ANALYSIS OF THE INCOME-BENEFIT AND COST-LOSS EFFECTS OF MACHINE LEARNING

Öz

Nowadays, large and medium-sized businesses are transforming into more data-driven structure by utilizing artificial intelligence applications to monitor and manage processes in all business functions, including decision-making processes. It is possible to analyze multi-dimensional and voluminous data sets defined as “Big Data” coming from business-centered and external sources with artificial intelligence techniques (machine learning, deep learning, etc.). As a result of ıt can be made predictions and common behavioral patterns, and can be detected systematic anomalies. In this way, firms can produce more effective solutions to any problem they encounter. The main purpose of these technologies is to provide both revenue growth and cost savings in businesses. Income increases occur in the both of monetary income and non-monetary benefits, costs occur in the form of monetary costs and non-monetary losses. The aim of this study is to reveal the monetary income-cost and non-monetary benefits and losses that machine learning projects provide to the business, and to develop a cost analysis table that can be used to analyze the costs of machine learning applications from a management accounting perspective. The table provides a comparative analysis of monetary income and cost, as well as non-monetary benefits and losses, depending on whether a company establishes its own on-premise technological infrastructure or leases infrastructure services from cloud providers (non-on-premise).Using this table, it is possible to comprehensively see the factors that, as a result of using machine learning technologies, lead to increased monetary income and non-monetary benefits, as well as the monetary costs and non-monetary losses incurred.

Anahtar Kelimeler

• Artificial intelligence
• machine learning
• machine learning costs
• machine learning revenues
• on-premise

MAKİNE ÖĞRENMESİNİN GELİR-FAYDA VE MALİYET-KAYIP ETKİSİNİN ANALİZİ

Öz

Günümüzde büyük ve orta ölçekli işletmeler, karar verme süreçleri dahil tüm işletme fonksiyonlarındaki süreçlerin izlenmesi ve yönetiminde yapay zeka uygulamalarından faydalanarak daha veri odaklı bir yapıya dönüşmektedirler. İşletme içi ve dışından gelen çok boyutlu ve hacimli “Büyük Veri” yığınlarının, yapay zekâ uygulamaları (makine öğrenmesi, derin öğrenme vb) ile analiz edilmesi sonucunda tahminlemeler yapılabilmekte, ortak davranış örüntüleri ve sistematik anomaliler vb tespit edilebilmektedir. Bu sayede işletmeler karşılaştıkları herhangi bir probleme daha etkili çözümler üretebilmektedirler. Çözüm üretirken temel amaç, hem gelir artışı hem de maliyet tasarrufu sağlayabilmektir. Gelir artışı hem parasal gelir hem de parasal olmayan fayda şeklinde gerçekleşirken, maliyetler de parasal maliyet ve parasal olmayan kayıp şeklinde meydana gelmektedir. Bu çalışmanın amacı da makine öğrenmesi projelerinin işletmeye sağladığı parasal gelir - maliyet ve parasal olmayan fayda ve kayıp unsurlarını ortaya koymak, yönetim muhasebesi açısından makine öğrenmesi uygulamalarının maliyet analizine tabi tutulabileceği maliyet analiz tablosu geliştirmektir. Tabloda işletmenin kurum içinde teknolojik alt yapıyı kendi kurması (On-Premise) ya da kurmayıp (Non On-Premise) bulut yağlayıclardan alt yapı hizmetini kiraladıkları durumlarda ortaya çıkan parasal gelir - maliyet ve parasal olmayan fayda ve kayıp unsurları karşılaştırılmalı olarak yer almaktadır. Bu tabloyu kullanarak, makine öğrenmesi teknolojilerinin kullanılması sonucu oluşacak parasal gelir-parasal olmayan fayda artışına neden olan unsurları ve meydana getirdiği parasal maliyet ve parasal olmayan kayıpları bir bütün olarak görmek mümkündür.

