Corporate governance fraud detection from annual reports using big data analytics
by G. Sudha Sadasivam; Mutyala Subrahmanyam; Dasaraju Himachalam; Bhanu Prasad Pinnamaneni; S. Maha Lakshme
International Journal of Big Data Intelligence (IJBDI), Vol. 3, No. 1, 2016

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%.

Online publication date: Tue, 29-Dec-2015

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Big Data Intelligence (IJBDI):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com