Int. J. of Big Data Intelligence   »   2016 Vol.3, No.1

 

 

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Title: Corporate governance fraud detection from annual reports using big data analytics

 

Authors: G. Sudha Sadasivam; Mutyala Subrahmanyam; Dasaraju Himachalam; Bhanu Prasad Pinnamaneni; S. Maha Lakshme

 

Addresses:
Department of CSE, PSG College of Technology, Coimbatore – 641 004, India
Commerce Department, Sri Venkateswara University, Tirupati – 517 504, India
Commerce Department, Sri Venkateswara University, Tirupati – 517 504, India
Computer Science and Engineering, Rajalakshmi Engineering College, Chennai – 602 105, India
Department of CSE, PSG College of Technology, Coimbatore – 641 004, India

 

Abstract: Financial reports of corporations publicise their performance. This in turn motivates manipulation of financial statements. Falsification of financial statements over prolonged period results in sudden collapse of multi-national companies, long-term economic loss to government and loss of trust of public. Detecting management frauds using normal audit procedures is time expensive as huge volume of data needs to be analysed. Hence additional analytical procedures should be used. The proposed work aims at automated analysis of annual reports using MapReduce paradigm to identify fraudulent companies. Annual reports of companies from public repositories are parsed to extract features for preparing a score card. Principal component analysis is applied on the score card to extract the principal features to train support vector machine. Experimental results show that 90% accuracy can be achieved using 10% to 25% of the principal features. Using MapReduce paradigm for feature extraction and classification improves the time efficiency by 85%.

 

Keywords: big data analytics; corporate governance; machine learning; financial statements; financial fraud; fraud detection; annual reports; financial statement manipulation; principal component analysis; PCA; feature extraction; support vector machines; SVM; classification; white collar crime.

 

DOI: 10.1504/IJBDI.2016.073895

 

Int. J. of Big Data Intelligence, 2016 Vol.3, No.1, pp.51 - 60

 

Submission date: 31 May 2014
Date of acceptance: 16 Jan 2015
Available online: 29 Dec 2015

 

 

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