Anahtar Kelimeler

• Yapay Zeka
• Makine öğrenmesi
• Makine öğrenmmesi maliyetleri
• makine öğrenmesi gelirleri
• bulut kurulum maliyeti

Kaynakça

1. Ali, A., Laghari, A.A., Kandhro, I.A., Kumar, K., &Younus, S.(2024). Systematic analysis of on premise and cloud services. International Journal Cloud Computing, 13 (3), 214–242.
2. Appelbaumi,D. & Kogan,A.(2017). Big data and analytics in the modern audit engagement: research needs, Audi¬ting: A Journal Of Practice & Theory,36(4),1-27.
3. Aylak,B.L., Oral,O. &Yazıcı,K. (2021). Yapay zeka ve makine öğrenmesi tekniklerinin lojistik sektöründe kulla¬nımı. El-Cezerî Fen ve Mühendislik Dergisi, 8 (1) , 74-93.
4. Bilgiç, E., M. A. Türkmenoğlu & A. Koçak (2020). Dijitalleşmenin lojistik yönetimi bağlamında incelenmesi. BEÜ İİBF AİD., 5(1), 56-69.
5. Bezuidenhout, C.,Heffernan, T., Abbas, B., & Mehmet,M. (2022). The impact of Artificial Intelligence on the marketing practices of Professional Services Firms. Journal Of Marketıng Theory And Practıce, https://doi.org/10 .1080/10696679.2022.2090005
6. Bitter, C., Tercan,H., Meisen, T.,Bodnar,T. & Meisen,P. (2021). When to message: investigating user response prediction with machine learning for advertisement emails. 4th International Conference on Artificial Intelligence for Industries, 25-29 .
7. Browen, G. (2009). Document analysis as a qualitative research method, Qualitative Research Journal, 9 (2), 27- 40. DOI 10.3316/QRJ0902027.
8. Bhushan, A.V., Pandit,A., Kannaiah,P., Prasad,B. & Kumar (2025). personalized marketing strategies: the impact of ai and machine learning on consumer engagement. Advances in Consumer Research,2 (4), 9-17.

