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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
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KEYWORDS
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.
ABSTRACT
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.
Angelov, D. (2020). Top2Vec: Distributed Representations of Topics. Retrieved from https://arxiv.org/abs/2008.09470.
Aroussi, R. (n.d.). yfinance PyPI. Retrieved February 4, 2022, from https://pypi.org/project/yfinance/.
Coucke, A., Saade, A., Ball, A., Bluche, T., Caulier, A., Leroy, D., … Dureau, J. (2018). Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces. Retrieved from https://arxiv.org/abs/1805.10190v3.
Detecting Financial Statement Fraud. (n.d.). Retrieved January 23, 2022, from https://www.investopedia.com/articles/financial-theory/11/detecting-financial-fraud.asp.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, 1, 4171–4186. Retrieved from https://arxiv.org/abs/1810.04805v2.
Dorrell, D., & Gadawski, G. (2012). Financial forensics body of knowledge. John Wiley & Sons, Inc. Retrieved from https://books.google.com/books?hl=en&lr=&id=NFlP-vBIHXAC&oi=fnd&pg=PT10&dq=Financial+Forensics+Body+of+Knowledge+-+Darrell+D.+Dorrell,+Gregory+A.+Gadawski&ots=UyL_LdAsPu&sig=t6w1K9qlumEkzrDpDUqT2bv8Erc.
EDRM, D. (n.d.). Processing Guide. Retrieved from http://www.edrm.net/frameworks-and-standards/edrm-model/processing/.
Forensic Audit vs. Internal Audit: Differences in Accounting. (n.d.). Retrieved January 31, 2022, from https://www.eidebailly.com/insights/articles/2019/3/forensic-audit-vs-internal-audit.
Fritz, F. (n.d.). The Costs Of E-Discovery And What May be Recoverable Under 28 U.S.C. § 1920. Retrieved February 15, 2022, from https://www.jdsupra.com/legalnews/the-costs-of-e-discovery-and-what-may-36639/.
GitHub. (n.d.). nlu-benchmark. Retrieved February 5, 2022, from https://github.com/wenjingu/nlu-benchmark.
Hancox, S. J., & Dinapoli, T. P. (n.d.). Red Flags for Fraud, State of New York Office of the State Comptroller.
Insider trading - Wikipedia. (n.d.). Retrieved January 29, 2022, from https://en.wikipedia.org/wiki/Insider_trading.
Insider Trading FAQ Part 1. (n.d.). Retrieved January 31, 2022, from https://prisonprofessors.com/insider-trading-faq-part-1/.
Islam, S. R., Khaled Ghafoor, S., & Eberle, W. (2019). Mining Illegal Insider Trading of Stocks: A Proactive Approach. Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, 1397–1406. https://doi.org/10.1109/BIGDATA.2018.8622303.
Jiang, J., Chen, J., Gu, T., Choo, K. K. R., Liu, C., Yu, M., … Mohapatra, P. (2019). Anomaly Detection with Graph Convolutional Networks for Insider Threat and Fraud Detection. Proceedings - IEEE Military Communications Conference MILCOM, 2019-Novem. https://doi.org/10.1109/MILCOM47813.2019.9020760.
Krishnan, S. (n.d.). Project. GitHub. Retrieved May 6, 2022, from https://github.com/kshsus.
Krishnan, S., Shashidhar, N., Varol, C., & Islam, A. R. (2022). Sentiment Analysis of Case Suspects in Digital Forensics and Legal Analytics. International Journal of Security, 13(1). Retrieved from https://www.cscjournals.org/journals/IJS/issues-archive.php.
Krishnan, S., Shashidhar, N., Varol, C., & Rezbaul Islam, A. (2021). Evidence Data Preprocessing for Forensic and Legal Analytics. International Journal of Computational Linguistics (IJCL), 12(2), 24–34. Retrieved from https://www.cscjournals.org/library/manuscriptinfo.php?mc=IJCL-122.
Lauar, F., & Arbex Valle, C. (2020). Detecting and Predicting Evidences of Insider Trading in the Brazilian Market. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12461 LNAI, 241–256. https://doi.org/10.1007/978-3-030-67670-4_15.
Lebichot, B., Borgne, Y.-A. Le, He-Guelton, L., Oblé, F., & Bontempi, G. (2019). Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection. Springer, 78–88. https://doi.org/10.1007/978-3-030-16841-4_8.
Li, T., Shin, D., & Wang, B. (2021). Cryptocurrency Pump-and-Dump Schemes. SSRN Electronic Journal. https://doi.org/10.2139/SSRN.3267041.
Liu, R., Mai, F., Shan, Z., & Wu, Y. (2020). Predicting shareholder litigation on insider trading from financial text: An interpretable deep learning approach. Information & Management, 57(8), 103387. https://doi.org/10.1016/J.IM.2020.103387.
