Corporate governance fraud detection from annual reports using big data analytics

2016 ◽  
Vol 3 (1) ◽  
pp. 51 ◽  
Author(s):  
G. Sudha Sadasivam ◽  
Mutyala Subrahmanyam ◽  
Dasaraju Himachalam ◽  
Bhanu Prasad Pinnamaneni ◽  
S. Maha Lakshme
Keyword(s):  
Corporate Governance ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Fraud Detection ◽  
Annual Reports

Financial fraud detection and big data analytics – implications on auditors’ use of fraud brainstorming session

2019 ◽  
Vol 34 (3) ◽  
pp. 324-337 ◽  
Author(s):  
Jiali Tang ◽  
Khondkar E. Karim
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Idea Generation ◽  
Fraud Detection ◽  
Content Type ◽  
Collective Wisdom ◽  
Fraud Risk ◽  
Collection Data ◽  
Audit Team

PurposeThis paper aims to discuss the application of Big Data analytics to the brainstorming session in the current auditing standards.Design/methodology/approachThe authors review the literature related to fraud, brainstorming sessions and Big Data, and propose a model that auditors can follow during the brainstorming sessions by applying Big Data analytics at different steps.FindingsThe existing audit practice aimed at identifying the fraud risk factors needs enhancement, due to the inefficient use of unstructured data. The brainstorming session provides a useful setting for such concern as it draws on collective wisdom and encourages idea generation. The integration of Big Data analytics into brainstorming can broaden the information size, strengthen the results from analytical procedures and facilitate auditors’ communication. In the model proposed, an audit team can use Big Data tools at every step of the brainstorming process, including initial data collection, data integration, fraud indicator identification, group meetings, conclusions and documentation.Originality/valueThe proposed model can both address the current issues contained in brainstorming (e.g. low-quality discussions and production blocking) and improve the overall effectiveness of fraud detection.

scholarly journals A Scalable Approach for Fraud Detection in Online E-Commerce Transactions with Big Data Analytics

2019 ◽  
Vol 60 (1) ◽  
pp. 179-192 ◽  
Author(s):  
Hangjun Zhou ◽  
Guang Sun ◽  
Sha Fu ◽  
Wangdong Jiang ◽  
Juan Xue
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Fraud Detection

Telecom Fraud Detection with Big Data Analytics

2021 ◽  
Vol 6 (3) ◽  
pp. 1
Author(s):  
Hakan Kilinc ◽  
Seref Sagiroglu ◽  
Duygu Sinanc Terzi
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Fraud Detection

scholarly journals Big Data in Auditing for the Future of Data Driven Fraud Detection

2020 ◽  
Vol 9 (3) ◽  
pp. 2902-2907
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Technological Development ◽  
New Technology ◽  
Big Data Analytics ◽  
Fraud Detection ◽  
Public Accounting ◽  
Adoption Of Technology ◽  
Accounting Firm ◽  
The Public

Industry revolution 4.0 makes people face change, the auditor profession is no exception. Auditors no longer conduct audits using the manual method but use computerized systems such as big data analytics. Our research aims to find out how auditors must change, when facing new technology approach audit. Very complicated and various data kinds that was too large to be audited manually. The research method in this research is descriptive qualitative, the method of data collection uses interviews with informants. The informant is the auditor partner of the public accounting firm. Big data has advantage and disadvantage to audit and fraud detection profession. The results of this study state that audit firm must be aware of the obstacles that come from internal and external for the implementation of big data analytics audit. Obstacles will hinder the adoption of technology for the auditor, while the auditor must change rapidly following the demands of technological development

Role of Data Science and Data Analytics in Forensic Accounting and Fraud Detection

2021 ◽  
pp. 203-227
Author(s):  
James Osabuohien Odia ◽  
Osaheni Thaddeus Akpata
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Fraud Detection ◽  
Economic Crime ◽  
Current Increase ◽  
Forensic Accounting ◽  
Global Increase

The chapter examines the roles of data science and big data analytics to forensic accountants and fraud detection. It also considers how data science techniques could be applied to the investigative processes in forensic accounting. Basically, the current increase in the volume, velocity, and variety of data offer a rich source of evidence for the forensic accountant who needs to be familiar with the techniques and procedures for extracting, analysing, and visualising such data. This is against backdrop of continuous global increase in economic crime and frauds, and financial criminals are getting more sophisticated, taking advantage of the opportunities provided by the unstructured data constantly being created with every email sent, every Facebook post, every picture on Instagram, or every thought share on Twitter. Consequently, it is important that forensic accountants are constantly abreast with developments in data science and data analytics in order to stay a step ahead of fraudsters as well as address evolving vulnerabilities created by big data.

Big Data Analytics: The Next Big Thing

2019 ◽  
Vol 54 (5) ◽  
pp. 20
Author(s):  
Dheeraj Kumar Pradhan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics
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