dump fraud indicators.
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Mr. Sundar Krishnan
Department of Computer Science, Sam Houston State University, Huntsville, TX - United States of America
Mr. Narasimha Shashidhar
Department of Computer Science, Sam Houston State University, Huntsville, TX - United States of America
Mr. Cihan Varol
Department of Computer Science, Sam Houston State University, Huntsville, TX - United States of America
Mr. ABM Rezbaul Islam
Department of Computer Science, Sam Houston State University, Huntsville, TX - United States of America

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