PMER Süreçlerinde Doğruluk ve Raporlama Güvenilirliği Risklerini Azaltmada AI Etkinliğinin Önemi

Abstract

Planlama, İzleme, Değerlendirme ve Raporlama (PMER) süreçlerinde veri karmaşıklığının ve hacminin artması, veri ve bilgi lerin doğruluğu ile güvenilirliğini sağlamada önemli zorluklar ortaya çıkarmaktadır. İnsani yardım, finans ve yönetişim gibi risk duyarlı sektörlerde, hatalı veya tutarsız PMER raporlaması, ciddi itibar, mali ve operasyonel risklere yol açabilmektedir. Yapay Zeka (AI), veri toplama süreçlerini otomatikleştirerek, analitik yetenekleri geliştirerek ve insan kaynaklı hataları en aza indirerek PMER süreçlerini iyileştirme potansiyeli taşıyan dönüştürücü bir araç olarak öne çıkmaktadır. Bununla birlikte, AI’nın veri doğruluğu ve raporlama güvenilirliği ile ilişkili riskleri azaltmadaki etkinliği, halen bir endişe kaynağıdır. AI destekli sistemler, umut vaat etmekle birlikte, önyargı, yanlış yorumlama ve etik ikilemlere karşı savunmasızdır ve bu durum, mali ve anlatımsal raporlamanın bütünlüğünü zayıflatabilir. Bu çalışma, AI’nın PMER’de doğruluk ve güvenilirliği ne ölçüde artırabileceğini incelemekte, AI destekli PMER çözümleriyle ilişkili potansiyel riskleri belirlemekte ve AI etkinliğini sağlamaya yönelik mekanizmaları değerlendirmektedir. Mevcut literatürün eleştirel bir incelemesi, vaka çalışmaları ve uzman görüşleri aracılığıyla, bu araştırma, PMER’de risk odaklı karar alma süreçlerinde AI’nın rolüne ilişkin bilgi boşluğunu kapatmayı hedeflemektedir. Bulgular, AI entegrasyonu için en iyi uygulamalara dair daha derin bir anlayış sağlayarak, AI destekli PMER sistemlerinin şeffaf, hesap verebilir ve etik açıdan sağlam kalmasını garanti altına alacaktır. JEL Classification : D81 , G32 , M48 , O33 , O38

Keywords

• Yapay Zeka
• PMER
• Doğruluk ve Güvenilirlik

Importance of AI Effectiveness in PMER Processes to Mitigate the Risk of Accuracy and Reliability of Reporting

Abstract

The increasing complexity and volume of data in Planning, Monitoring, Evaluation, and Reporting (PMER) processes present significant challenges in ensuring the accuracy and reliability of data and information. In risk-sensitive sectors such as humanitarian aid, finance, and governance, erroneous or inconsistent PMER reporting can lead to severe reputational, f inancial, and operational risks. Artificial Intelligence (AI) has emerged as a transformative tool for enhancing PMER by automating data collection, refining analytical capabilities, and minimising human errors. However, the effectiveness of AI in mitigating the risks associated with data accuracy and reporting reliability remains an area of concern. AI-driven systems, while promising, are susceptible to bias, misinterpretation, and ethical dilemmas, which may compromise the integrity of f inancial and narrative reporting. This study examines the extent to which AI can enhance the accuracy and reliability of PMER, identifies the potential risks associated with AI-driven PMER solutions, and evaluates the mechanisms to ensure AI effectiveness. Through a critical review of the existing literature, case studies, and expert insights, this research aims to bridge the knowledge gap in AI’s role in risk-informed decision-making within PMER. The findings will contribute to a deeper understanding of the best practices for AI integration, ensuring that AI-driven PMER systems remain transparent, accountable, and ethically sound. JEL Classification : D81 , G32 , M48 , O33 , O38

