Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
3BNS 267Machine Learning and Artificial Intelligence in Finance2+0+02326.10.2026
Course Details
Language of Instruction Turkish
Level of Course Unit Associate Degree
Department / Program Banking and Insurance
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course To introduce the basic concepts of machine learning and artificial intelligence and to give students experience in building simple models on finance and insurance data with Python.
Course Content Basic concepts of artificial intelligence and machine learning. Supervised and unsupervised learning. Data preparation and cleaning. Introduction to Python programming. Application areas in finance: credit scoring, fraud detection and algorithmic trading. Claim prediction and risk classification in insurance. Artificial intelligence applications in customer services. Ethics of artificial intelligence, model risk and the legal framework. Hands-on building of simple models.
Course Methods and Techniques Hands-on instruction in the computer lab, applications with sample datasets, assignments and exams.
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Asist Prof.Dr.
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Python and scikit-learn online documentation (www.python.org, scikit-learn.org).

Course Category
Mathematics and Basic Sciences %30
Field %70

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Mid-terms 1 % 40
Final examination 1 % 60
Total
2
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 2 28
Assignments 14 3 42
Mid-terms 1 8 8
Final examination 1 12 12
Total Work Load   Number of ECTS Credits 3 90

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Explains the basic concepts of artificial intelligence and machine learning, and supervised and unsupervised learning.
2 Applies data preparation and cleaning with Python.
3 Builds simple machine learning models for credit scoring, fraud detection and claim prediction.
4 Evaluates application areas of artificial intelligence in finance and insurance.
5 Interprets the ethics of artificial intelligence, model risk and the legal framework.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
0 Algorithmic Trading The topics covered most recently and the topics to be covered in the new week are reviewed in advance using the related printed and digital resources and online content. Dixon, M. F., Halperin, I. & Bilokon, P. Machine Learning in Finance: From Theory to Practice. Springer.
1 Basic Concepts of Artificial Intelligence and Machine Learning Reading Dixon, M. F., Halperin, I. & Bilokon, P. Machine Learning in Finance: From Theory to Practice. Springer.
2 Supervised and Unsupervised Learning The topics covered most recently and the topics to be covered in the new week are reviewed in advance using the related printed and digital resources and online content. Dixon, M. F., Halperin, I. & Bilokon, P. Machine Learning in Finance: From Theory to Practice. Springer.
3 Introduction to Python Programming The topics covered most recently and the topics to be covered in the new week are reviewed in advance using the related printed and digital resources and online content. Dixon, M. F., Halperin, I. & Bilokon, P. Machine Learning in Finance: From Theory to Practice. Springer.
4 Data Preparation and Cleaning with Python The topics covered most recently and the topics to be covered in the new week are reviewed in advance using the related printed and digital resources and online content. Dixon, M. F., Halperin, I. & Bilokon, P. Machine Learning in Finance: From Theory to Practice. Springer.
5 Basic Classification and Regression Models The topics covered most recently and the topics to be covered in the new week are reviewed in advance using the related printed and digital resources and online content. Dixon, M. F., Halperin, I. & Bilokon, P. Machine Learning in Finance: From Theory to Practice. Springer.
6 Model Evaluation Metrics The topics covered most recently and the topics to be covered in the new week are reviewed in advance using the related printed and digital resources and online content. Dixon, M. F., Halperin, I. & Bilokon, P. Machine Learning in Finance: From Theory to Practice. Springer.
7 Application in Finance: Credit Scoring The topics covered most recently and the topics to be covered in the new week are reviewed in advance using the related printed and digital resources and online content. Dixon, M. F., Halperin, I. & Bilokon, P. Machine Learning in Finance: From Theory to Practice. Springer.
8 MIDTERM EXAM
9 Application in Finance: Fraud Detection The topics covered most recently and the topics to be covered in the new week are reviewed in advance using the related printed and digital resources and online content. Dixon, M. F., Halperin, I. & Bilokon, P. Machine Learning in Finance: From Theory to Practice. Springer.
11 Claim Prediction and Risk Classification in Insurance The topics covered most recently and the topics to be covered in the new week are reviewed in advance using the related printed and digital resources and online content. Dixon, M. F., Halperin, I. & Bilokon, P. Machine Learning in Finance: From Theory to Practice. Springer.
12 Artificial Intelligence Applications in Customer Services The topics covered most recently and the topics to be covered in the new week are reviewed in advance using the related printed and digital resources and online content. Dixon, M. F., Halperin, I. & Bilokon, P. Machine Learning in Finance: From Theory to Practice. Springer.
13 Ethics of Artificial Intelligence, Model Risk and the Legal Framework The topics covered most recently and the topics to be covered in the new week are reviewed in advance using the related printed and digital resources and online content. Dixon, M. F., Halperin, I. & Bilokon, P. Machine Learning in Finance: From Theory to Practice. Springer.
14 Hands-on Model Building Exercise The topics covered most recently and the topics to be covered in the new week are reviewed in advance using the related printed and digital resources and online content. Dixon, M. F., Halperin, I. & Bilokon, P. Machine Learning in Finance: From Theory to Practice. Springer.
15 FINAL EXAM

 
Recommended Optional Programme Components
BNS 249 Financial Modeling
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14
All 4 1 5 5 4 3 2 2 2 2 1
C1
C2
C3
C4
C5

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant