Data mining applications in accounting: A review of the literature and organizing framework

Amani, Farzaneh; Fadlalla, Adam

doi:10.1016/j.accinf.2016.12.004

Cited by 130 publications

(90 citation statements)

References 211 publications

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“…We adopt the methodology presented in [9] considering its sophisticated research design, which is clearly demonstrated to the reader via Firstly, as clarified in the last section, the research scope is defined as the DM applications in banking post 2013. The search process follows the usual approach by defining a range of keywords, where we employed the significant terms for both banking and DM techniques, including banking, fraud detection, credit card, credit scoring, risk management, deposit, mortgage, debit, loan, CRM, bank marketing; and data mining, clustering, text mining, classification and other specific DM technique terms.…”

Section: Methodsmentioning

confidence: 99%

Digitalisation and Big Data Mining in Banking

Hassani

Huang

Silva

2018

BDCC

120

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Banking as a data intensive subject has been progressing continuously under the promoting influences of the era of big data. Exploring the advanced big data analytic tools like Data Mining (DM) techniques is key for the banking sector, which aims to reveal valuable information from the overwhelming volume of data and achieve better strategic management and customer satisfaction. In order to provide sound direction for the future research and development, a comprehensive and most up to date review of the current research status of DM in banking will be extremely beneficial.Since existing reviews only cover the applications until 2013, this paper aims to fill this research gap and presents the significant progressions and most recent DM implementations in banking post 2013. By collecting and analyzing the trends of research focus, data resources, technological aids, and data analytical tools, this paper contributes to bringing valuable insights with regard to the future developments of both DM and the banking sector along with a comprehensive one stop reference table. Moreover, we identify the key obstacles and present a summary for all interested parties that are facing the challenges of big data.

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Section: Methodsmentioning

confidence: 99%

Digitalisation and Big Data Mining in Banking

Hassani

Huang

Silva

2018

BDCC

120

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“…According to Tabachnick and Fidell (2007), the application of multivariate discriminant methods began during the 30s of the last century as a statistical category. In time, it came to be used in economics as showed by Holdsworth, Tan and Chung (2008) or Zhou, Lu and Fujita (2015),and in the analysis of bankrupt corporations as showed by Altman (1968) Albashrawi (2016) or Amania and Fadlallab (2017) came to the same conclusions during the classification of potential accounting errors.…”

Section: Methodsmentioning

confidence: 99%

The Possibility of Predictions in Auditor’s Opinion: The Case of the Serbian Tobacco Industry

Spahić

Vuković

2018

Manage: J. Sustainable Bus. Manage, Solutions Emerging Econ.

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Research Questions: This paper investigated whether there is a possibility for predictions of an auditor's opinion that can be used to predict, in an extremely accurate way, future developments in one company. Motivation: The research of Dopuch, Holhausen and Leftwich (1987); Kirkos, or Kirskos (2012) and Kim and Upneja (2014) open space for new challenges for using auditing methods.The most trying task is to find a technique that will be able to timely, accurately and with the least waste of resources respond to the challenge. The fact that auditors are forced to expand the scope and purpose of the audit work, respecting new risks that are continually changing represents the primary inspiration for this paper. Idea: Our goal was to explore whether one of the possible techniques for prediction the auditor's opinion -multivariate discriminant analysis -can precisely predict a correct future audit opinion and whether this analysis is useful for finding solutions to performing predictions. Data: The analysis was conducted using data from financial statements of 4 Serbian tobacco companies of years

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“…New technologies seem to favor fraud, but also provide more sophisticated detection techniques [70]. Data analysis (e.g., [71]), support vector machines (e.g., [72]), blockchain [73] and data mining (e.g., [26,74,75]) are some of the techniques used. In this same line, corporate governance should also use a systematic approach to investigate fraud in a company.…”

Section: Accounting Fraud: a General Visionmentioning

confidence: 99%

“…Amani and Fadlalla, 2017 [26] Review of the literature on the application of the data mining technique in accounting. Fraud detection, financial health of the company and forensic accounting are the areas that have benefited most from this technique.…”

Section: Kim Et Al 2016 [205]mentioning

confidence: 99%

“…For the "auditors" category, which is second in the number of articles analyzed (51 articles), future research can study the effect of Artificial Intelligence and its various technologies (data mining, text analysis, etc.) on their daily work [26]. We also propose expanding the study of the auditor's responsibility in the evaluation of the financial statements, since this influences the same [104].…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

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Research Topics in Accounting Fraud in the 21st Century: A State of the Art

2019

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Companies play a role in society that clearly goes beyond mere economic interest. Their contribution to social development and to the sustainability of the territory where they are located seems unquestionable. However, after the great financial scandals of companies such as ENRON, WorldCom or AHOLD, interest groups require accurate and transparent financial information. The development of more demanding financial reporting standards seems, however, not to have been up to scratch, since accounting fraud continues to be detected all over the world. The search, therefore, for possible causes that may induce companies to act unethically was the main motivation behind this research. To do this, a review of the literature in high-impact journals that has dealt with accounting fraud, covering the main lines of research, was carried out. The findings of the literature review highlight the importance of responsible corporate governance and good accounting practices, as well as the importance of certain psychological characteristics of managers and employees as enhancers of the lack of ethics. It is clear that the social cost of accounting fraud should be minimized, and governments should develop specific policies that combine responsible corporate governance in companies with the sustainability of their environment.

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Data mining applications in accounting: A review of the literature and organizing framework

Cited by 130 publications

References 211 publications

Digitalisation and Big Data Mining in Banking

Digitalisation and Big Data Mining in Banking

The Possibility of Predictions in Auditor’s Opinion: The Case of the Serbian Tobacco Industry

Research Topics in Accounting Fraud in the 21st Century: A State of the Art

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