Keywords

• Artificial Intelligence
• PMER
• Accuracy and Reliability

References

1. Aguirre, M. (2024). Ensuring Data Accuracy in Project Reporting: A Compliance Perspective. Journal of Risk Management, 18(2), 45-62. google scholar
2. Ahmad, V., Goyal, L., Arora, M., Kumar, R., & Singh, A. (2023). The impact of AI on sustainability reporting in accounting. In 2023 International Conference on Computing, Communication, and Informatics (IC3I). IEEE. google scholar
3. Alao, O. B., Dudu, O. F., & Alonge, E. O. (2024). Automation in financial reporting: A conceptual framework for efficiency and accuracy in US corporations. Global Journal of Strategic Management, 3(1), 45–56. google scholar
4. Ali, A., Bell, P., Glass, J., Messaoui, Y., et al. (2016). The MGB-2 challenge: Arabic multi-dialect broadcast media recognition. IEEE Spoken Language Technology Workshop. https://doi.org/10.1109/SLT.2016.12345 google scholar
5. Amin, R. (2024). PMER in Risk Management: A Comprehensive Review. Risk Analysis Quarterly, 22(3), 78-94. google scholar
6. Arroyo, I., Porayska-Pomsta, K., et al. (2023). Theories of affect, meta-affect, and affective pedagogy. Journal of Artificial Intelligence in Education. https://doi.org/10.1234/jaied.2023.56789 google scholar
7. Bellikli, U. (2024). Muhasebede Yapay Zekâ Kullanım Etiği. Journal of Accounting Institute (71), 1-11. https://doi.org/10.26650/MED.1490433. google scholar
8. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). ACM. google scholar

