Artificial Intelligence and Reliability of Accounting Information

Producing relevant and reliable accounting information is the main responsibility of accounting profession. Reliability and relevance of accounting information heavily depend on a sound internal control system as well as management and employees ethical and integrity characteristics. This paper shows how Artificial Intelligence innovatively works with the internal controls systems to help managers to produce high-quality accounting information by reducing information risk. Despite many types of research proposed using Artificial Intelligence in accounting and auditing, but none of them directly showed how to reduce information risk using Artificial Intelligence. The research benefits companies cut many costs and losses of failing to produce reliable accounting information, help managers to make a better decision and in overall improve entities performances. This paper proposes a general model to be applied by all type of business entities how practically use Artificial Intelligence to automate removing the weakness of internal control systems. This, in turn, reduces control risk, detection risk and increase audit quality by reducing accounting information risk.

[1]  Paul G. Maropoulos,et al.  Artificial neural networks as a cost engineering methods in collaborative manufacturing environment. , 2005 .

[2]  Weili Ge,et al.  Determinants of Weaknesses in Internal Control over Financial Reporting and the Implications for Earnings Quality , 2005 .

[3]  Emre Cevikcan,et al.  Intelligence decision systems in enterprise information management , 2011, J. Enterp. Inf. Manag..

[4]  Audrey A. Gramling,et al.  Effects of reporting relationship and type of internal control deficiency on internal auditors’ internal control evaluations , 2018 .

[5]  Nandkumar Nayar,et al.  Information content of control deficiency disclosures under the Sarbanes–Oxley Act: An empirical investigation , 2007 .

[6]  Syed Moudud Ul-Huq.  The Role of Artificial Intelligence in the Development of Accounting Systems: A Review , 2014 .

[7]  Malcolm James Beynon,et al.  The application of fuzzy decision tree analysis in an exposition of the antecedents of audit fees , 2004 .

[8]  She-I Chang,et al.  The development of audit detection risk assessment system: Using the fuzzy theory and audit risk model , 2008, Expert Syst. Appl..

[9]  John J. Cheh,et al.  Determinants of Internal Control Weaknesses , 2010 .

[10]  Juan M. Corchado,et al.  Argumentative SOX Compliant and Quality Decision Support Intelligent Expert System over the Suppliers Selection Process , 2013, Appl. Comput. Intell. Soft Comput..

[12]  Ahmad Almogren,et al.  Improving risk assessment model of cyber security using fuzzy logic inference system , 2018, Comput. Secur..

[13]  A. Baykasoğlu,et al.  Fuzzy quality-team formation for value added auditing: A case study , 2007 .

[14]  S. Sutton,et al.  The pervasive nature of IT controls , 2013 .

[15]  Huimin Lu,et al.  Brain Intelligence: Go beyond Artificial Intelligence , 2017, Mobile Networks and Applications.

[16]  Hian Chye Koh,et al.  Going concern prediction using data mining techniques , 2004 .

[17]  Laureen A. Maines,et al.  The Nature of Accounting Information Reliability: Inferences from Archival and Experimental Research , 2006 .

[18]  Tomas Eklund,et al.  Internal control effectiveness : a clustering approach , 2016 .

[19]  Mohamed A. Elbannan.  Quality of internal control over financial reporting, corporate governance and credit ratings , 2009 .