How Can We Use Artificial Intelligence and Machine Learning in Accounting and Auditing: An Engineering Overview with the Mapping Method in the Field of Business
DOI:
https://doi.org/10.5281/zenodo.18136609Keywords:
Auditing, Accounting, Artificial Intelligence, Bibliometric Analysis, Machine LearningAbstract
Today, the industry faces data-related challenges stemming from the use of artificial intelligence. Similarly, sustainability challenges in real world applications also arise. One of these is the accounting and auditing fields. Because of how to use artificial intelligence in this field and what technical approaches are not known enough. This study presents a machine learning approach with engineering and technical view. Because of the technical complexity, the papers on artificial intelligence and machine learning in accounting and auditing are often hard to evaluate and need a clear understanding of critical methodological parts. This paper aims to guide the key methodological aspects of artificial intelligence and machine learning for non-expert researchers of accounting and auditing. The proposed paper draws up the general framework for the use of machine learning, a sub-branch of artificial intelligence, in sustainable accounting studies. The most frequently used classification methods for accounting data and the metrics used in the evaluation of the results have been explained. In addition, the framework of recent studies such as bankruptcy prediction, financial distress, corporate failure, accounting fraud prediction, tax compliance problems, and financial difficulties conducted with accounting and auditing data has been examined. One of the findings obtained from this study is that although many studies in literature mention artificial intelligence with a few words, they decorate the title or keywords of the study with artificial intelligence. Keyword analyses are also among the findings of the study. All findings of the analysis emphasize the increasing importance of artificial intelligence in the field of accounting and auditing and the diversity of research in this field. If the notion of artificial intelligence is one of the essential components of future research, this study advises researchers to use it carefully in the study's title and keywords
References
Agarwal, V., Taffler, R. J. (2007). Twenty-Five Years Of The Taffler Z-Score Model: Does It Really Have Predictive Ability? Accounting And Business Research, 37(4), 285–300. Https://Doi.Org/10.1080/00014788.2007.9663313
Agustí, M. A., Orta-Pérez, M. (2023). Big Data And Artificial Intelligence In The Fields Of Accounting And Auditing: A Bibliometric Analysis. Spanish Journal Of Finance And Accounting/Revista Española De Financiación Y Contabilidad, 52(3), 412-438.
Al-Areqi, F., Konyar, M. Z. (2022). Effectiveness Evaluation Of Different Feature Extraction Methods For Classification Of Covid-19 From Computed Tomography Images: A High Accuracy Classification Study. Biomedical Signal Processing And Control, 76, 103662.
Alloghani, M., Al-Jumeily, D., Mustafina, J., Hussain, A., Aljaaf, A. J. (2020). A Systematic Review On Supervised And Unsupervised Machine Learning Algorithms For Data Science. Supervised And Unsupervised Learning For Data Science, 3-21.
Altman, E. I˙., Sabato, G., Wilson, N. (2010). The Value Of Non-Financial I˙nformation I˙n Sme Risk Management. Thejournal Of Credit Risk, 6(2), 95–127. Https://Doi.Org/10.21314/Jcr.2010.110
Amani, F., Fadlalla, A.M. (2017). Data Mining Applications In Accounting: A Review Of The Literature And Organizing Framework. Int. J. Account. Inf. Syst., 24, 32-58.
Aranha, M., Bolar, K. (2023). Efficacies Of Artificial Neural Networks Ushering Improvement In The Prediction Of Extant Credit Risk Models. Cogent Economics Finance, 11(1), 2210916.
Bauer, J., Agarwal, V. (2014). Are Hazard Models Superior To Traditional Bankruptcy Prediction Approaches? A Comprehensive Test. Journal Of Banking And Finance, 40, 432-442.
Beaver, W., M. Mcnichols, And J. Rhie. 2005. Have Financial Statements Become Less Informative? Evidence From The Ability Of Financial Ratios To Predict Bankruptcy. Review Of Accounting Studies, 10(1): 93-102
Behr, A., Weinblat, J. (2017). Default Patterns In Seven Eu Countries: A Random Forest Approach. International Journal Of The Economics Of Business, 24(2), 181-222.
