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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

1. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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
28. [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
29. [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
30. [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
31. [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
32. [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
33. [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
34. [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
35. [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
36. [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
37. [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