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Corporate Sector Fraud: Challenges and Safety
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Author(s): Jay Prakash Maurya (Samrat Ashok Technological Institute, India), Deepak Rathore (LNCT University, India), Sunil Joshi (Samrat Ashok Technological Institute, India), Manish Manoria (Sagar Institute of Research and Technology, India)and Vivek Richhariya (Lakshmi Narain College of Technology, Bhopal, India)
Copyright: 2021
Pages: 16
Source title:
Machine Learning Applications for Accounting Disclosure and Fraud Detection
Source Author(s)/Editor(s): Stylianos Papadakis (Hellenic Mediterranean University, Greece), Alexandros Garefalakis (Hellenic Mediterranean University, Greece), Christos Lemonakis (Hellenic Mediterranean University, Greece), Christiana Chimonaki (University οf Portsmouth, UK)and Constantin Zopounidis (School of Production Engineering and Management, Technical University of Crete, Greece & Audencia Business School, France)
DOI: 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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