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

Ahmet Efe

Makale detayları

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

Yazarlar:

Ahmet Efe

Yayıncı:

Istanbul University

Ana Başlıklar:

İş Sistemleri (Diğer) | Mali Tablo Analizi | Sürdürülebilirlik Muhasebesi ve Raporlama

Dergi:

Muhasebe Enstitüsü Dergisi Issue 73, pp.45-60

Belge Tipi:

Makale

Yayın Tarihi:

8/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001360066

pISSN:

2602-3202

Anahtar kelimeler:

Özet:

 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 AIs 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.

 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 AIs 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.

 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 AIs 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.

 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, AInın 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, AInın PMERde 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, PMERde risk odaklı karar alma süreçlerinde AInın 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.

Makale detayları

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

Yazarlar:

Ahmet Efe

Yayıncı:

Istanbul University

Ana Başlıklar:

İş Sistemleri (Diğer) | Mali Tablo Analizi | Sürdürülebilirlik Muhasebesi ve Raporlama

Dergi:

Muhasebe Enstitüsü Dergisi Issue 73, pp.45-60

Belge Tipi:

Makale

Yayın Tarihi:

8/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001360066

pISSN:

2602-3202

Artificial Intelligence

PMER

Accuracy and ReliabilityYapay Zeka

PMER

Doğruluk ve Güvenilirlik

Sayı

Makale detayları

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

Yazarlar: Ahmet Efe

Yayıncı: Istanbul University

Ana Başlıklar: İş Sistemleri (Diğer) | Mali Tablo Analizi | Sürdürülebilirlik Muhasebesi ve Raporlama

Dergi: Muhasebe Enstitüsü Dergisi Issue 73, pp.45-60

Belge Tipi: Makale

Yayın Tarihi: 8/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001360066

pISSN: 2602-3202

Artificial Intelligence

PMER

Accuracy and ReliabilityYapay Zeka

PMER

Doğruluk ve Güvenilirlik

Sayı

Yazarlar:

Ahmet Efe

Yayıncı:

Istanbul University

Ana Başlıklar:

İş Sistemleri (Diğer) | Mali Tablo Analizi | Sürdürülebilirlik Muhasebesi ve Raporlama

Dergi:

Muhasebe Enstitüsü Dergisi Issue 73, pp.45-60

Belge Tipi:

Makale

Yayın Tarihi:

8/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001360066

pISSN:

2602-3202