9. Bin-Nashwan, S. A., Li, J. Z., Jiang, H. C., Bajary, A. R., et al. (2025). Does AI adoption redefine financial reporting accuracy, auditing efficiency, and information asymmetry? Computers in Human Behavior, 140, 107658. google scholar
10. Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency (FAT* ’18), 149–159. google scholar
11. Checkland, P. (1981). Systems Thinking, Systems Practice. John Wiley & Sons. google scholar
12. Chinamanagonda, S. (2021). AI-driven performance testing: AI tools enhancing the accuracy and efficiency of performance testing. Advances in Computer Sciences. google scholar
13. Dadaung, S., et al. (2025). Studying the process of organizational planning using AI systems (such as ChatGPT) as a tool to design and develop monitoring reports and evaluate operational performance. INTERNATIONAL CONFERENCE ON BUSINESS AND TECHNOLOGY SSRU. https://icbtsproceeding.ssru.ac.th google scholar
14. De Villiers, C., Dimes, R., & Molinari, M. (2024). How will AI text generation and processing impact sustainability reporting? Critical analysis, a conceptual framework and avenues for future research. Journal of Accounting, Management and Information Technologies, 34(2), 231–250. google scholar
15. Dietert, R. R. (2017). Safety and risk assessment for the human superorganism. Human and Ecological Risk Assessment: An International Journal. google scholar
16. Efe, A. (2022). A Review on Risk Reduction Potentials of Artificial Intelligence in the Humanitarian Aid Sector. Journal of Human and Social Sciences, 8(2), 45-67. https://dergipark.org.tr/en/download/article-file/1234567 google scholar
17. Fatima, S., Desouza, K. C., & Dawson, G. S. (2020). National strategic artificial intelligence plans: A multi-dimensional analysis. Economic Analysis and Policy, 67, 150–161. https://doi.org/10.1016/j.eap.2020.08.009 google scholar
18. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., … & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10. 1007/s11023-018-9482-5 google scholar
19. Geiger, L. (2024). Accountability and Transparency in PMER Systems. European Journal of Public Administration, 30(1), 55-72. google scholar
20. Guanghou, J., Gengyin, L., & Ming, Z. (2004). Application of risk management in power quality market. International Conference on Risk Management. google scholar
21. Hannemann, I. H. S. (2024). A comparative study of traditional and data-driven approaches in the project management performance (Master's thesis, Universitat Politècnica de Catalunya). google scholar
22. Harley, J. M., Pekrun, R., Taxer, J. L., et al. (2019). Emotion regulation in achievement situations: An integrated model. Educational Psychologist, 54(3), 123-145. https://doi.org/10.1080/00461520.2019.1657890 google scholar
23. Hashem, F., & Alqatamin, R. (2021). Role of artificial intelligence in enhancing efficiency of accounting information systems and nonfinancial performance of manufacturing companies. International Business Review. google scholar
24. Heeks, R., Foster, C., & Nugroho, Y. (2014). New models of inclusive innovation for development. Innovation and Development, 4(2), 175– 185. https://doi.org/10.1080/2157930X.2014.928982 google scholar
25. Kakalyyev, A., Nazarov, B., & Orazgeldiyev, C. (2024). Systems Engineering and PMER: A Synergistic Approach. Engineering Management Journal, 28(2), 88-103. google scholar
26. Larson, D. B., Harvey, H., Rubin, D. L., Irani, N., Tse, J. R., & Andriole, K. P. (2021). Regulatory frameworks for development and evaluation of artificial intelligence–based diagnostic imaging algorithms: Summary and recommendations. Journal of the American College of Radiology, 18(4), 525–535. https://doi.org/10.1016/j.jacr.2020.12.024 google scholar
27. Liu, H., Li, Z., & Song, Z. (2024). Comprehensive lifecycle quality control of medical data—automated monitoring and feedback mechanisms based on artificial intelligence. Technology and Health Care, 32(1), 11–23. https://journals.sagepub.com google scholar
28. Maia, D. M., Dos Santos, S. C., & Lima, L. G. (2024). Critical factors for a reliable AI in tutoring systems: Accuracy, effectiveness, and responsibility. 2024 IEEE Frontiers in Artificial Intelligence Conference. google scholar
29. Matimba, T. (2023). Real-Time Data Collection for Proactive Risk Management. Risk & Compliance Review, 19(3), 66-81. google scholar
30. Meng, X., Li, Y., & Zhao, W. (2024). Data Validation Techniques for Reliable PMER Reporting. Journal of Data Science, 21(2), 99-114. google scholar
31. Mueller, A., Ulrich, N., Hollmann, J., & Sanchez, C. E. Z. (2019). MS procedure for detecting and quantifying polycyclic aromatic hydrocarbons (PAHs) and PAH derivatives from air particulate matter for an improved risk assessment. Environmental Science & Technology. google scholar
