KA241401 FinTech

Hiroshima University Syllabus

Japanese

Academic Year 2025Year School/Graduate School School of Informatics and Data Science

Lecture Code KA241401 Subject Classification Specialized Education

Subject Name フィンテック

Subject Name
（Katakana）フィンテック

Subject Name in
English FinTech

Instructor TING HIAN ANN

Instructor
(Katakana) ティン　ヒェン　アン

Campus Higashi-Hiroshima Semester/Term 3rd-Year, First Semester, 1Term

Days, Periods, and Classrooms (1T) Thur5-8：ENG 107

Lesson Style Lecture Lesson Style
(More Details) Face-to-face

Credits 2.0 Class Hours/Week 4 Language of Instruction B : Japanese／English

Course Level 3 : Undergraduate High-Intermediate

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 02 : Information Science

Eligible Students

Keywords

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) Computer Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.

Data Science Program
（Comprehensive Abilities）
・D3. Ability to overlook social needs and issues that are intertwined in a complex manner and to solve issues with quantitative and logical thinking based on data, a multifaceted perspective, and advanced information analysis ability.

Intelligence Science Program
（Abilities and Skills）
・D2. Information processing ability and data analysis ability to contribute to the application and development of artificial intelligence and IoT.
（Comprehensive Abilities）
・D3. Ability to grasp complexly intertwined social needs and issues from a bird's-eye view and solve issues with a multifaceted perspective and analytical ability based on a wide range of knowledge in intelligent science.

Class Objectives
/Class Outline In this course, students will learn how technologies such as data science and AI informatics are changing and reshaping the world of banking and finance. In order to effectively apply these technologies, students must be well versed in the areas of application. Therefore, we will elucidate the essential concepts and frameworks of quantitative finance so that students can navigate the vast "universe" of financial products. Students will also learn about areas where data scientists play an important role, such as robo-advisors and algorithmic trading services, under the name of fintech.

Class Schedule lesson1 Introduction
lesson2 Essentials of AI and Data Science
lesson3 Mechanisms and Roles of Finance
lesson4 New Financial Business
lesson5 Uncertainty and Risk
lesson6 Fundamentals of Financial Theory
lesson7 Risk Premium
lesson8 Mid-term Test
lesson9 Fundamentals of Portfolio Theory
lesson10 Verification of Portfolio Theory
lesson11 Models of Price Fluctuation
lesson12 Monte Carlo Simulation
lesson13 FinTech 1 (Stock Price Prediction and Asset Management)
lesson14 FinTech 2 (Credit Scores and Regime Changes)
lesson15 Final Test

Mid-Term and Final Test

Mid-term test and final exam

Using Python, students will also learn how to perform practical applications (data collection).

Text/Reference
Books,etc. 1. 木島著『日経文庫：金融工学』（日本経済新聞社）
2. 木島・鈴木・後藤著『ファイナンス理論入門 – 金融工学へのプロローグ』（朝倉書店）
3. 木島・青沼著『 Excel ＆ VBA で学ぶファイナンスの数理』（金融財政事情研究会）
Reference books are used for self-study (standard in financial institutions)
Materials distributed in lectures

PC or AV used in
Class,etc. Handouts, Microsoft Teams, Zoom, moodle

(More Details)

Learning techniques to be incorporated Quizzes/ Quiz format, PBL (Problem-based Learning)/ TBL (Team-based Learning), Project Learning, Post-class Report

Suggestions on
Preparation and
Review Develop a habit of preparing in advance and ask questions during the lecture about any unclear parts.
If you do not fully understand the lecture content, reviewing is recommended, but asking the professor directly is more effective.

Requirements

Grading Method A. Comprehensive evaluation based on exercise submissions, midterm test, and assignment reports
B. Final exam
A total score of 60% or higher is required to pass.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2025Year	School/Graduate School	School of Informatics and Data Science
Lecture Code	KA241401	Subject Classification	Specialized Education
Subject Name	フィンテック
Subject Name （Katakana）	フィンテック
Subject Name in English	FinTech
Instructor	TING HIAN ANN
Instructor (Katakana)	ティン　ヒェン　アン
Campus	Higashi-Hiroshima	Semester/Term	3rd-Year, First Semester, 1Term
Days, Periods, and Classrooms	(1T) Thur5-8：ENG 107
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face

Credits	2.0	Class Hours/Week	4	Language of Instruction	B : Japanese／English
Course Level	3 : Undergraduate High-Intermediate
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	02 : Information Science
Eligible Students
Keywords
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)	Computer Science Program （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. Data Science Program （Comprehensive Abilities）・D3. Ability to overlook social needs and issues that are intertwined in a complex manner and to solve issues with quantitative and logical thinking based on data, a multifaceted perspective, and advanced information analysis ability. Intelligence Science Program （Abilities and Skills）・D2. Information processing ability and data analysis ability to contribute to the application and development of artificial intelligence and IoT. （Comprehensive Abilities）・D3. Ability to grasp complexly intertwined social needs and issues from a bird's-eye view and solve issues with a multifaceted perspective and analytical ability based on a wide range of knowledge in intelligent science.
Class Objectives /Class Outline	In this course, students will learn how technologies such as data science and AI informatics are changing and reshaping the world of banking and finance. In order to effectively apply these technologies, students must be well versed in the areas of application. Therefore, we will elucidate the essential concepts and frameworks of quantitative finance so that students can navigate the vast "universe" of financial products. Students will also learn about areas where data scientists play an important role, such as robo-advisors and algorithmic trading services, under the name of fintech.
Class Schedule	lesson1 Introduction lesson2 Essentials of AI and Data Science lesson3 Mechanisms and Roles of Finance lesson4 New Financial Business lesson5 Uncertainty and Risk lesson6 Fundamentals of Financial Theory lesson7 Risk Premium lesson8 Mid-term Test lesson9 Fundamentals of Portfolio Theory lesson10 Verification of Portfolio Theory lesson11 Models of Price Fluctuation lesson12 Monte Carlo Simulation lesson13 FinTech 1 (Stock Price Prediction and Asset Management) lesson14 FinTech 2 (Credit Scores and Regime Changes) lesson15 Final Test Mid-Term and Final Test Mid-term test and final exam Using Python, students will also learn how to perform practical applications (data collection).
Text/Reference Books,etc.	1. 木島著『日経文庫：金融工学』（日本経済新聞社） 2. 木島・鈴木・後藤著『ファイナンス理論入門 – 金融工学へのプロローグ』（朝倉書店） 3. 木島・青沼著『 Excel ＆ VBA で学ぶファイナンスの数理』（金融財政事情研究会） Reference books are used for self-study (standard in financial institutions) Materials distributed in lectures
PC or AV used in Class,etc.	Handouts, Microsoft Teams, Zoom, moodle
(More Details)
Learning techniques to be incorporated	Quizzes/ Quiz format, PBL (Problem-based Learning)/ TBL (Team-based Learning), Project Learning, Post-class Report
Suggestions on Preparation and Review	Develop a habit of preparing in advance and ask questions during the lecture about any unclear parts. If you do not fully understand the lecture content, reviewing is recommended, but asking the professor directly is more effective.
Requirements
Grading Method	A. Comprehensive evaluation based on exercise submissions, midterm test, and assignment reports B. Final exam A total score of 60% or higher is required to pass.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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