DİJİTALLEŞMENİN FİNANSAL KARAR VERME SÜREÇLERİNDEKİ ROLÜ: DOLANDIRICILIK RİSKİNİN AZALTILMASINA YÖNELİK BİR DEĞERLENDİRME

Öz

Finans sektöründe dijital teknolojilerin yaygınlaşması, karar verme süreçleri ile dolandırıcılık risk yönetimini önemli ölçüde dönüştürmektedir. Bu araştırma, yapay zekâ, blokzincir, büyük veri analitiği ve robotik süreç otomasyonu teknolojilerinin finansal karar alma modellerine etkisini incelemektedir. Net Bugünkü Değer ve Maliyet-Fayda Analizi gibi geleneksel araçların dijital uygulamalarla entegrasyonu değerlendirilmiştir. Bankacılık, kamu yönetimi, kurumsal işletmeler ve FinTech sektörlerinden örnekler analiz edilerek dijital dönüşümün dolandırıcılık riskini azaltmadaki rolü ortaya konulmuştur. Bulgular, dijitalleşmenin finansal karar alma süreçlerinde doğruluk, verimlilik ve dolandırıcılığa karşı dayanıklılığı artırdığını göstermektedir.

Anahtar Kelimeler

• Yapay Zekâ
• Blokzincir
• Dijitalleşme
• Finansal Karar Verme
• Dolandırıcılık Riski
• Tahmine Dayalı Analitik
• Risk Yönetimi
• Sektörel Analiz

THE ROLE OF DIGITALIZATION IN FINANCIAL DECISION-MAKING PROCESSES: AN ASSESSMENT FOR FRAUD RISK REDUCTION

Öz

The widespread adoption of digital technologies in the financial sector is transforming decision-making processes and fraud risk management. This study examines the impact of artificial intelligence, blockchain, big data analytics, and robotic process automation on financial decision-making models. The integration of traditional tools such as Net Present Value and Cost-Benefit Analysis with digital applications is evaluated. Examples from banking, public administration, corporate enterprises, and FinTech sectors are analyzed to reveal the role of digital transformation in reducing fraud risks. Findings show that digitalization improves accuracy, efficiency, and resilience against fraud in financial decision-making processes.

Anahtar Kelimeler

• Artificial Intelligence
• Blockchain
• Digitalization
• Financial Decision-Making
• Fraud Risk
• Predictive Analytics
• Risk Management
• Sectoral Analysis

Kaynakça

1. Aktay, H., & Sarıca, Y. P. (2024). Finans sektöründe dijital dönüşüm uygulamaları ve dolandırıcılık tespiti. Social Review of Technology and Change, 2(2), 1–26.
2. Alloy. (2025). State of fraud benchmark report 2025. https://geneo.app/query-reports/fintech-fraud-trends-report-2025
3. Alles, M. G. (2015). Drivers of the use and facilitators and obstacles of the evolution to continuous monitoring and continuous auditing. Accounting Horizons, 29(2), 439-449.
4. Arner, D. W., Barberis, J., & Buckley, R. P. (2015). The evolution of FinTech: A new post-crisis paradigm? Georgetown Journal of International Law, 47, 1271.
5. Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Alarcón, E., López-Sánchez, D., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82-115.
6. Association of Certified Fraud Examiners (ACFE) (2018). Report to the Nations: Global Study On Occupational Fraud and Abuse. Association of Certified Fraud Examiner. Inc (Journal Bambang Ariyanto).
7. Aydın, S. (2025). Bulanık küme ve çok kriterli karar verme: Bibliyometrik inceleme. Current Perspectives in Social Sciences, 29(1), 66–86.
8. Baisholan, N., Dietz, J. E., Gnatyuk, S., Turdalyuly, M., Matson, E. T., & Baisholanova, K. (2025). FraudX AI: An interpretable machine learning framework for credit card fraud detection on imbalanced datasets. Computers, 14(4), 120.

