Abstract
The use of artificial intelligence in financial accounting is gradually moving from the realm of purely scientific research to the practice of use by business entities. The results of the most popular area of artificial intelligence, namely machine learning in forecasting, error and fraud detection, are becoming commonplace and have had enormous positive results. However, in a dynamically changing economy, which is influenced by economic, political, and geopolitical indicators, not standard, but rather original, atypical solutions based not only on known algorithms but also on professional judgment and non-standard vision of an accounting specialist are of particular value. This is a promising area for the development of artificial intelligence and adds an economic and psychological component to technical and information processes
Keywords:
References
[1] Wilson, R.L., & Sharda, R. (1994). Bankruptcy prediction using neural networks. Decision Support Systems, 11(5), 545-557. doi: 10.1016/0167-9236(94)90024-8.
[2] Lacher, R.C., Coats, P.K., Sharma, S.C., & Fant, L. (1995). A neural network for classifying the financial health of a firm. European Journal of Operational Research, 85(1), 53-65. doi: 10.1016/0377-2217(93)E0274-2.
[3] Kim, K.S. (2005). Examining corporate bankruptcy: an artificial intelligence approach. International Journal of Business Performance Management, 7(3), 241.
[4] Zhai, W., Wu, G., Bao, W., & Niu, L. (2021). Big data analysis of accounting forecasting
based on machine learning. In 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP) (pp. 399-402), Xi’an: IEEE.
[5] Huang, S.M., Tsai, C.-F., Yen, D.C. & Cheng, Y.-L. (2008), A hybrid financial analysis model for business failure prediction. Expert Systems with Applications, 35(3), 1034-1040. doi: 10.1016/j.eswa.2007.08.040.
[6] Hung, D.N., Ha, H.T.V. & Binh, D.T. (2017). Application of F-score in predicting fraud, errors: experimental research in Vietnam. International Journal of Accounting and Financial Reporting, 7(2), 303-322. doi: 10.5296/ijafr.v7i2.12174.
[7] Shi, L., Xi, L., Ma, X., & Hu, X. (2009). Bagging of Artificial neural networks for bankruptcy prediction. In International Conference on Information and Financial Engineering (pp. 154-156). Singapore: IEEE.
[8] Lu, Y., Zeng, N., Liu, X., & Yi, S. (2015). A new hybrid algorithm for bankruptcy prediction using switching particle swarm optimization and support vector machines. Discrete Dynamics in Nature and Society, 2015(1), article number 294930. doi: 10.1155/2015/294930.
[9] Antunes, F., Ribeiro, B., & Pereira, F. (2017). Probabilistic modeling and visualization for bankruptcy prediction. Applied Soft Computing, 60, 831-843. doi: 10.1016/j.asoc.2017.06.043.
[10] Rainarli, E. (2019). The comparison of machine learning model to predict bankruptcy: Indonesian stock exchange data, In IOP Conference Series: Materials Science and Engineering (pp. 1-7). Bristol: IOP Publishing. doi: 10.1088/1757-899X/662/5/052019.
[11] Sehgal, S., Mishra, R.K., Deisting, F., & Vashisht, R. (2021). On the determinants and prediction of corporate financial distress in India. Managerial Finance, 47(10), 1428-1447. doi: 10.1108/MF-06-2020-0332.
[12] Kostopoulos, G., Karlos, S., Kotsiantis, S., & Tampakas, V. (2017). Evaluating active learning methods for bankruptcy prediction. In Brain Function Assessment in Learning (pp. 57-66). Patras: Springer.
[13] Jones, S., & Wang, T. (2019). Predicting private company failure: A multi-class analysis. Journal of International Financial Markets, Institutions and Money. 61, 161-188. doi: 10.1016/j.intfin.2019.03.004.
[14] Uthayakumar, J., Vengattaraman, T., & Dhavachelvan, P. (2020). Swarm intelligence based classification rule induction (CRI) framework for qualitative and quantitative approach: An application of bankruptcy prediction and credit risk analysis. Journal of King Saud University - Computer and Information Sciences, 32(6), 647-657. doi: 10.1016/j.jksuci.2017.10.007.
[15] Alexandropoulos, S.-A., Aridas, C.K., Kotsiantis, S., & Vrahatis, M.N. (2019). A deep dense neural network for bankruptcy prediction. In Pädiatrie (pp. 435-444). Cham: Springer Nature Switzerland AG.
[16] Cao, Y., Liu, X., Zhai, J., & Hua, S. (2020). A two-stage Bayesian network model for corporate bankruptcy prediction. International Journal of Finance & Economics, 27(1), 455-472. doi: 10.1002/ijfe.2162.
[17] Jang, Y., Jeong, I., & Cho, Y.K. (2020). Business failure prediction of construction contractors using a LSTM RNN with accounting, construction market, and macroeconomic variables. Journal of Management in Engineering, 36(2), article number 4019039. doi: 10.1061/(ASCE)ME.1943-5479.0000733
[18] Ding, K., Peng, X., & Wang, Y. (2019). A machine learning-based peer selection method withfinancial ratios. Accounting Horizons, 33(3), 75-87. doi: 10.2308/acch-52454.
[19] Soui, M., Smiti, S., Mkaouer, M.W., & Ejbali, R. (2020). Bankruptcy prediction using stacked autoencoders. Applied Artificial Intelligence, 34(1), 80-100. doi: 10.1080/08839514.2019.1691849.
