Konferans Bildirisi
BibTex RIS Kaynak Göster

ARTIFICIAL INTELLIGENCE-BASED AUDIT SOFTWARE: TODAY'S REALITIES AND FUTURE VISION

Yıl 2024, Sayı: 31, 180 - 197, 01.12.2024
https://doi.org/10.58348/denetisim.1512650

Öz

In today's era of big data, traditional audit methods may not always be sufficient to address the complex risks faced by businesses. At this point, AI-supported audit software (AIAS) emerges as a promising solution to overcome these challenges.
This study aims to comprehensively examine the current state and future potential of AIAS, analyzing the opportunities and challenges arising from the integration of these technologies into audit processes. To achieve this, we investigate the use cases of AIAS across various audit types and sectors, assessing the benefits they offer and the challenges they present.
Additionally, by analyzing the global and local pioneers of AIAS, we identify the factors driving the development of these technologies and uncover future trends. This compilation-based study reveals that AIAS has the potential to make audit processes more efficient, effective, and reliable. However, it also emphasizes the need for careful consideration of issues such as data privacy, algorithmic bias, and ethical implications.
This study underscores the importance of collaboration among auditors, businesses, and regulators to fully harness the potential of AIAS while minimizing potential risks. It advocates investing in a continuous learning and adaptation process. Future research should delve deeper into the impact of AIAS across different sectors and develop recommendations to fully capitalize on the potential of these technologies.

Kaynakça

  • Accounting Today. (2023). How AI is transforming the accounting profession. Retrieved from https://www.accountingtoday.com/
  • Applegate, L. M., Austin, R. D., & McFarlan, F. W. (2019). Corporate information strategy and management: Text and cases. McGraw-Hill Education.
  • Arel, B., Beaudry, A., & Wood, D. A. (2023). The effects of artificial intelligence on the audit profession: A synthesis of the literature and avenues for future research. Accounting Horizons, 37(1), 177-201. AuditBoard. (2024). AuditBoard. Retrieved from https://www.auditboard.com/
  • Cao, M., Chychyla, R., & Stewart, T. (2020). Artificial intelligence in auditing: The state of play. International Journal of Accounting Information Systems, 38, 100476. https://doi.org/10.1016/j.accinf.2020.100476
  • Caseware. (2024). Caseware IDEA. Retrieved from https://www.caseware.com/
  • DataProt. (2022). AI statistics for 2022. Retrieved from https://www.dataprot.net/
  • Deloitte. (2023). Audit in the age of AI: A perspective on the evolution of audit. Retrieved from https://www2.deloitte.com/
  • Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
  • FAS. (2024). FAS Bağımsız Denetim. Retrieved from https://fas-audit.com.tr/
  • Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.b18d5be3
  • Goh, J., & Woo, G. (2023). The impact of artificial intelligence on the auditing profession: A research agenda. Journal of International Accounting, Auditing and Taxation, 48, 100483. https://doi.org/10.1016/j.intaccaudtax.2023.100483
  • HighRadius. (2024). HighRadius. Retrieved from https://www.highradius.com/
  • ICAEW. (2019). Artificial intelligence and the future of audit. Retrieved from https://www.icaew.com/
  • Issa, H., Sun, T., & Vasarhelyi, M. A. (2016). Research ideas for artificial intelligence in auditing: The formalization of audit and workforce supplementation. Journal of Emerging Technologies in Accounting, 13(2), 1-20. https://doi.org/10.2308/jeta-51494
  • Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399. https://doi.org/10.1038/s42256-019-0088-2
  • Kleinberg, J., Mullainathan, S., & Raghavan, M. (2018). Inherent trade-offs in the fair determination of risk scores. In 8th Innovations in Theoretical Computer Science Conference (ITCS 2017) (pp. 43:1-43:14). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik. https://doi.org/10.4230/LIPIcs.ITCS.2017.43
  • KPMG. (2024). KPMG AI in Control. Retrieved from https://advisory.kpmg.us/services/ai-in-control.html
  • Logo Mind. (2024). Logo Mind. Retrieved from https://www.logo.com.tr/en/product/logo-mind-insight
  • McKinsey & Company. (2023). The state of AI in 2023. Retrieved from https://www.mckinsey.com/
  • Menzies, K. (2021). The future of audit: How AI is transforming the profession. The Wall Street Journal. Retrieved from https://www.wsj.com/
  • Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity. Information & Management, 58(1), 103357. https://doi.org/10.1016/j.im.2020.103357
  • MindBridge AI. (2024). MindBridge AI Auditor. Retrieved from https://www.mindbridge.ai/
  • NewVantage Partners. (2023). Big Data and AI Executive Survey 2023. Retrieved from https://newvantage.com/
  • ProManage. (2024). ProManage. Retrieved from https://www.promanage.com/
  • Susskind, R., & Susskind, D. (2017). The future of the professions: How technology will transform the work of human experts. Oxford University Press.
  • Sutton, R. S., McAllester, D. A., Singh, S., & Mansour, Y. (2000). Policy gradient methods for reinforcement learning with function approximation. In Advances in Neural Information Processing Systems (pp. 1057-1063).
  • The Financial Times. (2023). Artificial intelligence is changing the face of audit. Retrieved from https://www.ft.com/
  • UiPath. (2024). UiPath. Retrieved from https://www.uipath.com/
  • World Economic Forum. (2020). Transforming audit with artificial intelligence. Retrieved from https://www.weforum.org/
  • Zhang, Y., Wang, H., & Dai, W. (2021). Blockchain-based data sharing scheme for artificial intelligence in industrial IoT. IEEE Transactions on Industrial Informatics, 17(6), 4170-4178.