9. Banazılı, A. M. (2025). Yapay zekânın kamu yönetiminde kullanımı üzerine nitel bir inceleme: Faydalar, riskler ve Türk kamu yönetimi bağlamında çıkarımlar. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 34, 538–563.
10. Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and machine learning: Limitations and opportunities. MIT press.
11. Basic, N. (2024, August 16). Tax audits using artificial intelligence in Italy. Fiscal Solutions. Retrieved from: https://www.fiscal-requirements.com/news/3177
12. Bello, H. O., Ige, A. B., & Ameyaw, M. N. (2024). Adaptive machine learning models: Concepts for real-time financial fraud prevention in dynamic environments. World Journal of Advanced Engineering Technology and Sciences, 12(02), 021-034.
13. Boztaş, H. (2023). Girişimcilik için yeni nesil finansman aracı: Kitle fonlaması (Doktora tezi). Marmara Üniversitesi.
14. Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 2053951715622512.
15. Bussmann, N., Giudici, P., & Marinelli, D. (2021). Explainable machine learning in credit risk management. Computational Economics, 57(1), 203–216.
16. Catalini, C., & Gans, J. S. (2020). Some simple economics of the blockchain. Communications of the ACM, 63(7), 80–90.
17. Cavoukian, A. (2009). Privacy by design: The 7 foundational principles. Information and Privacy Commissioner of Ontario. Canada, 5(2009), 12.
18. Central Bank of the Republic of Türkiye. (2023). Central Bank guidelines on open banking service (Turkey Law No. 6493/2013).
19. Chang, Y., Xu, Y., Cao, J., Zhang, Y., Jin, Y., & Zhu, M. (2024). Credit card fraud detection using advanced transformer model. In 2024 IEEE International Conference on Metaverse Computing, Networking, and Applications (MetaCom) (pp. 343-350). IEEE.
20. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 1165-1188.
21. Chukwudum, C. P., Ekwealor, O. U., Ikenna, U. C., Agbata, O. U., & Ibeh, C. A. (2024). Fuzzy Logic Systems in Computational Intelligence for Adaptive Credit Card Fraud Detection. International Journal of Research and Innovation in Applied Science, 9(11), 620-630.
22. Crawford, K., & Joler, V. (2018). Anatomy of an AI system. AI Now Institute.
23. Crosby, M., Pattanayak, P., Verma, S., & Kalyanaraman, V. (2016). Blockchain technology: Beyond bitcoin. Applied Innovation, 2(6-10), 71–74.
24. Daidj, N. (2022). The digital transformation of auditing and the evolution of the internal audit. HAL. Retrieved from: https://hal.science/hal-03818703
25. Dal Pozzolo, A., Caelen, O., Le Borgne, Y. A., Waterschoot, S., & Bontempi, G. (2014). Learned lessons in credit card fraud detection from a practitioner perspective. Expert Systems with Applications, 41(10), 4915-4928.
26. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
27. Davis, N. A., & Harris, S. A. (2024). Leveraging Machine Learning Models for Real-Time Fraud Detection in Financial Transactions. International Journal of Computer Technology and Science, 1(1), 56.
28. Demir, V., Özdarak, E., & Sebilcioğlu, F. (2022). İşletme yolsuzlukları. İstanbul Chamber of Certified Public Accountants.
29. Ding, X., Seleznev, N., Kumar, S., Bruss, C. B., & Akoglu, L. (2023). From explanation to action: An end-to-end human-in-the-loop framework for anomaly reasoning and management. arXiv preprint arXiv:2304.03368.
30. Dorminey, J., Fleming, A. S., Kranacher, M. J., & Riley, R. A. (2012). The evolution of fraud theory. Issues in Accounting Education, 27, 555-579.
31. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
32. Ertugrul, M., & Demir, V. (2021). The impact of IFRS adoption on information asymmetry: Evidence from takeovers. In T. Aksoy & U. Hacioglu (Eds.), Auditing ecosystem and strategic accounting in the digital era (pp. 65–81). Springer.
33. Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
34. European AI & Society Fund. (2025, July 16). How AI-driven welfare systems are deepening inequality and poverty across Europe. Retrieved from: https://europeanaifund.org/newspublications/how-ai-driven-welfare-systems-are-deepening-inequality-and-poverty-across-europe