Nam, D. (2020). Modelling Stock Market Manipulation in Online Forums. Retrieved February 23, 2022, from https://qspace.library.queensu.ca/handle/1974/28239?show=full.
NLP vs. NLU vs. NLG: the differences between three natural language processing concepts. (n.d.). Retrieved February 15, 2022, from https://www.ibm.com/blogs/watson/2020/11/nlp-vs-nlu-vs-nlg-the-differences-between-three-natural-language-processing-concepts/.
NLP vs. NLU: What’s the Difference and Why Does it Matter? (n.d.). Retrieved February 15, 2022, from https://rasa.com/blog/nlp-vs-nlu-whats-the-difference/.
NLU-benchmark/2017-06-custom-intent-engines at master • sonos/nlu-benchmark. (n.d.). Retrieved February 5, 2022, from https://github.com/sonos/nlu-benchmark/tree/master/2017-06-custom-intent-engines.
Officers, Directors and 10 percent Shareholders | SEC.gov. (n.d.). Retrieved January 31, 2022, from https://www.sec.gov/smallbusiness/goingpublic/officersanddirectors.
Open source conversational AI. (n.d.). Retrieved February 6, 2022, from https://rasa.com/.
Pump & Dump Schemes - Securities Fraud Attorneys. (n.d.). Retrieved January 31, 2022, from https://www.criminallawyergroup.com/practice-areas/securities-and-commodities-fraud/pump-dump-schemes/.
Pump and dump - Wikipedia. (n.d.). Retrieved January 29, 2022, from https://en.wikipedia.org/wiki/Pump_and_dump.
Pump and dump Schemes. (n.d.). Retrieved January 31, 2022, from https://www.sec.gov/rss/your_money/pump_and_dump.htm.
reddit.com: api documentation. (n.d.). Retrieved February 4, 2022, from https://www.reddit.com/dev/api/.
Reurink, A. (2018). FINANCIAL FRAUD: A LITERATURE REVIEW. Journal of Economic Surveys, 32(5), 1292–1325. https://doi.org/10.1111/JOES.12294.
Roy, N. C., & Basu, S. (2021). Bank’s battle against insider frauds ignitors and mitigators: an emerging nation experience. Journal of Facilities Management, 19(4), 437–457. https://doi.org/10.1108/JFM-04-2020-0021/FULL/XML.
Samaneh Sorournejad, Zojaji, Z., Atani, R. E., & Monadjemi, A. H. (2016). A Survey of Credit Card Fraud Detection Techniques: Data and Technique Oriented Perspective. Retrieved from https://arxiv.org/abs/1611.06439v1.
Securities fraud - Wikipedia. (n.d.). Retrieved January 29, 2022, from https://en.wikipedia.org/wiki/Securities_fraud.
Selective Disclosure and Insider Trading. (n.d.). Retrieved January 30, 2022, from https://www.sec.gov/rules/final/33-7881.htm.
Snips Natural Language Understanding — Snips NLU 0.20.2 documentation. (n.d.). Retrieved February 5, 2022, from https://snips-nlu.readthedocs.io/en/latest/.
Srivastava, S., & Bhatnagar, R. (2021). Process Mining Techniques for Detecting Fraud in Banks: A Study. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(12). Retrieved from https://turcomat.org/index.php/turkbilmat/article/view/8058.
Terblanche, M., & Marivate, V. (2021). Loughran McDonald-SA-2020 Sentiment Word List. Retrieved February 6, 2022, from https://researchdata.up.ac.za/articles/dataset/Loughran_McDonald-SA-2020_Sentiment_Word_List/14401178.
The Difference Between a Financial Statement Audit & a Forensic Audit. (n.d.). Retrieved January 31, 2022, from https://bizfluent.com/info-12085490-difference-between-financial-statement-audit-forensic-audit.html.
The Laws That Govern the Securities Industry. (n.d.). Retrieved January 30, 2022, from https://www.investor.gov/introduction-investing/investing-basics/role-sec/laws-govern-securities-industry.
Three Trends Driving Up E-Discovery Costs. (n.d.). Retrieved February 15, 2022, from https://www.forbes.com/sites/forbestechcouncil/2021/10/22/three-trends-driving-up-e-discovery-costs/?sh=2bb25be2724c.
West, J., & Bhattacharya, M. (2016). Intelligent financial fraud detection: A comprehensive review. Computers & Security, 57, 47–66. https://doi.org/10.1016/J.COSE.2015.09.005.
Xu, J., & Livshits, B. (n.d.). The Anatomy of a Cryptocurrency Pump-and-Dump Scheme | USENIX. Retrieved February 23, 2022, from https://www.usenix.org/conference/usenixsecurity19/presentation/xu-jiahua.
Mr. Sundar Krishnan
Department of Computer Science, Sam Houston State University, Huntsville, TX - United States of America
skrishnan@shsu.edu
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