MACHINE LEARNING USE CASE DISCOVERY AND IMPLEMENTATION IN THE FINANCE AND ACCOUNTING DOMAINS OF COMPANIES

Year 2023, Volume: 9 Issue: 2, 89 - 106, 15.10.2023

Tolga Tuzcuoğlu

Abstract

This research paper presents an approach for identifying and implementing machine learning use cases in finance and accounting in an agile setting. The study aims to address the gap in the literature, which predominantly covers the individual advantages of using machine learning in accounting and finance; however, it lacks a comprehensive view of the generation of use cases in this field. Furthermore, the study provides insights for organizations in creating machine learning-driven solutions, improving productivity, attaining operational excellence, generating cost savings, and fostering profitable growth. The proposed methodology includes a comprehensive step-by-step strategy comprising 18 distinct process phases categorized into five main clusters.

Keywords

Machine Learning in Finance and Accounting, Digital Transformation, Use Case Generation, Agile Management, Corporate Finance

Ethical Statement

Makalede etik beyan gerektirecek bir husus bulunmamaktadır.

References

• [1] Achakzai, M. A. K., & Peng, J. (2023). Detecting financial statement fraud using dynamic ensemble machine learning. International Review of Financial Analysis, 89, 102827. https://doi.org/10.1016/j.irfa.2023.102827
• [2] Ahmed, S., Alshater, M. M., Ammari, A. E., & Hammami, H. (2022). Artificial intelligence and machine learning in finance: A bibliometric review. Research in International Business and Finance, 61, 101646. https://doi.org/10.1016/j.ribaf.2022.101646
• [3] Albukhitan, S. (2020). Developing Digital Transformation Strategy for Manufacturing. Procedia Computer Science, 170, 664–671. https://doi.org/10.1016/j.procs.2020.03.173
• [4] Aygün, B., & Kabakçı Günay, E. (2021). Comparison of Statistical and Machine Learning Algorithms for Forecasting Daily Bitcoin Returns. European Journal of Science and Technology, 21, 444-454. https://doi.org/10.31590/ejosat.822153
• [5] Cao, R., & Iansiti, M. (2022). Digital transformation, data architecture, and legacy systems. Journal of Digital Economy, 1(1), 1–19. https://doi.org/10.1016/j.jdec.2022.07.001
• [6] Cavalcanti, D. R., Oliveira, T., & De Oliveira Santini, F. (2022). Drivers of digital transformation adoption: A weight and meta-analysis. Heliyon, 8(2), e08911. https://doi.org/10.1016/j.heliyon.2022.e08911
• [7] Chakri, P., Pratap, S., Lakshay, & Gouda, S. K. (2023). An exploratory data analysis approach for analyzing financial accounting data using machine learning. Decision Analytics Journal, 7, 100212. https://doi.org/10.1016/j.dajour.2023.100212
• [8] Ciric, D., Lalic, B., Gracanin, D., Tasic, N., Delic, M., & Medic, N. (2019). Agile vs. Traditional Approach in Project Management: Strategies, Challenges, and Reasons to Introduce Agile. Procedia Manufacturing, 39, 1407–1414. https://doi.org/10.1016/j.promfg.2020.01.314
• [9] Consoli, S., Reforgiato Recupero, D., & Saisana, M. (Eds.). (2021). Data Science for Economics and Finance: Methodologies and Applications. Springer International Publishing. https://doi.org/10.1007/978-3-030-66891-4
• [10] Cui, L., & Wang, Y. (2023). Can corporate digital transformation alleviate financial distress? Finance Research Letters, 55, 103983. https://doi.org/10.1016/j.frl.2023.103983
• [11] Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
• [12] Evdokimov, I., Kampouridis, M., & Papastylianou, T. (2023). Application Of Machine Learning Algorithms to Free Cash Flows Growth Rate Estimation. Procedia Computer Science, 222, 529–538. https://doi.org/10.1016/j.procs.2023.08.191
• [13] Femila Roseline, J., Naidu, G., Samuthira Pandi, V., Alamelu Alias Rajasree, S., & Mageswari, Dr. N. (2022). Autonomous credit card fraud detection using machine learning approach. Computers and Electrical Engineering, 102, 108132. https://doi.org/10.1016/j.compeleceng.2022.108132
• [14] Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D. (2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32, 100577. https://doi.org/10.1016/j.jbef.2021.100577
• [15] Hoda, R., & Murugesan, L. K. (2016). Multi-level agile project management challenges: A self-organizing team perspective. Journal of Systems and Software, 117, 245–257. https://doi.org/10.1016/j.jss.2016.02.049
• [16] Lahann, J., Scheid, M., & Fettke, P. (2019). Utilizing Machine Learning Techniques to Reveal VAT Compliance Violations in Accounting Data. 2019 IEEE 21st Conference on Business Informatics (CBI), 1–10. https://doi.org/10.1109/CBI.2019.00008