35. European Banking Authority. (2020). Guidelines on fraud reporting under PSD2. Retrieved from: https://www.eba.europa.eu/activities/single-rulebook/regulatory-activities/payment-services-and-electronic-money/guidelines-fraud-reporting-under-psd2
36. European Commission. (2021). Proposal for a regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Retrieved from: https://digital-strategy.ec.europa.eu/en/library/proposal-regulation-laying-down-harmonised-rules-artificial-intelligence
37. Federal Trade Commission. (2025, March 10). New FTC data show a big jump in reported losses to fraud to $12.5 billion in 2024. Retrieved from: https://www.ftc.gov/news-events/news/press-releases/2025/03/new-ftc-data-show-big-jump-reported-losses-fraud-125-billion-2024
38. Financial Times. (2025, May). Increasing reliance on complex technology leaves banks vulnerable. Retrieved from: https://www.ft.com/content/c148d46f-8c6f-487e-b8d7-8e7b9eddc324
39. Finextra Research. (2024). APP Fraud Liability: A Guide for Banks. https://www.finextra.com/researcharticle/301/app-fraud-liability-a-guide-for-banks
40. Foley, S., Karlsen, J. R., & Putniņš, T. J. (2019). Sex, drugs, and bitcoin: How much illegal activity is financed through cryptocurrencies? The Review of Financial Studies, 32(5), 1798–1853.
41. Gai, K., Qiu, M., & Sun, X. (2018). A survey on FinTech. Journal of Network and Computer Applications, 103, 262–273.
42. Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM Computing Surveys (CSUR), 51(5), 1-42.
43. Hadwick, D., & Lan, S. (2021). Lessons to be learned from the Dutch childcare allowance scandal: A comparative review of algorithmic governance by tax administrations in the Netherlands, France and Germany. World Tax Journal, 13(4), 609-645.
44. Hashemi, S. K., Mirtaheri, S. L., & Greco, S. (2023). Fraud detection in banking data by machine learning techniques. IEEE Access, 11, 3034–3043.
45. HSBC. (2019, January 14). HSBC settles $250 billion of FX transactions using distributed ledger technology. Retrieved from: https://www.hsbc.com/news-and-views/news/media-releases/2019/fx-everywhere
46. Howard, R. A. (1966). Decision analysis: Applied Decision Theory.
47. Kadam, P. (2024, December). Enhancing Financial Fraud Detection with Human-in-the-Loop Feedback and Feedback Propagation. In 2024 International Conference on Machine Learning and Applications (ICMLA), 1198-1203. IEEE.
48. Karahan, Ç., & Tüfekci, A. (2019). Blokzincir teknolojisinin iç denetim faaliyetlerine etkileri: Fırsatlar ve tehditler. Denetişim, (19), 55–72.
49. Kaspersky. (2024, May 30). 36 times surge in mobile banking malware and 83% crypto phishing spike: New financial cyberthreats report by Kaspersky. Retrieved from: https://www.kaspersky.com/about/press-releases/36-times-surge-in-mobile-banking-malware-and-83-crypto-phishing-spike-new-financial-cyberthreats-report-by-kaspersky
50. Kılıçarslan, A., Kazak, H., & Akcan, A. T. (2025). Borsa İstanbul’da çok kriterli karar verme: Literatürün bibliyometrik analizi ve entelektüel yapının haritalanması.
51. Koyeda, V. (2025). Transforming Financial Operations through Robotic Process Automation. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(1), 2493–2502.
52. Köse, H. Ö., & Polat, N. (2021). Dijital dönüşüm ve denetimin geleceğine etkisi. Sayıştay Dergisi, 32(123), 9–41.
53. Kranacher, M. J., & Riley, R. (2019). Forensic accounting and fraud examination. John Wiley & Sons.
54. Kshetri, N. (2018). Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management, 39, 80–89.
55. Kulatilleke, G. K. (2022). Challenges and complexities in machine learning based credit card fraud detection. arXiv preprint arXiv:2208.10943.
56. Lacity, M. C., & Willcocks, L. P. (2016). Robotic process automation: The next transformation lever for shared services. Retrieved from The Outsourcing Unit, LSE: http://www.umsl.edu/lacitym
57. Lee, I., & Shin, Y. J. (2018). FinTech: Ecosystem, business models, investment decisions, and challenges. Business Horizons, 61(1), 35–46.
58. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.
59. McAfee, A., & Brynjolfsson, E. (2017). Machine, platform, crowd: Harnessing our digital future. WW Norton & Company.
60. Medetoğlu, B. (2024). Çok kriterli karar verme yöntemleriyle finansal performans analizi: BIST aracı kurumları üzerine uygulama. Uluslararası İktisadi ve İdari İncelemeler Dergisi, (45), 115–132.
61. Nakano, M., Takahashi, A., & Takahashi, S. (2017). Fuzzy logic-based portfolio selection with particle filtering and anomaly detection. Knowledge-Based Systems, 131, 113-124.
62. Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3), 559–569.
63. OECD. (2019). AI principles. Retrieved from: https://www.oecd.org/en/topics/ai-principles.html Organisation for Economic Co-operation and Development (OECD). (2024). Corruption and fraud in crises: Strengthening law enforcement responses in Asia and the Pacific (OECD Business and Finance Policy Papers, No. 60).
64. O'Leary, D. E., & Watkins, P. R. (1989). Review of expert systems in auditing. Expert Systems Review, 2(1), 3-22.
65. Osmani, M., El-Haddadeh, R., Hindi, N., Janssen, M., & Weerakkody, V. (2021). Blockchain for next generation services in banking and finance: cost, benefit, risk and opportunity analysis. Journal of Enterprise Information Management, 34(3), 884-899.
66. Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.
67. Perols, J. (2011). Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Auditing: A Journal of Practice & Theory, 30(2), 19–50.
68. Petticrew, M., & Roberts, H. (2008). Systematic reviews in the social sciences: A practical guide. John Wiley & Sons. PYMNTS. (2023, November 7). Smaller FinTech issuers lose nearly 60% more revenue to fraud than larger ones. Retrieved from: https://www.pymnts.com/fraud-prevention/2023/smaller-fintech-issuers-lose-nearly-60-percent-more-revenue-to-fraud-than-larger-ones
69. Phua, C., Lee, V., Smith, K., & Gayler, R. (2010). A comprehensive survey of data mining-based fraud detection research. arXiv preprint arXiv:1009.6119.
70. PwC. (2022). Global Economic Crime and Fraud Survey 2022. Retrieved from: https://www.pwc.com/gx/en/services/forensics/economic-crime-survey/2022.html
71. Reuters. (2024, March 1). Legal transparency in AI finance: facing the accountability dilemma in digital decision making. Retrieved from: https://www.reuters.com/legal/transactional/legal-transparency-ai-finance-facing-accountability-dilemma-digital-decision-2024-03-01
72. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?”: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135–1144).
73. Ripla, A. (2025, July 8). AI-powered fraud detection: Transforming security across major sectors in the digital age. Retrieved from: https://www.linkedin.com/pulse/ai-powered-fraud-detection-transforming-security-age-andre-ledde
74. Saaty, T. L. (1980). The analytic hierarchy process: Planning, priority setting, resource allocation. McGraw-Hill.
75. Sayıştay. (2023). 2022 Yılı Faaliyet Raporu. Retrieved from: https://sayistay.gov.tr/files/2701_Say%C4%B1%C5%9Ftay%202022%20Y%C4%B1l%C4%B1%20Faaliyet%20Raporu%20(2).pdf
76. Scott, R. (2024, February). Advanced fraud prevention strategies for FinTech security professionals. Verisoul. Retrieved from: https://www.verisoul.ai/articles/advanced-fraud-prevention-strategies-for-fintech-security-professionals
77. Shin, I. H., Lee, M. G., & Park, W. (2013). Implementation of the continuous auditing system in the ERP‐based environment. Managerial Auditing Journal, 28(7), 592-627.
78. Stojanović, B., Božić, J., Hofer-Schmitz, K., Nahrgang, K., Weber, A., Badii, A., Sundaram, M., Jordan, E., & Runevic, J. (2021). Follow the trail: Machine learning for fraud detection in Fintech applications. Sensors, 21(5), 1594.
79. Şahin, S. (2024). Finans sektöründe yapay zeka, makine öğrenmesi ve büyük veri kullanımı: Fırsatlar, zorluklar ve politika yapıcılar için çıkarımlar. Finans Ekonomi ve Sosyal Araştırmalar Dergisi, 9(4), 364–381.