Ben Jabeur, S., Stef, N., Carmona, P. (2023). Bankruptcy Prediction Using The Xgboost Algorithm And Variable Importance Feature Engineering. Computational Economics, 61(2), 715-741.
Berghout, T., Benbouzid, M. (2022). A Systematic Guide For Predicting Remaining Useful Life With Machine Learning. Electronics, 11(7), 1125.
Brédart, X., Séverin, E., Veganzones, D. (2021). Human Resources And Corporate Failure Prediction Modeling: Evidence From Belgium. Journal Of Forecasting, 40(7), 1325-1341.
Cao, Y., Liu, X., Zhai, J., Hua, S. (2022). A Two-Stage Bayesian Network Model For Corporate Bankruptcy Prediction. International Journal Of Finance Economics, 27(1), 455-472.
Carling, K., Jacobson, T., Lindé, J., Roszbach, K. (2007). Corporate Credit Risk Modeling And The Macroeconomy. Journal Of Banking And Finance, 31, 845-868.
Chen, Y., Song, L., Liu, Y., Yang, L., Li, D. (2020). A Review Of The Artificial Neural Network Models For Water Quality Prediction. Applied Sciences, 10(17), 5776.
Creamer, G., Freund, Y. (2010). Using Boosting For Financial Analysis And Performance Prediction: Application To Sp 500 Companies, Latin American Adrs And Banks. Computational Economics, 36, 133-151.
Daumé, H. (2017). A Course In Machine Learning. Hal Daumé Iii.
Davalos, S., Leng, F., Feroz, E. H., Cao, Z. (2014). Designing An If–Then Rules-Based Ensemble Of Heterogeneous Bankruptcy Classifiers: A Genetic Algorithm Approach. Intelligent Systems In Accounting, Finance And Management, 21(3), 129-153.
De Jesus, D. P., Da Nóbrega Besarria, C. (2023). Machine Learning And Sentiment Analysis: Projecting Bank Insolvency Risk. Research In Economics, 77(2), 226-238.
Ding, X., Yang, Z. (2020). Knowledge Mapping Of Platform Research: A Visual Analysis Using Vosviewer And Citespace. Electronic Commerce Research, 1-23. Https://Doi.Org/10.2991/Aebmr.K.191225.081
Eck, N., Waltman, L. (2009). Software Survey: Vosviewer, A Computer Program For Bibliometric Mapping. Scientometrics, 84, 523-538. Https://Doi.Org/10.1007/S11192-009-0146-3.
Géron, A. (2022). Hands-On Machine Learning With Scikit-Learn, Keras, And Tensorflow. " O’reilly Media, Inc.".
Guleria, D. And Kaur, G. (2021), "Bibliometric Analysis Of Ecopreneurship Using Vosviewer And Rstudio Bibliometrix, 1989–2019", Library Hi Tech, Vol. 39 No. 4, Pp. 1001-1024.
Gullkvıst, Benita, “Towards Paperles Accounting And Auditing”, Http://Citeseerx.Ist.Psu.Edu/Viewdoc/Download? Doi=10.1.1.101.3007rep=Rep1type=P Df (Access Time:30.03.2024).
Güney, C., Ala, T. (2024). The Insight Of Publications In The Field Of Artificial Intelligence (Ai)-Based Risk Management In Public Sector: A Bibliometric Overview. Edpacs, 69(1), 41-68.
Https://Cabim.Ulakbim.Gov.Tr/Bibliyometrik-Analiz/Bibliyometrik-Analiz-Sikca-Sorulan-Sorular/ (23.11.2023)
Jiang, C., Lyu, X., Yuan, Y., Wang, Z., Ding, Y. (2022). Mining Semantic Features In Current Reports For Financial Distress Prediction: Empirical Evidence From Unlisted Public Firms In China. International Journal Of Forecasting, 38(3), 1086-1099.
Jones, S. (2017). Corporate Bankruptcy Prediction: A High Dimensional Analysis. Review Of Accounting Studies, 22, 1366 - 1422.
Jones, S., Hensher, D. A. (2004). Predicting Firm Financial Distress: A Mixed Logit Model. The Accounting Review, 79(4), 1011–1038. Http://Www.Jstor.Org/Stable/4093084
Jones, S., Wang, T. (2019). Predicting Private Company Failure: A Multi-Class Analysis. Journal Of International Financial Markets, Institutions And Money.