• [17] Luo, Y., Cui, H., Zhong, H., & Wei, C. (2023). Business environment and enterprise digital transformation. Finance Research Letters, 57, 104250. https://doi.org/10.1016/j.frl.2023.104250
• [18] Murugan, M. S., & T, S. K. (2023). Large-scale data-driven financial risk management & analysis using machine learning strategies. Measurement: Sensors, 27, 100756. https://doi.org/10.1016/j.measen.2023.100756
• [19] Nambisan, S. (2018). Architecture vs. ecosystem perspectives: Reflections on digital innovation. Information and Organization, 28(2), 104–106. https://doi.org/10.1016/j.infoandorg.2018.04.003
• [20] Prabadevi, B., Shalini, R., & Kavitha, B. R. (2023). Customer churning analysis using machine learning algorithms. International Journal of Intelligent Networks, 4, 145–154. https://doi.org/10.1016/j.ijin.2023.05.005
• [21] Saarikko, T., Westergren, U. H., & Blomquist, T. (2020). Digital transformation: Five recommendations for the digitally conscious firm. Business Horizons, 63(6), 825–839. https://doi.org/10.1016/j.bushor.2020.07.005
• [22] Saha, D., Young, T. M., & Thacker, J. (2023). Predicting firm performance and size using machine learning with a Bayesian perspective. Machine Learning with Applications, 11, 100453. https://doi.org/10.1016/j.mlwa.2023.100453
• [23] Seify, M., Sepehri, M., Hosseinian-far, A., & Darvish, A. (2022). Fraud Detection in Supply Chain with Machine Learning. IFAC-PapersOnLine, 55(10), 406–411. https://doi.org/10.1016/j.ifacol.2022.09.427
• [24] Sigrist, F., & Leuenberger, N. (2023). Machine learning for corporate default risk: Multi-period prediction, frailty correlation, loan portfolios, and tail probabilities. European Journal of Operational Research, 305(3), 1390–1406. https://doi.org/10.1016/j.ejor.2022.06.035
• [25] Sun, G., Li, T., Ai, Y., & Li, Q. (2023). Digital finance and corporate financial fraud. International Review of Financial Analysis, 87, 102566. https://doi.org/10.1016/j.irfa.2023.102566
• [26] Thesing, T., Feldmann, C., & Burchardt, M. (2021). Agile versus Waterfall Project Management: Decision Model for Selecting the Appropriate Approach to a Project. Procedia Computer Science, 181, 746–756. https://doi.org/10.1016/j.procs.2021.01.227
• [27] Uçan, Y., & Bayazıt, N. G. (2018). Belirsizlik Koşularında Fuzzy Rough Algoritması: Kredi Skorlama da Bir Uygulama. Doğuş Üniversitesi Dergisi, 2(19), 55–64. https://doi.org/10.31671/dogus.2018.4
• [26] 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. https://doi.org/10.1016/j.bar.2022.101096
• [28] Wang, D., Li, L., & Zhao, D. (2022). Corporate finance risk prediction based on LightGBM. Information Sciences, 602, 259–268. https://doi.org/10.1016/j.ins.2022.04.058
• [29] Warner, K. S. R., & Wäger, M. (2019). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning, 52(3), 326–349. https://doi.org/10.1016/j.lrp.2018.12.001
• [30] Wiechmann, D. M., Reichstein, C., Haerting, R.-C., Bueechl, J., & Pressl, M. (2022). Agile management to secure competitiveness in times of digital transformation in medium-sized businesses. Procedia Computer Science, 207, 2353–2363. https://doi.org/10.1016/j.procs.2022.09.294
• [31] Xiao, N., Zhou, J., & Fang, X. (2023). Role of digital finance, investment, and trade in technological progress. Global Finance Journal, 57, 100853. https://doi.org/10.1016/j.gfj.2023.100853
• [32] Yeti̇z, F., Terzi̇oğlu, M., & Kayakuş, M. (2021). Makina Öğrenmesi Yöntemleri ile Türk Mevduat Bankalarının Müşteri Tahminine Yönelik Bir Uygulama. Sosyoekonomi, 29(50), 413–432. https://doi.org/10.17233/sosyoekonomi.2021.04.19
• [33] Zema, T., Kozina, A., Sulich, A., Römer, I., & Schieck, M. (2022). Deep learning and forecasting in practice: An alternative costs case. Procedia Computer Science, 207, 2958–2967. https://doi.org/10.1016/j.procs.2022.09.354
• [34] Zhai, H., Yang, M., & Chan, K. C. (2022). Does digital transformation enhance a firm's performance? Evidence from China. Technology in Society, 68, 101841. https://doi.org/10.1016/j.techsoc.2021.101841
• [35] Zhang, H., & Dong, S. (2023). Digital transformation and firms' total factor productivity: The role of internal control quality. Finance Research Letters, 57, 104231. https://doi.org/10.1016/j.frl.2023.104231
• [36] Zhang, Y., Ge, M., Yang, J., Liu, C., & Chen, X. (2023). Controlling shareholders' equity pledge, digital finance, and corporate digital transformation. International Review of Financial Analysis, 90, 102853. https://doi.org/10.1016/j.irfa.2023.102853