
| Course Code | : ML473 |
| Course Type | : Area Elective |
| Couse Group | : First Cycle (Bachelor's Degree) |
| Education Language | : Turkish |
| Work Placement | : N/A |
| Theory | : 3 |
| Prt. | : 0 |
| Credit | : 3 |
| Lab | : 0 |
| ECTS | : 5 |
This course aims to teach the use of artificial intelligence technologies in public finance, their effects on public financial management, and their transformative potential for the future. It provides students with knowledge of the applications of artificial intelligence, machine learning, and data analytics in tax administration, public expenditures, budgeting, and fiscal auditing processes. Furthermore, the impacts of artificial intelligence on fiscal policies and its ethical, legal, and administrative dimensions are evaluated.
The course begins with the fundamental concepts of artificial intelligence and data analytics. The process of digital transformation in public financial management is examined. Applications of artificial intelligence in tax administration, revenue forecasting, budgeting, monitoring public expenditures, and fiscal auditing are evaluated. The role of big data analytics and decision-support systems in public finance is discussed. In addition, the effects of artificial intelligence on the effectiveness of fiscal policies, along with issues of data security, ethics, and law, are examined. National and international case studies are analyzed.
| 1. | Explains the application areas of artificial intelligence technologies in public finance. |
| 2. | Analyzes the effects of artificial intelligence on tax administration, budgeting, and fiscal auditing processes. |
| 3. | Evaluates big data and data analytics applications from a public finance perspective. |
| 4. | Interprets the ethical, legal, and administrative dimensions of artificial intelligence applications. |
| 5. | Critically evaluates the effects of artificial intelligence on the effectiveness of fiscal policies. |
| 1. | Organ, İ. & Bozdoğan, D. Yapay Zekâ ve Kamu Maliyesi. Ekin Yayınevi. |
| 2. | Agrawal, A., Gans, J. & Goldfarb, A. Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Review Press. |
| Type of Assessment | Count | Percent |
|---|---|---|
| Presentation | 1 | %10 |
| Assignment | 1 | %10 |
| Midterm Examination | 1 | %20 |
| Final Examination | 1 | %60 |
| Activities | Count | Preparation | Time | Total Work Load (hours) |
|---|---|---|---|---|
| Lecture - Theory | 14 | 4 | 3 | 98 |
| Midterm Examination | 1 | 10 | 1 | 11 |
| Final Examination | 1 | 10 | 1 | 11 |
| TOTAL WORKLOAD (hours) | 120 | |||
PÇ-1 | PÇ-2 | PÇ-3 | PÇ-4 | PÇ-5 | PÇ-6 | PÇ-7 | PÇ-8 | PÇ-9 | PÇ-10 | |
OÇ-1 | 4 | 5 | 4 | 5 | 4 | 5 | 4 | 5 | 4 | 5 |
OÇ-2 | 5 | 4 | 5 | 4 | 5 | 4 | 5 | 4 | 5 | 5 |
OÇ-3 | 5 | 5 | 5 | 4 | 4 | 5 | 4 | 5 | 4 | 5 |
OÇ-4 | 5 | 4 | 5 | 4 | 5 | 4 | 5 | 4 | 5 | 4 |
OÇ-5 | 4 | 5 | 4 | 5 | 4 | 5 | 4 | 5 | 4 | 5 |