El Halawany, Safaa Mohammed, Shehata, Shehata Elsayed. (2024). The Relationship between Foreign Exchange rate prediction using Artificial Intelligence and Audit Effort. المجلة العلمية للدراسات والبحوث المالية والإدارية, 16(3), 39-48. doi: 10.21608/masf.2024.373952
Safaa Mohammed El Halawany; Shehata Elsayed Shehata. "The Relationship between Foreign Exchange rate prediction using Artificial Intelligence and Audit Effort". المجلة العلمية للدراسات والبحوث المالية والإدارية, 16, 3, 2024, 39-48. doi: 10.21608/masf.2024.373952
El Halawany, Safaa Mohammed, Shehata, Shehata Elsayed. (2024). 'The Relationship between Foreign Exchange rate prediction using Artificial Intelligence and Audit Effort', المجلة العلمية للدراسات والبحوث المالية والإدارية, 16(3), pp. 39-48. doi: 10.21608/masf.2024.373952
El Halawany, Safaa Mohammed, Shehata, Shehata Elsayed. The Relationship between Foreign Exchange rate prediction using Artificial Intelligence and Audit Effort. المجلة العلمية للدراسات والبحوث المالية والإدارية, 2024; 16(3): 39-48. doi: 10.21608/masf.2024.373952
The Relationship between Foreign Exchange rate prediction using Artificial Intelligence and Audit Effort
1Assistant Lecturer, Information Systems department High Institute for Computer & Information Systems- Abi Qir Alexandria, Egypt
2Professor, Department of Accounting and Auditing, Faculty of Commerce, Alexandria University Dean of High Institute for Computer & Information Systems- Abi Qir Alexandria, Egypt
تاريخ الاستلام: 01 سبتمبر 2024،
تاريخ القبول: 01 سبتمبر 2024
المستخلص
this working paper explores how Artificial Intelligence (AI) usage to predict foreign exchange rates has an effect on audit effort in financial firms. The challenge to forecast accurately in the ever-volatile foreign exchange markets necessitates the infusion of AI algorithms, for example, machine learning and neural networks.
By using AI technology in forecasting future foreign exchange rates, accurate predictions are made leading to efficiency. Nevertheless, there are questions about the model’s complex nature and transparency which can make audits more complicated thereby increasing compliance costs, regulatory burden as well as risk management requirements. Furthermore, auditors need to consider new factors after integrating AI since they need to learn the underlying algorithms used in its design; assess data integrity during appraisal; and estimate model reliability selection when making decisions.
This working paper emphasizes the necessity of further researching into the subtle effects of AI-driven exchange rate prediction on audit practices such as their influence on planning audits or sampling techniques for a particular year or even generally the approach taken by auditors.
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