For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| FIT5003 | Financial Statistics | 3 | 6 | Major | Master/Doctor | FinTech | Korean | Yes | |
| This course covers an introductory level of probability and statistical analysis for graduate students who major in finance. We emphasize topics of probability and statistical theories that students will encounter in graduate finance and econometric courses. Topics include probability theory, sampling, statistical estimation, and hypothesis testing. | |||||||||
| FIT5005 | AI & Wealth Management | 3 | 6 | Major | Master/Doctor | FinTech | English | Yes | |
| This is a course in AI & Wealth Management for FinTech Master or Ph.D. program.This is a course in AI & Wealth Management for FinTech Master or Ph.D. program.This is a course in AI & Wealth Management for FinTech Master or Ph.D. program.This is a course in AI & Wealth Management for FinTech Master or Ph.D. program.This is a course in AI & Wealth Management for FinTech Master or Ph.D. program.This is a course in AI & Wealth Management for FinTech Master or Ph.D. program.This is a course in AI & Wealth Management for FinTech Master or Ph.D. program. | |||||||||
| FIT5006 | Blockchain & Financial Application | 3 | 6 | Major | Master/Doctor | FinTech | Korean | Yes | |
| This is a course in Blockchain & Financial Application for FinTech Master or Ph.D. program.This is a course in Blockchain & Financial Application for FinTech Master or Ph.D. program.This is a course in Blockchain & Financial Application for FinTech Master or Ph.D. program.This is a course in Blockchain & Financial Application for FinTech Master or Ph.D. program. | |||||||||
| FIT5007 | AI Financial Application | 3 | 6 | Major | Master/Doctor | FinTech | - | No | |
| This is a course in AI Financial Application for FinTech Master or Ph.D. program.This is a course in AI Financial Application for FinTech Master or Ph.D. program.This is a course in AI Financial Application for FinTech Master or Ph.D. program. | |||||||||
| FIT5011 | AI Financial Data Market Analysis | 3 | 6 | Major | Master/Doctor | FinTech | - | No | |
| This is a course in AI Financial Data Market Analysis for FinTech Master or Ph.D. program.This is a course in AI Financial Data Market Analysis for FinTech Master or Ph.D. program.This is a course in AI Financial Data Market Analysis for FinTech Master or Ph.D. program.This is a course in AI Financial Data Market Analysis for FinTech Master or Ph.D. program. | |||||||||
| FIT5012 | RegTech | 3 | 6 | Major | Master/Doctor | FinTech | - | No | |
| This is a course in RegTech for FinTech Master or Ph.D. program. This is a course in RegTech for FinTech Master or Ph.D. program. This is a course in RegTech for FinTech Master or Ph.D. program. This is a course in RegTech for FinTech Master or Ph.D. program. This is a course in RegTech for FinTech Master or Ph.D. program. | |||||||||
| FIT5013 | AI Financial Platform | 3 | 6 | Major | Master/Doctor | FinTech | - | No | |
| This is a course in AI Financial Platform for FinTech Master or Ph.D. program.This is a course in AI Financial Platform for FinTech Master or Ph.D. program.This is a course in AI Financial Platform for FinTech Master or Ph.D. program.This is a course in AI Financial Platform for FinTech Master or Ph.D. program.This is a course in AI Financial Platform for FinTech Master or Ph.D. program.This is a course in AI Financial Platform for FinTech Master or Ph.D. program. | |||||||||
| FIT5015 | FinTech Marketing Issue | 3 | 6 | Major | Master/Doctor | FinTech | - | No | |
| This is a course in FinTech Marketing Issue for FinTech Master or Ph.D. program.This is a course in FinTech Marketing Issue for FinTech Master or Ph.D. program.This is a course in FinTech Marketing Issue for FinTech Master or Ph.D. program.This is a course in FinTech Marketing Issue for FinTech Master or Ph.D. program.This is a course in FinTech Marketing Issue for FinTech Master or Ph.D. program. | |||||||||
| FIT5016 | FinTech Business Model Development | 3 | 6 | Major | Master/Doctor | FinTech | - | No | |
| This is a course in FinTech Business Model Development for FinTech Master or Ph.D. program.This is a course in FinTech Business Model Development for FinTech Master or Ph.D. program.This is a course in FinTech Business Model Development for FinTech Master or Ph.D. program.This is a course in FinTech Business Model Development for FinTech Master or Ph.D. program.This is a course in FinTech Business Model Development for FinTech Master or Ph.D. program. | |||||||||
| FIT5017 | AI & Insurance Plan | 3 | 6 | Major | Master/Doctor | FinTech | - | No | |
| This is a course in AI & Insurance Plan for FinTech Master or Ph.D. program.This is a course in AI & Insurance Plan for FinTech Master or Ph.D. program.This is a course in AI & Insurance Plan for FinTech Master or Ph.D. program.This is a course in AI & Insurance Plan for FinTech Master or Ph.D. program.This is a course in AI & Insurance Plan for FinTech Master or Ph.D. program.This is a course in AI & Insurance Plan for FinTech Master or Ph.D. program.This is a course in AI & Insurance Plan for FinTech Master or Ph.D. program. | |||||||||