YAPAY ZEKÂ DESTEKLİ DENETİM YAZILIMLARI: BUGÜNÜN GERÇEKLERİ VE GELECEĞİN VİZYONU

Yıl 2024, Sayı: 31, 180 - 197, 01.12.2024
https://doi.org/10.58348/denetisim.1512650

Öz

Günümüzün büyük veri çağında, geleneksel denetim yöntemleri, işletmelerin karşılaştığı karmaşık riskler karşısında her zaman yeterli olmayabilmektedir. Bu noktada, yapay zekâ destekli denetim yazılımları (YZDY), bu zorlukların üstesinden gelmede umut vadeden bir çözüm olarak öne çıkmaktadır.
Bu çalışmanın amacı, YZDY'lerin mevcut durumunu ve gelecekteki potansiyelini kapsamlı bir şekilde incelemek ve bu teknolojilerin denetim süreçlerine entegrasyonunun getirdiği fırsatları ve zorlukları analiz etmektir. Bu amaçla, YZDY'lerin farklı denetim türlerinde ve farklı sektörlerdeki kullanım örnekleri incelenmiş, sağladığı faydalar ve getirdiği zorluklar değerlendirilmiştir.
Ayrıca, YZDY'lerin küresel ve yerel pazardaki öncüleri analiz edilerek, bu teknolojilerin gelişimine yön veren faktörler ve gelecekteki trendler ortaya konmuştur. Derleme yöntemi kullanılarak yapılan bu çalışma, YZDY'lerin denetim süreçlerini daha verimli, etkili ve güvenilir hale getirme potansiyeline sahip olduğunu ortaya koymuştur. Ancak, veri gizliliği, algoritma yanlılığı ve etik gibi konuların da dikkatle ele alınması gerektiği vurgulanmıştır.
Bu çalışma, YZDY'lerin potansiyelinden tam olarak yararlanmak ve olası riskleri en aza indirmek için, denetçilerin, işletmelerin ve düzenleyicilerin iş birliği içinde çalışmasının ve sürekli öğrenme ve adaptasyon sürecine yatırım yapmasının önemini vurgulamaktadır. Gelecekteki araştırmaların, YZDY'lerin farklı sektörlerdeki etkilerini daha derinlemesine incelemesi ve bu teknolojilerin potansiyelinden tam olarak yararlanmak için çözüm önerileri geliştirmesi faydalı olabileceği sonucuna varılmıştır.