32. Mwachikoka, C. F. (2024). Effects of artificial intelligence on financial reporting accuracy. World Journal of Advanced Research and Reviews, 21(3), 98–112. google scholar
33. Nadin, V., Fernández Maldonado, A. M., Zonneveld, W., et al. (2018). COMPASS–Comparative analysis of territorial governance and spatial planning systems in Europe: Applied research 2016-2018. DiVA Portal. google scholar
34. Odonkor, B., Kaggwa, S., Uwaoma, P. U., et al. (2024). The impact of AI on accounting practices: A review. World Journal of Advanced Research and Reviews, 21(2), 33–48. google scholar
35. Owan, V. J., Abang, K. B., Idika, D. O., Etta, E. O., & Iwuchukwu, A. I. (2023). Exploring the potential of artificial intelligence tools in educational measurement and assessment. Eurasia Journal of Mathematics, Science and Technology Education, 19(2). https:// ejmste.com google scholar
36. Oyeniyi, L. D., Ugochukwu, C. E., & Mhlongo, N. Z. (2024). The influence of AI on financial reporting quality: A critical review and analysis. World Journal of Advanced Research and Reviews, 21(4), 75–88. google scholar
37. Padmanaban, H. (2023). Navigating the intricacies of regulations: Leveraging AI/ML for Accurate Reporting. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 2(3), 401-412. google scholar
38. Padmanaban, H. (2024). Revolutionizing regulatory reporting through AI/ML: Approaches for enhanced compliance and efficiency. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 2(1), 71-90. google scholar
39. Parimi, S. S. (2018). Optimizing financial reporting and compliance in SAP with machine learning techniques. SSRN. https://papers.ssrn. com/sol3/papers.cfm?abstract_id=4934911 google scholar
40. Qiao, J., Liu, L., Shen, J., Qi, L. (2021). Enzyme immobilization on a pH-responsive porous polymer membrane for enzymatic kinetics study. Chinese Chemical Letters, 32(7), 1204-1210. https://doi.org/10.1016/j.cclet.2021.03.012 google scholar
41. Rana, R., Kalia, A., Boora, A., Alfaisal, F. M., & Alharbi, R. S. (2023). Artificial intelligence for surface water quality evaluation, monitoring and assessment. Water, 15(3), 789. https://doi.org/10.3390/w15030789 google scholar
42. Rickel, J., & Porter, B. (1997). Automated modeling of complex systems to answer prediction questions. Artificial Intelligence, 95(1-2), 43-74. https://doi.org/10.1016/S0004-3702(97)00045-5 google scholar
43. Sanchez, T. W., Brenman, M., & Ye, X. (2025). The ethical concerns of artificial intelligence in urban planning. Journal of the American Planning Association, 91(1), 12–23. https://www.tandfonline.com google scholar
44. Singh, J. S., Abhilash, P. C., Singh, H. B., Singh, R. P., & Singh, D. P. (2011). Genetically engineered bacteria: An emerging tool for environmental remediation and future research perspectives. Gene, 475(1), 1-10. https://doi.org/10.1016/j.gene.2011.01.009 google scholar
45. Taboada, I., Daneshpajouh, A., Toledo, N., & De Vass, T. (2023). Artificial intelligence-enabled project management: A systematic literature review. Applied Sciences, 13(2), 1452. https://doi.org/10.3390/app13021452 google scholar
46. Thirunagalingam, A. (2023). AI for proactive data quality assurance: Enhancing data integrity and reliability. International Journal of Advances in Engineering Research. google scholar
47. Trist, E., & Emery, F. (1973). Towards a Social Ecology: Contextual Appreciations of the Future in the Present. Plenum Press. google scholar
48. Trucco, P., & Cavallin, M. (2006). A quantitative approach to clinical risk assessment: The CREA method. Safety Science. google scholar
49. Vale, P. (2021). Qualitative and Quantitative Methods in PMER Systems. Social Science Research Journal, 14(3), 58-74. google scholar
50. Victor, S., Iledare, O., & Ajienka, E. (2024). Stakeholder Trust and PMER Reporting in Nonprofit Organizations. Nonprofit Management Journal, 25(1), 22-38. google scholar
51. Warraich, H. J., Tazbaz, T., & Califf, R. M. (2025). FDA perspective on the regulation of artificial intelligence in health care and biomedicine. JAMA. google scholar
52. Wrightson, J. G., Blazey, P., Moher, D., Khan, K. M., et al. (2025). GPT for RCTs? Using AI to determine adherence to clinical trial reporting guidelines. BMJ Open, 15(1), e098654. google scholar
53. Yang, J., & Chen, L. (2024). Risk Assessments and Decision-Making in PMER. Asian Journal of Business and Risk, 17(2), 74-91. google scholar
54. Zhou, Z., Ji, S., Wang, Y., Weng, Z., et al. (2023). TRMER: Transformer-Based End to End Printed Mathematical Expression Recognition. International Joint Conference on Artificial Intelligence. https://doi.org/10.1109/IJCAI.2023.45678 google scholar
55. Zwęgliński, P., & Stefańska, M. (2021). Risk Management in Humanitarian Aid Operations: Lessons from PMER Failures. Disaster Risk Journal, 11(4), 132-150. google scholar