| FIT5019 | Machine Learning in Finance | 3 | 6 | Major | Master/Doctor | FinTech | Korean | Yes | |
| The amount of data used in financial practice is increasing, and the trend toward using unstructured data goes beyond traditional structured data. Therefore, it is very important to develop the ability to apply Machine Learning methods to deal with financial tasks in order for students to be competitive and to become leaders in the future of finance. To this end, this course aims to create the ability to use financial data in practice by using R and Python to lay the groundwork for overall data science and machine learning, from data collection, organization, analysis, visualization, machine learning, and report writing. | |||||||||
| FIT5020 | Financial Data Analysis | 3 | 6 | Major | Master/Doctor | FinTech | Korean | Yes | |
| In finance area, basic understanding and analyses of financial data is becoming more crucial. In order to implement meaningful analyses of financial data, students need to know how to get access to the key data sources as well as understand the key structure of the financial data. In this course students will learn how to get access to the key financial data (Compustat, CRSP, Fn DataGuide, etc.), how to handle the large data set, and how to analyze the data. The purpose of this course is to provide exposure to the key financial data, and to provide meaningful experience in handling and analyzing the large data set so that students can have a foundation in advanced data analyses. This course will require students to use the data handling and analyses skills to replicate the real research papers from the quality international journals. Furthermore, students are going to be required to extend the research paper that they have replicated. This will provide students to get exposure to the whole process of accessing, handling, and analyzing the financial data. | |||||||||
| FIT5021 | Data-driven Credit Modelling | 3 | 6 | Major | Master/Doctor | FinTech | Korean | Yes | |
| This course introduces credit modelling and methods of credit analysis using data-based big data and artificial intelligence technology. To understand the traditional methodologies of credit modelling, students will learn credit risk models such as structural models and reduced-form models, and credit analysis techniques through financial statement analysis. Based on the understanding of traditional credit risk analysis, students will learn credit analysis methods using machine learning techniques such as linear regression, decision tree models, support vector machine, and artificial neural network models. Artificial neural network models include convolution neural network (CNN) and recurrent neural network (RNN) models. The machine learning methodologies studied in this course are applied to default probability prediction, loss given default (LGD) prediction, credit rating classification, and portfolio and credit derivatives risk estimation. In each class, practice using Python is conducted for improving practical application skills. Lastly, the environmental changes of credit risk due to the BIS rules are introduced, and an expert from the industry will be invited to give a special lecture. | |||||||||
| FIT5022 | AI Finance Seminar I | 3 | 6 | Major | Master/Doctor | FinTech | Korean | Yes | |
| This course focuses on examining various issues in finance using various ML techniques. One of the advantages of ML techniques is that the high-dimensional nature of the methods enhance their flexibility relative to more traditional econometric prediction techniques. ML may better approximate the unknown and likely complex data processes in asset returns or corporate financial decisions. In particular, we will use machine learning techniques to estimate asset premiums, to classify various classes of companies and stocks, to check extra alphas from ML-based portfolio formation, to detect new types of risk factors, to identify stock market anomalies and suspicious trading patterns, and to extract new variables from various texts, voices and images data. We will first review the existing related finance literature for your solid theoretical background, and then apply ML techniques to those problems to see whether ML techniques can expand our understandings of traditional finance. | |||||||||
| FIT5024 | Insurtech Theory and Practice | 3 | 6 | Major | Master/Doctor | FinTech | - | No | |
| This course explores how information technology is changing prices, processes, and products in the insurance market. Specifically, students study how the technology such as machine learning and AI is changing the insurance market through lectures, case discussions, and student presentations. | |||||||||