Kaynakça

  • Accounting Today. (2023). How AI is transforming the accounting profession. Retrieved from https://www.accountingtoday.com/
  • Applegate, L. M., Austin, R. D., & McFarlan, F. W. (2019). Corporate information strategy and management: Text and cases. McGraw-Hill Education.
  • Arel, B., Beaudry, A., & Wood, D. A. (2023). The effects of artificial intelligence on the audit profession: A synthesis of the literature and avenues for future research. Accounting Horizons, 37(1), 177-201. AuditBoard. (2024). AuditBoard. Retrieved from https://www.auditboard.com/
  • Cao, M., Chychyla, R., & Stewart, T. (2020). Artificial intelligence in auditing: The state of play. International Journal of Accounting Information Systems, 38, 100476. https://doi.org/10.1016/j.accinf.2020.100476
  • Caseware. (2024). Caseware IDEA. Retrieved from https://www.caseware.com/
  • DataProt. (2022). AI statistics for 2022. Retrieved from https://www.dataprot.net/
  • Deloitte. (2023). Audit in the age of AI: A perspective on the evolution of audit. Retrieved from https://www2.deloitte.com/
  • Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
  • FAS. (2024). FAS Bağımsız Denetim. Retrieved from https://fas-audit.com.tr/
  • Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.b18d5be3
  • Goh, J., & Woo, G. (2023). The impact of artificial intelligence on the auditing profession: A research agenda. Journal of International Accounting, Auditing and Taxation, 48, 100483. https://doi.org/10.1016/j.intaccaudtax.2023.100483
  • HighRadius. (2024). HighRadius. Retrieved from https://www.highradius.com/
  • ICAEW. (2019). Artificial intelligence and the future of audit. Retrieved from https://www.icaew.com/
  • Issa, H., Sun, T., & Vasarhelyi, M. A. (2016). Research ideas for artificial intelligence in auditing: The formalization of audit and workforce supplementation. Journal of Emerging Technologies in Accounting, 13(2), 1-20. https://doi.org/10.2308/jeta-51494
  • Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399. https://doi.org/10.1038/s42256-019-0088-2
  • Kleinberg, J., Mullainathan, S., & Raghavan, M. (2018). Inherent trade-offs in the fair determination of risk scores. In 8th Innovations in Theoretical Computer Science Conference (ITCS 2017) (pp. 43:1-43:14). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik. https://doi.org/10.4230/LIPIcs.ITCS.2017.43
  • KPMG. (2024). KPMG AI in Control. Retrieved from https://advisory.kpmg.us/services/ai-in-control.html
  • Logo Mind. (2024). Logo Mind. Retrieved from https://www.logo.com.tr/en/product/logo-mind-insight
  • McKinsey & Company. (2023). The state of AI in 2023. Retrieved from https://www.mckinsey.com/
  • Menzies, K. (2021). The future of audit: How AI is transforming the profession. The Wall Street Journal. Retrieved from https://www.wsj.com/
  • Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity. Information & Management, 58(1), 103357. https://doi.org/10.1016/j.im.2020.103357
  • MindBridge AI. (2024). MindBridge AI Auditor. Retrieved from https://www.mindbridge.ai/
  • NewVantage Partners. (2023). Big Data and AI Executive Survey 2023. Retrieved from https://newvantage.com/
  • ProManage. (2024). ProManage. Retrieved from https://www.promanage.com/
  • Susskind, R., & Susskind, D. (2017). The future of the professions: How technology will transform the work of human experts. Oxford University Press.
  • Sutton, R. S., McAllester, D. A., Singh, S., & Mansour, Y. (2000). Policy gradient methods for reinforcement learning with function approximation. In Advances in Neural Information Processing Systems (pp. 1057-1063).
  • The Financial Times. (2023). Artificial intelligence is changing the face of audit. Retrieved from https://www.ft.com/
  • UiPath. (2024). UiPath. Retrieved from https://www.uipath.com/
  • World Economic Forum. (2020). Transforming audit with artificial intelligence. Retrieved from https://www.weforum.org/
  • Zhang, Y., Wang, H., & Dai, W. (2021). Blockchain-based data sharing scheme for artificial intelligence in industrial IoT. IEEE Transactions on Industrial Informatics, 17(6), 4170-4178.
Toplam 30 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgi Sistemleri (Diğer)
Bölüm Makale
Yazarlar

Salahattin Altundağ 0000-0002-6198-7959

Yayımlanma Tarihi 1 Aralık 2024
Gönderilme Tarihi 9 Temmuz 2024
Kabul Tarihi 25 Eylül 2024
Yayımlandığı Sayı Yıl 2024 Sayı: 31

Kaynak Göster

APA Altundağ, S. (2024). ARTIFICIAL INTELLIGENCE-BASED AUDIT SOFTWARE: TODAY’S REALITIES AND FUTURE VISION. Denetişim(31), 180-197. https://doi.org/10.58348/denetisim.1512650

TR Dizin'de yer alan Denetişim dergisi yayımladığı çalışmalarla; alanındaki profesyoneller, akademisyenler ve düzenleyiciler arasında etkili bir iletişim ağı kurarak, etkin bir denetim ve yönetim sistemine ulaşma yolculuğunda önemli mesafelerin kat edilmesine katkı sağlamaktadır.