[20] 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. doi: 10.1016/j.ejor.2018.10.024.
[21] Sisaye, S. (2021). The influence of non-governmental organizations (NGOs) on the development of voluntary sustainability accounting reporting rules. Journal of Business and SocioEconomic Development, 1(1), 5-23. doi: 10.1108/JBSED-02-2021-0017.
[22] Boussabaine, A.H., & Kaka, A.P. (1998). A neural networks approach for cost flow forecasting. Construction Management and Economics. 16(4), 471-479. doi: 10.1080/014461998372240.
[23] Karaca, I., Gransberg, D.D., & Jeong, H.D. (2020). Improving the accuracy of early cost estimates on transportation infrastructure projects. Journal of Management in Engineering, 36(5), article number 4020063. doi: 10.1061/(ASCE)ME.1943-5479.0000819.
[24] Kuzey, C., Uyar, A., & Delen, D. (2019). An investigation of the factors influencing cost system functionality using decision trees, support vector machines and logistic regression. International Journal of Accounting and Information Management, 27(1), 27-55. doi: 10.1108/IJAIM-04-2017-0052.
[25] Machuga, S.M., Pfeiffer, Jr.R.J., & Verma, K. (2002). Economic value added, future accounting earnings, and financial analysts’ earnings per share forecasts. Review of Quantitative Finance and Accounting, 18(1), 59-73. doi: 10.1023/A:1013814328460.
[26] Asquith, P. and Mullins, D.W. (1983). The impact of initiating dividend payments on shareholders’ wealth. The Journal of Business. 56(1), 77-96.
[27] Barnes, M.B., & Lee, V.C-.S. (2007). Feature selection techniques, company wealth assessment and intra-sectoral firm behaviours. In International Conference on Intelligent Computing 2007. (pp. 134-146). Berlin: Springer-Verlag London Ltd.
[28] Creamer, G., & Freund, Y. (2010). Using boosting for financial analysis and performance prediction: Application to S&P 500 companies, Latin American ADRs and banks. Computational Economics, 36(2), 133-151. doi: 10.1007/s10614-010-9205-3.
[29] Lee, S.Y., Oh, S.Y., Lee, S., & Gim, G.Y. (2021). The firm life cycle forecasting model using machine learning based on news articles. International Journal of Networked and Distributed Computing. 9(1), 1-9. doi: 10.2991/ijndc.k.201218.002.
[30] Cheng, M.-Y., & Roy, A.F. (2011). Evolutionary fuzzy decision model for cash flow prediction using time-dependent support vector machines. International Journal of Project Management, 29(1), 56-65. doi: 10.1016/j.ijproman.2010.01.004.
[31] Bahrami, M., Bozkaya, B., & Balcisoy, S. (2020). Using behavioral analytics to predict customer invoice payment. Big Data, 8(1), 25-37. doi: 10.1089/big.2018.0116.
[32] Vineeth, V.S., Kusetogullari, H., & Boone, A. (2020). Forecasting sales of truck components: a machine learning approach. In Proceedings of 2020 IEEE 10th International Conference on Intelligent Systems (pp. 510-516). Varna: IEEE. doi: 10.1109/IS48319.2020.9200128.
[33] Jang, Y., Jeong, I., & Cho, Y.K. (2020). Business failure prediction of construction contractors using a LSTM RNN with accounting, construction market, and macroeconomic variables. Journal of Management in Engineering, 36(2), article number 4019039. doi: 10.1061/(ASCE)ME.1943-5479.0000733.
[34] Choi, Y. (2021). A study of employee acceptance of artificial intelligence technology. European Journal of Management and Business Economics, 30(3), 318-330.
[35] Rahul, K., Seth, N., & Kumar, U. (2018). Spotting earnings manipulation: using machine learning for financial fraud detection. In SGAI Conferences (pp. 343-356). Cham: Springer. doi: 10.1007/978-3-030-04191-5_29.
[36] Bao, Y., Ke, B.I., Li, B.I., Yu, Y.J., & Zhang, J.I. (2020). Detecting accounting fraud in publicly traded U.S. Firms using a machine learning approach. Journal of Accounting Research, 58(1), 199-235. doi: 10.1111/1475-679X.12292.
[37] Brown, N.C., Crowley, R.M., & Elliott, W.B. (2020). What are you saying? Using topic to detect financial misreporting. Journal of Accounting Research, 58(1), 237-291.
[38] Venkatesh, V., Thong, J.Y.L., & Xu, X. (2016). Unified theory of acceptance and use of technology: a synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328-376. doi: 10.17705/1jais.00428.
[39] Bauer, K., Hinz, O., van der Aalst, W., & Weinhardt, C. (2021). Expl(AI)n it to me – explainable AI and information systems research. Business and Information Systems Engineering, 63(2), 79-82. doi: 10.1007/s12599-021-00683-2.
[40] Huttunen, J., Jauhiainen, J., Lehti, L., Nylund, A., Martikainen, M., & Lehner, O. (2019). Big data, cloud computing and data science applications in finance and accounting. ACRN Journal of Finance and Risk Perspectives, 8, 16-30.
[41] Onyshchenko, O., Shevchuk, K., Shara, Y., Koval, N., & Demchuk, O. (2022). Industry 4.0 and accounting: Directions, challenges, opportunities. Independent Journal of Management & Production, 13(3), 161-195. doi: 10.14807/ijmp.v13i3.1993.