9. Buyya, R., Ilager, S. & Arroba, P. Energy-efficiency and sustainability in new generation cloud computing: a vision and directions for integrated management of data centre resources and workloads. Softw. Pract. Exp. 54, 24–38 (2024).
10. Climent,R.Ç., Haftor, D.M. & Staniewski,M.V. (2023). Using machine learning to create and capture value in the business models of small and medium-sized enterprises. International Journal of Information Management, https:// doi.org/10.1016/j.ijinfomgt.2023.102637
11. Chang,Y. (2022). Research on marketing data mining model based on support vector machine algorithm, Interna¬tional Conference on Artificial Intelligence and Autonomous Robot Systems, 350-354.
12. Chakrabortty, R.K., Rahman, M.H.F. & Ding, W.(2023). “ special section on developing resilient supply chains in a post-covıd pandemic era: application of artificial ıntelligent technologies for emerging ındustry 5.0”, Industrial Informatics, 19(3):3296-3299
13. Chari,H., Aswale, S., Pawar, V.N., Shetgaonkar, P. & Kumar,K.M.C. (2021).Advertisement click fraud detection using machine learning techniques. International Conference on Technological Advancements and Innovation, 109-114.
14. Chintalapati, S. & Pandey, S.K..(2022). Artificial intelligence in marketing: a systematic literature review, Interna¬tional Journal of Market Research, 64(1) 38–68. DOI: 10.1177/14707853211018428
15. Ciof,R., Travaglioni, M.,Piscitell, G., Petrillo,A. & Felice, F.D. (2020). Artificial ıntelligence and machine lear¬ning applications in smart production: progress, Trends, and Directions, Sustainability, 12, 1-26; doi:10.3390/ su1202049
16. Çayıroğlu, M. & Schlege, D. (2023). “A criteria framework for the evaluation of cloud-based machine learning, International Journal of Management Cases, 25 (1), 31-48.
17. Deloitte Access Economics, (2017). Business impacts of machine learning ,Sponsored by Google Cloud https:// www.fortunebusinessinsights.com/machine-learning-market-102226
18. Demirel,E. & Aslaner,H. (2021). Türkiye’de internet reklamcılığının vergisel boyutu, Maliye Çalışmaları Dergisi, 65, 25-48.
19. Ersöz,F. & Çınar,Y.(2021). Veri madenciliği ve makine öğrenimi yaklaşımlarının karşılaştırılması: tekstil sektö¬ründe bir uygulama”, Avrupa Bilim ve Teknoloji Dergisi, Özel Sayı 29, 397-414.
20. Garrett,M. https://tgg-accounting.com/managerial-accounting-vs-financial accounting/#:~:text=Y%C3%B6ne-tim%20muhasebesi%2C%20y%C3%B6netime%20i%C3%A7%20karar,almalar%C4%B1na%20yard%C4%-B1mc%C4%B1%20olmak%20%C3%BCzere%20tasarlanm%C4%B1%C5%9Ft%C4%B1r.
21. IDC Blogu ( https://blogs.idc.com/2024/08/16/a-deep-dive-into-idcs-global-ai-and-generative-ai-spending/)
22. Johnson, L., Callaghan, C., Balasubramanian, M., Haq, H., & Spallek, H. (2019). Cost Comparison of an On-Pre¬mise IT Solution with a Cloud-Based Solution for Electronic Health Records in a Dental School Clinic. J. Dent. Educ., 83, 895–903. [Google Scholar] [CrossRef]
23. Juhasz,Z.(2021).Quantitative cost comparison of on-premise and cloud infrastructure based EEG data processing, Cluster Comput.( https://link.springer.com/article/10.1007/s10586-020-03141-y)
24. Kacır, Ü., Çelik,S. & Kacır,Y.T. (2025). Kurumsal yönetimde dijital dönüşüm: python ile veri bilimi uygulamaları. All Sciences Academy.
25. Kasem, M. S., Hamada, M., & Taj-Eddin, I. (2023). Customer profiling, segmentation, and sales prediction using ai in direct marketing. arXiv preprint arXiv:2302.01786.
26. Küçükefe,B.(2017). Makine Öğrenmesi›nin ekonomi araştırmalarında kullanımı. Conference: Anadolu Internatio¬nal Conference in Economics V, May 11-13, Eskişehir, Turkey.
27. Lee,I., & Shin,Y.J. 2020. “Machine learning for enterprises: applications, algorithm selection, and challenges, Business Horizons, 63 (2),157-170.
28. Liu, J., Chen, Q., & Qiuc, Y.(2020). The design of erp ıntelligent sales management system. fuzzy systems and data mining VI A.J. Tallón-Ballesteros, doi:10.3233/FAIA200720
29. Maddikuntra,P.K.R.,Pham, Q.V.,Prabadevi, B.,Deepa N.,Dev,K., Gadekallu,T.R.,Ruby, R., & Liyanage,M.(2021) .Industry 5.0: a survey on enabling technologies and potential applications, Journal of Industrial Information In¬tegration, 26,1-19. https://doi.org/10.1016/j.jii.2021.1002577
