Information Package / Course Catalogue
Artificial Intelligence Applications in Banking and Insurance
Course Code: BSN131
Course Type: Area Elective
Couse Group: Short Cycle (Associate's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 2
Prt.: 0
Credit: 2
Lab: 0
ECTS: 4
Objectives of the Course

This course aims to introduce artificial intelligence applications in banking and insurance sectors, enabling students to understand AI-based solutions in credit scoring, fraud detection, customer segmentation, claims management, and automated decision-making systems.

Course Content

This course comprehensively addresses the application areas of artificial intelligence and data-driven technologies in the banking and insurance sectors. The course covers the fundamental concepts of artificial intelligence, an introduction to machine learning algorithms, and methods used for the analysis of financial data.

Name of Lecturer(s)
Learning Outcomes
1.Explain basic concepts of artificial intelligence.
2.Identify AI applications in banking and insurance.
3.Interpret machine learning models used in credit risk assessment.
4.Describe fraud detection systems in insurance.
5.Evaluate the relationship between FinTech and AI.
6.Analyze basic AI-based financial applications.
Recommended or Required Reading
1.Alpaydın, E. (2020). Yapay Öğrenme: Yeni Yapay Zeka. Tellekt.
2.Çavdar, Ş. Ç., & Aydın, A. D. (2018). Finans Alanında Yapay Zeka ve Ekonometrik Uygulamalar. Ankara: Seçkin Yayıncılık.
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction to Artificial Intelligence and fundamental concepts
Week 1 - Preparation Work
The relevant section(s) of the textbook.
Week 2 - Theoretical
Data science and financial data fundamentals
Week 2 - Preparation Work
The relevant section(s) of the textbook.
Week 3 - Theoretical
Introduction to machine learning
Week 3 - Preparation Work
The relevant section(s) of the textbook.
Week 4 - Theoretical
Digital transformation in banking
Week 4 - Preparation Work
The relevant section(s) of the textbook.
Week 5 - Theoretical
Credit scoring systems
Week 5 - Preparation Work
The relevant section(s) of the textbook.
Week 6 - Theoretical
Artificial intelligence in risk management
Week 6 - Preparation Work
The relevant section(s) of the textbook.
Week 7 - Theoretical
Fraud detection systems
Week 7 - Preparation Work
The relevant section(s) of the textbook.
Week 8 - Theoretical
Artificial intelligence applications in insurance
Week 8 - Preparation Work
The relevant section(s) of the textbook.
Week 9 - Theoretical
Claims analytics and management
Week 9 - Preparation Work
The relevant section(s) of the textbook.
Week 10 - Theoretical
Customer segmentation and CRM systems
Week 10 - Preparation Work
The relevant section(s) of the textbook.
Week 11 - Theoretical
Chatbots and automation systems
Week 11 - Preparation Work
The relevant section(s) of the textbook.
Week 12 - Theoretical
FinTech and artificial intelligence integration
Week 12 - Preparation Work
The relevant section(s) of the textbook.
Week 13 - Theoretical
Ethics, data privacy (GDPR/KVKK)
Week 13 - Preparation Work
The relevant section(s) of the textbook.
Week 14 - Theoretical
General Review, Case Studies
Week 14 - Preparation Work
The relevant section(s) of the textbook.
Assessment Methods and Criteria
Type of AssessmentCountPercent
Assignment1%5
Quiz1%5
Midterm Examination1%30
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory142256
Assignment110010
Quiz1404
Midterm Examination112012
Final Examination116117
TOTAL WORKLOAD (hours)99
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
PÇ-12
PÇ-13
PÇ-14
OÇ-1
2
2
3
2
1
1
1
3
OÇ-2
4
2
3
3
4
5
3
1
1
2
2
4
OÇ-3
3
3
5
3
5
5
1
1
3
1
4
OÇ-4
3
3
2
5
3
5
5
1
1
4
1
4
OÇ-5
3
2
2
2
5
5
4
2
2
3
2
1
5
OÇ-6
2
1
2
2
3
5
5
2
2
3
1
1
5
Adnan Menderes University - Information Package / Course Catalogue
2026