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Corporate Sector Fraud: Challenges and Safety

Corporate Sector Fraud: Challenges and Safety

Jay Prakash Maurya, Deepak Rathore, Sunil Joshi, Manish Manoria, Vivek Richhariya
Copyright: © 2021 |Pages: 16
ISBN13: 9781799848059|ISBN10: 1799848051|ISBN13 Softcover: 9781799857853|EISBN13: 9781799848066
DOI: 10.4018/978-1-7998-4805-9.ch002
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MLA

Maurya, Jay Prakash, et al. "Corporate Sector Fraud: Challenges and Safety." Machine Learning Applications for Accounting Disclosure and Fraud Detection, edited by Stylianos Papadakis, et al., IGI Global, 2021, pp. 16-31. https://doi.org/10.4018/978-1-7998-4805-9.ch002

APA

Maurya, J. P., Rathore, D., Joshi, S., Manoria, M., & Richhariya, V. (2021). Corporate Sector Fraud: Challenges and Safety. In S. Papadakis, A. Garefalakis, C. Lemonakis, C. Chimonaki, & C. Zopounidis (Eds.), Machine Learning Applications for Accounting Disclosure and Fraud Detection (pp. 16-31). IGI Global. https://doi.org/10.4018/978-1-7998-4805-9.ch002

Chicago

Maurya, Jay Prakash, et al. "Corporate Sector Fraud: Challenges and Safety." In Machine Learning Applications for Accounting Disclosure and Fraud Detection, edited by Stylianos Papadakis, et al., 16-31. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-4805-9.ch002

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Abstract

This chapter aims to possess a review of machine learning techniques for detection of corporate fraud in modern era. Detecting company frauds using traditional procedures is time costly as immense volume of information must be analysed. Thus, further analytical procedures should be used. Machine learning techniques are most emerging topic with great importance in field of information learning and prediction. The machine learning (ML) approach to fraud detection has received a lot of promotion in recent years and shifted business interest from rule-based fraud detection systems to ML-based solutions. Machine learning permits for making algorithms that process giant data-sets with several variables and facilitate realize these hidden correlations between user behaviors and also the probability of fallacious actions. Strength of machine learning systems compared to rule-based ones is quicker processing and less manual work. The chapter aims at machine-driven analysis of knowledge reports exploitation machine learning paradigm to spot fraudulent companies.

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