30. Mahesh,B. (2018). Machine learning algorithms - a review. International Journal of Science and Research,9 (1), 381-386, DOI: 10.21275/ART20203995
31. Mauro,A., Sestino, A., & Bacconi,A. (2022). Machine learning and artifcial intelligence use in marketing: a gene¬ral taxonomy, Italian Journal of Marketing, 439–457 https://doi.org/10.1007/s43039-022-00057-w440
32. Mellema,V.(2022). Machine Learning in E-Commerce: Benefits & challenges And How to Use. https://blog. converted.in/en-us/blog/machine-learning-in-e-commerce-how-to-use-machine-learning-to-improve-your-busi¬ness-operations-and-customer-experience
33. Mitra A, Jain A, Kishore A, Kumar P (2022) A comparative study of demand forecasting models for a multi-chan¬nel retail company: a novel hybrid machine learning approach. In: Operations research forum, vol 3, no 4. Sprin¬ger. https://doi.org/10.1007/s43069-022-00166-4
34. Shobana, J., Gangadhar,C., Arora,R.K.,Renjith,P.N., Bamini,J., & Chincholkarf,Y.D. (2023). E-commerce custo¬mer churn prevention using machine learning-based business intelligence strategy. Measurement: Sensors,27,1-8.
35. Sirota,A.2025.Cloud AI vs. on-premises AI: Where should my organization run workloads?, https://www.pluralsi¬ght.com/resources/blog/ai-and-data/ai-on-premises-vs-in-cloud
36. Skansi, S. (2018). Introduction to Deep Learning: from logical calculus to artificial intelligence. Springe
37. Şahinaslan,E. (2023). İş süreci optimizasyonu: yöntem, teknoloji, riskler ve fırsatlar, Akademik İzdüşüm Dergisi, 8 (1), 570-604.
38. U.S. department of the treasury, cloud report,2023. The Financial Services Sector’s Adoption of Cloud Services
39. Volkmar , G., Fischer, P.M., & Reinecke,S. (2022). Artificial ıntelligence and machine learning: exploring drivers, barriers, and future developments in marketing management. Journal of Business Research,149, 599-614.
40. Zaheer, A.,Tahir, S.,Humayun,M., Almufareh,M.F., & Jhanjhi,Z.N. (2022). A Novel machine learning technique for fake smart watches advertisement detection. 14th International Conference On Mathematics, Actuarial Scien¬ce, Computer Science And Statistics, 1-5.
41. Zhengxin,F., Yi,Y., Jingyu,Z., Yue, L., Yuechen, M., Qinghua, L., Xiwei, X., Jeff, W., Chen, W , Shuai, Z., & Shiping, C.(2023). MLOps spanning whole machinelearninglife cycle: asurvey, https://arxiv.org/ftp/arxiv/pa¬pers/2304/2304.07296.pdf, https://doi.org/10.48550/arXiv.2304.07296
42. Zhao;Z., Li, D. & Dai,W.(2023). Machine-learning-enabled intelligence computing for crisis management in small and medium-sized enterprises (SMEs). Technological Forecasting & Social Change Journal,1-13,https://doi.or-g/10.1016/j.techfore.2023.122492
43. Zhao, Q. (2016)E-commerce product recommendation by personalized promotion and total surplus maximization. In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining (WSDM ‘16), San Francisco, CA, USA, 22–25 February
44. Zhao, Q., Zhang, Y., Friedman, D. & Tan, F.(2015). E-Commerce Recommendation with Personalized Promotion. In RecSys ‘15: Proceedings of the 9th ACM Conference on Recommender Systems, Vienna, Austria, 16 Septem¬ber, ACM: New York, NY, USA, pp. 219–226.
45. Yükçü, S., Polat, T. B. & Yükçü, C. (2021). Savaş tarihi muhasebesi: bir vaka analizi. Muhasebe ve Finans Tarihi Araştırmaları Dergisi, (2021 Özel Sayı), 11-24.
46. https://bilisim.io/2019/07/09/makine-ogrenmesinin-blok-zinciri-ile-bulusmasi/
47. https://www.pwc.com.tr/ceo-arastirmasi
48. https://www.aa.com.tr/tr/sirkethaberleri/hizmet/trendyoldan-son-3-ayda-1000-kisilik-istihdam/660338
49. https://www.sap.com/turkey/products/artificial-intelligence/what-is-generative-ai.html#:~:text=%C3%9Cret-ken%20yapay%20zeka%2C%20yeni%20verilerden,%C5%9Fekilde%20anlamas%C4%B1n%C4%B1%20 ve%20yan%C4%B1tlamas%C4%B1n%C4%B1%20sa%C4%9Flar.
50. https://synoptek.com/insights/it-blogs/data-insights/ai-ml-dl-and-generative-ai-face-off-a-comparative-analysis/
51. https://www.cloudzero.com/blog/cloud-service-providers/
52. https://www.leewayhertz.com/machine-learning-techniques/