Jones, S., Wang, T. (2019). Predicting Private Company Failure: A Multi-Class Analysis. Journal Of International Financial Markets, Institutions And Money, 61, 161-188.
Jørgensen, R. K., Igel, C. (2021). Machine Learning For Financial Transaction Classification Across Companies Using Character-Level Word Embeddings Of Text Fields. Intelligent Systems In Accounting, Finance And Management, 28(3), 159-172.
Kalinová, E. (2021). Artificial Intelligence For Cluster Analysis: Case Study Of Transport Companies In Czech Republic. Journal Of Risk And Financial Management, 14(9), 411.
Kim, S. Y., Upneja, A. (2021). Majority Voting Ensemble With A Decision Trees For Business Failure Prediction During Economic Downturns. Journal Of Innovation Knowledge, 6(2), 112-123.
Kocaog˘lu, D., Turgut, K., Konyar, M. Z. (2022). Sector-Based Stock Price Prediction With Machine Learning Models. Sakarya University Journal Of Computer And Information Sciences, 5(3), 415-426.
Koç, F., Seçkin, A. Ç., Bayri, O. (2022). Forecasting Deferred Taxes In International Accounting With Machine Learning.. Journal Of Mehmet Akif Ersoy University Economics And Administrative Sciences Faculty, 9(2), 1303-1326.
Kureljusic, M. And Karger, E. (2024), "Forecasting In Financial Accounting With Artificial Intelligence – A Systematic Literature Review And Future Research Agenda", Journal Of Applied Accounting Research, Vol. 25 No. 1, Pp. 81-104. Https://Doi.Org/10.1108/Jaar- 06-2022-0146
Kureljusic, M., Karger, E. (2023). Forecasting İn Financial Accounting With Artificial İntelligence–A Systematic Literature Review And Future Research Agenda. Journal Of Applied Accounting Research, (Ahead-Of-Print).
Kureljusic, M., Metz, J. (2023). The Applicability Of Machine Learning Algorithms In Accounts Receivables Management. Journal Of Applied Accounting Research, 24(4), 769-786.
Küçüksavas¸, N., (2001). Genel Muhasebe İlkeler Ve Uygulaması, Bata Yayınları, İstanbul.
Lacher, R., Coats, P.K., Sharma, S., Fant, L.F. (1995). A Neural Network For Classifying The Financial Health Of A Firm. European Journal Of Operational Research, 85, 53-65.
Lahann, J., Scheid, M., Fettke, P. (2019, July). Utilizing Machine Learning Techniques To Reveal Vat Compliance Violations In Accounting Data. In 2019 Ieee 21st Conference On Business Informatics (Cbi) (Vol. 1, Pp. 1-10).
Liashenko, O., Kravets, T., Kostovetskyi, Y. (2023). Machine Learning And Data Balancing Methods For Bankruptcy Prediction. Ekonomika, 102(2), 28-46.
Libby, R. (1975). Accounting Ratios And The Prediction Of Failure: Some Behavioral Evidence. Journal Of Accounting Research, 13, 150-161.
Lu, Z., Zhuo, Z. (2021). Modelling Of Chinese Corporate Bond Default–A Machine Learning Approach. Accounting Finance, 61(5), 6147-6191.
Mai, F., Tian, S., Lee, C., Ma, L. (2019). Deep Learning Models For Bankruptcy Prediction Using Textual Disclosures. European Journal Of Operational Research, 274(2), 743-758.
Mcallister, J., Lennertz, L., Mojica, Z. (2021). Mapping A Discipline: A Guide To Using Vosviewer For Bibliometric And Visual Analysis. Science Technology Libraries, 41, 319- 348. Https://Doi.Org/10.1080/0194262x.2021.1991547
Mendes, A., Cardoso, R. L., Mário, P. C., Martinez, A. L., Ferreira, F. R. (2014). Insolvency Prediction In The Presence Of Data Inconsistencies. Intelligent Systems In Accounting, Finance And Management, 21(3), 155-167.
