Corporate governance fraud detection from annual reports using big data analytics. (2016)

• Record Type: Journal Article
• Authors: Sadasivam, G. Sudha; Subrahmanyam, Mutyala; Himachalam, Dasaraju; Pinnamaneni, Bhanu Prasad; Lakshme, S. Maha
• Is Part Of: International journal of big data intelligence. Volume 3:Number 1(2016)
• Page Start: 51
• Page End: 60
• Publication Date: 2016
• Subjects: 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; Big data -- Periodicals; 005.705
• Languages: English
• ISSNs: 2053-1389

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%.