80. Tan, E., Petit Jean, M., Simonofski, A., Tombal, T., Kleizen, B., Sabbe, M., Bechoux, L., & Willem, P. (2023). Artificial intelligence and algorithmic decisions in fraud detection: An interpretive structural model. Data & Policy, 5, e25.
81. Tazegül, A. (2025). Çok kriterli karar verme yöntemi ile finansal performans değerlendirmesi: BİST 50 şirketleri üzerine bir uygulama. Ünye İktisadi ve İdari Bilimler Fakültesi Dergisi, 7(2), 106–127.
82. Taddeo, M., & Floridi, L. (2018). How AI can be a force for good. Science, 361(6404), 751–752.
83. Thimonier, H., Popineau, F., Rimmel, A., Doan, B. L., & Daniel, F. (2023). Comparative evaluation of anomaly detection methods for fraud detection in online credit card payments. In International Congress on Information and Communication Technology (pp. 37-50). Singapore: Springer Nature Singapore.
84. Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222.
85. Türkiye Bankalar Birliği (TBB). (2022). Uzaktan müşteri edinimi istatistikleri. Retrieved from: https://www.tbb.org.tr/Content/Upload/istatistikiraporlar/ekler/3979/Uzaktan_ve_Subeden_Musteri_Edinimi_Istatistikleri-Aralik_2022.pdf
86. Türkiye Bankalar Birliği (TBB). (2023). Dijital bankacılık verileri. Retrieved from: https://www.tbb.org.tr/Content/Upload/istatistikiraporlar/ekler/4057/Dijital-Internet-Mobil_Bankacilik_Istatistikleri-Mart_2023.pdf
87. UK Government. (2024). State of digital government review. Retrieved from: https://www.gov.uk/government/publications/state-of-digital-government-review/state-of-digital-government-review.
88. Vasarhelyi, M. A., Alles, M. G., & Kogan, A. (2018). Principles of analytic monitoring for continuous assurance. In Continuous Auditing: Theory and Application (pp. 191-217). Emerald Publishing Limited.
89. Vereda, L. (2024, September 27). The missing tools in the fight against online fraud. Financial Times.
90. Verizon. (2025). Data Breach Investigations Report. Retrieved from: https://www.verizon.com/business/resources/reports/dbir
91. Vigdor, N. (2019). Apple card investigated after gender discrimination complaints. The New York Times, Retrieved from: https://www.nytimes.com/2019/11/10/business/Apple-credit-card-investigation.html.
92. Voigt, P., & Von dem Bussche, A. (2017). The EU General Data Protection Regulation (GDPR). Springer International Publishing.
93. Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a right to explanation of automated decision-making does not exist in the General Data Protection Regulation. International Data Privacy Law, 7(2), 76–99.
94. Wells, J. T. (2017). Corporate Fraud Handbook: Prevention and Detection (4th ed.). Wiley.
95. Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading digital: Turning technology into business transformation. Harvard Business Review Press.
96. Wieringa, M. (2020). What to account for when accounting for algorithms. FAT Conference.
97. Willcocks, L., Lacity, M., & Craig, A. (2017). Robotic process automation. Journal of Information Technology Teaching Cases, 7(1), 17–28.
98. World Public Sector Report. (2023). United Nations Department of Economic and Social Affairs. Retrieved from: https://desapublications.un.org/publications/world-public-sector-report-2023
99. Yu, G., & Luo, Z. (2025). Financial fraud detection using a hybrid deep belief network and quantum optimization approach. Discover Applied Sciences, 7(5), 454.
100. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.
101. Zarsky, T. Z. (2016). The trouble with algorithmic decisions. Science, Technology & Human Values, 41(1), 118–132.
102. Zetzsche, D. A., Buckley, R. P., Barberis, J. N., & Arner, D. W. (2017). Regulating a revolution: From regulatory sandboxes to smart regulation. Fordham Journal of Corporate & Financial Law, 23, 31.
103. Zetzsche, D. A., Buckley, R. P., & Arner, D. W. (2019). Regulating LIBRA: The transformative potential of Facebook’s cryptocurrency and possible regulatory responses.
104. Zetzsche, D. A., Buckley, R. P., Arner, D. W., & Barberis, J. N. (2020). From FinTech to TechFin: The regulatory challenges of data-driven finance. NYU Journal of Law & Business, 14, 393.