Nour, A.I., Najjar, M., Al Koni, S., Abudiak, A., Noor, M.I., Shahwan, R. (2023). The Impact Of Corporate Governance Mechanisms On Corporate Failure: An Empirical Evidence From Palestine Exchange. Journal Of Accounting In Emerging Economies.
Özbag˘, G., Esen, M., Esen, D. (2019). Bibliometric Analysis Of Studies On Social Innovation. International Journal Of Contemporary Economics And Administrative Sciences, 25-45.
Peat, M., Jones, S. (2012). Using Neural Nets To Combine Information Sets In Corporate Bankruptcy Prediction. Intelligent Systems In Accounting, Finance And Management, 19(2), 90-101.
Popa, D. C. S., Popa, D. N., Bogdan, V., Simut, R. (2021). Composite Financial Performance Index Prediction–A Neural Networks Approach. Journal Of Business Economics And Management, 22(2), 277-296.
Qu, Y., Quan, P., Lei, M., Shi, Y. (2019). Review Of Bankruptcy Prediction Using Machine Learning And Deep Learning Techniques. Procedia Computer Science, 162, 895-899.
Rahman, M. J., Zhu, H. (2023). Predicting Accounting Fraud Using Imbalanced Ensemble Learning Classifiers–Evidence From China. Accounting Finance, 63(3), 3455-3486.
Serrano-Cinca, Carlos, Gutiérrez-Nieto, Begoña And Bernate-Valbuena, Martha, (2019), The Use Of Accounting Anomalies Indicators To Predict Business Failure, European Management Journal, 37(3): 353-375.
Sevilengül, O., (2005). Genel Muhasebe, Gazi Kitabevi, Ankara.
Sharma, P., Bora, B. J. (2022). A Review Of Modern Machine Learning Techniques In The Prediction Of Remaining Useful Life Of Lithium-Ion Batteries. Batteries, 9(1), 13.
Tascón, M., Castaño, F. J. (2012). Variables And Models For The Identification And Prediction Of Business Failure: Revision Of Recent Empirical Research Advances. Spanish Accounting Review, 15(1), 7-58. Https://Doi.Org/10.1016/S1138-4891(12)70037-7
Tian, S., Yu, Y., Guo, H. (2015). Variable Selection And Corporate Bankruptcy Forecasts. Journal Of Banking And Finance, 52, 89-100.
Tinoco, M. H., Wilson, N. (2013). Financial Distress And Bankruptcy Prediction Among Listed Companies Using Accounting, Market And Macroeconomic Variables. International Review Of Financial Analysis, 30, 394-419.
Trustorff, J. H., Konrad, P. M., Leker, J. (2011). Credit Risk Prediction Using Support Vector Machines. Review Of Quantitative Finance And Accounting, 36, 565-581.
Uddin, M. S., Chi, G., Al Janabi, M. A., Habib, T., Yuan, K. (2022). Modeling Credit Risk With A Multi-Stage Hybrid Model: An Alternative Statistical Approach. Journal Of Forecasting, 41(7), 1386-1415.
Van Der Heijden, H. (2022). Predicting Industry Sectors From Financial Statements: An Illustration Of Machine Learning In Accounting Research. The British Accounting Review, 54(5), 101096.
Vu, N. T., Nguyen, N. H., Tran, T., Le, B. T., Vo, D. H. (2023). A Lasso-Based Model For Financial Distress Of The Vietnamese Listed Firms: Does The Covid-19 Pandemic Matter?. Cogent Economics Finance, 11(1), 2210361.
Wang, D., Chen, Z., Florescu, I., Wen, B. (2023). A Sparsity Algorithm For Finding Optimal Counterfactual Explanations: Application To Corporate Credit Rating. Research In International Business And Finance, 64, 101869.
Web Of Science Query Link: Https://Www.Webofscience.Com/Wos/Woscc/Summary/385d6d20-0eca-439d-Bb91-F9217d251e7a- C15c730c/Date-Descending/1 (31.12.2023).
Yao, X., Wu, D., Li, Z., Xu, H. (2024). On The Prediction Of Stock Price Crash Risk Using Textual Sentiment Of Management Statement. China Finance Review International, 14(2), 310-331.
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