SpringerLink
Log in

Impact of Machine Learning on the Improvement of Accounting Information Quality

  • Conference paper
  • First Online:
International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 637))

  • 742 Accesses

Abstract

Nowadays, the Accounting field is in constant evolution due to Artificial Intelligence’s new technologies, such as Machine Learning. On the other hand stakeholders such as investors, corporate managers, and creditors rely vitally on information provided by Accounting, known as Accounting Information in making better business decisions. To make such decisions, producing high-quality Accounting Information by companies is essential and is the objective of Financial Accounting. The main aim of this work is to explore, analyze, and discuss the impact of Machine Learning algorithms in improving Accounting Information quality. It does so through a bibliometric analysis conducted on 114 publications to identify key research trends. Afterward, as financial statements are the primary source of Accounting Information, we analyzed case studies on the impact of Machine Learning algorithms on financial statements. Further, Machine Learning, specifically the classification algorithm, is some of the main trends and plays a significant role in fraud detection in financial statements, thus, improving Accounting Information reliability.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Hou, X.: Design and application of intelligent financial accounting model based on knowledge graph. Mobile Inf. Syst. 2022 (2022)

    Google Scholar 

  2. Zhang, Y., **ong, F., **e, Y., Fan, X., Gu, H.: The impact of artificial intelligence and blockchain on the accounting profession. IEEE Access 8, 110461–110477 (2020)

    Article  Google Scholar 

  3. Shi, Y.: The impact of artificial intelligence on the accounting industry. In: Xu, Z., Choo, K.K., Dehghantanha, A., Parizi, R., Hammoudeh, M. (eds.) Cyber Security Intelligence and Analytics. CSIA 2019. Advances in Intelligent Systems and Computing, vol. 928, pp. 971–978. Springer, Cham (2020). 10.1007/978-3-030-15235-2_129

    Google Scholar 

  4. Collier, P.M.: Accounting for Managers: Interpreting Accounting Information for Decision Making. John Wiley & Sons, Chichester (2015)

    Google Scholar 

  5. Hope, O.K., Thomas, W.B., Vyas, D.: Stakeholder demand for accounting quality and economic usefulness of accounting in us private firms. J. Account. Public Policy 36(1), 1–13 (2017)

    Article  Google Scholar 

  6. Lambert, R., Leuz, C., Verrecchia, R.E.: Accounting information, disclosure, and the cost of capital. J. Account. Res. 45(2), 385–420 (2007)

    Article  Google Scholar 

  7. Neogy, D.: Evaluation of efficiency of accounting information systems: a study on mobile telecommunication companies in Bangladesh. Global Disclosure Econ. Bus. 3(1) (2014)

    Google Scholar 

  8. Buljubašić, E., Ilgün, E.: Impact of accounting information systems on decision making case of Bosnia and Herzegovina. Europ. Research. Series A (7), 460–469 (2015)

    Google Scholar 

  9. Munteanu, V., Zuca, M., Tinta, A.: The financial accounting information system central base in the managerial activity of an organization. J. Inf. Syst. Oper. Manag. 5(1), 63–74 (2011)

    Google Scholar 

  10. Board, F.A.S.: Scope and implications of the conceptual framework project. Financial Accounting Standards Board (1976)

    Google Scholar 

  11. Djongoué, G.: Qualité perçue de l’information comptable et décisions des parties prenantes. Ph.D thesis, Bordeaux (2015)

    Google Scholar 

  12. Baştanlar, Y., Özuysal, M.: Introduction to machine learning. miRNomics: MicroRNA biology and computational analysis, pp. 105–128 (2014)

    Google Scholar 

  13. Abdi, M.D., Dobamo, H.A., Bayu, K.B.: Exploring current opportunity and threats of artificial intelligence on small and medium enterprises accounting function; evidence from south west part of ethiopia, oromiya, jimma and snnpr, bonga. Acad. Acc. Financ. Stud. J. 25(2), 1–11 (2021)

    Google Scholar 

  14. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., Lim, W.M.: How to conduct a bibliometric analysis: an overview and guidelines. J. Bus. Res. 133, 285–296 (2021)

    Article  Google Scholar 

  15. Van Eck, N., Waltman, L.: Software survey: VOSviewer, a computer program for bibliometric map**. Scientometrics 84(2), 523–538 (2010)

    Article  Google Scholar 

  16. Hamal, S., Senvar, Ö.: Comparing performances and effectiveness of machine learning classifiers in detecting financial accounting fraud for turkish smes. Int. J. Comput. Intell. Syst. 14(1), 769–782 (2021)

    Article  Google Scholar 

  17. Kotsiantis, S., Koumanakos, E., Tzelepis, D., Tampakas, V.: Predicting fraudulent financial statements with machine learning techniques. In: Antoniou, G., Potamias, G., Spyropoulos, C., Plexousakis, D. (eds.) Advances in Artificial Intelligence. SETN 2006. Lecture Notes in Computer Science, vol. 3955, pp. 538–542. Springer, Heidelberg (2006). 10.1007/11752912_63

    Google Scholar 

  18. Kirkos, E., Spathis, C., Manolopoulos, Y.: Data mining techniques for the detection of fraudulent financial statements. Expert Syst. Appl. 32(4), 995–1003 (2007)

    Article  Google Scholar 

  19. Yao, J., Zhang, J., Wang, L.: A financial statement fraud detection model based on hybrid data mining methods. In: International Conference on Artificial Intelligence and Big Data (ICAIBD), vol. 2018, 57–61. IEEE (2018)

    Google Scholar 

  20. Hajek, P., Henriques, R.: Mining corporate annual reports for intelligent detection of financial statement fraud-a comparative study of machine learning methods. Knowl.-Based Syst. 128, 139–152 (2017)

    Article  Google Scholar 

  21. Song, X.P., Hu, Z.H., Du, J.G., Sheng, Z.H.: Application of machine learning methods to risk assessment of financial statement fraud: evidence from China. J. Forecast. 33(8), 611–626 (2014)

    Article  MathSciNet  Google Scholar 

  22. Huang, S.M., Tsai, C.F., Yen, D.C., Cheng, Y.L.: A hybrid financial analysis model for business failure prediction. Expert Syst. Appl. 35(3), 1034–1040 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Research Laboratory in Finance, Accounting, Management and Information Systems and Decision Support, ENCG, Hassan First University of Settat, P.O. Box 577, Settat, Morocco

    Meryem Ayad & Said El Mezouari

  2. National Center for Scientific and Technical Research (CNRST), Rabat, Morocco

    Nassim Kharmoum

Authors
  1. Meryem Ayad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Said El Mezouari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nassim Kharmoum
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Meryem Ayad .

Editor information

Editors and Affiliations

  1. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

  2. Abdelmalek Essaâdi University, Tangier, Morocco

    Mostafa Ezziyyani

  3. Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ayad, M., El Mezouari, S., Kharmoum, N. (2023). Impact of Machine Learning on the Improvement of Accounting Information Quality. In: Kacprzyk, J., Ezziyyani, M., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development. AI2SD 2022. Lecture Notes in Networks and Systems, vol 637. Springer, Cham. https://doi.org/10.1007/978-3-031-26384-2_43

Download citation

Publish with us

Policies and ethics

Search

Navigation