Course in English: FinTech & RegTech
Online Streaming link: https://www.twitch.tv/abclabs/ Follow us to be notified when the class starts!
Join Slido and ask questions: FinTechNTU
Project grouping: https://docs.google.com/spreadsheets/d/1bDmDhe8_K1rvUCFMi4n3NI1VVRbJ3tC1md_zUanub8k/
Course Description ("課程概述"):
1. 金融科技與監理科技就像矛與盾一般，缺一不可。金融科技與監理科技 (Financial Technology and Regulatory Technology, FinTech & RegTech) 是目前全球金融業與科技業的焦點。
2. FinTech ABCD = AI, Blockchain, Cloud, Big Data (Deloitte 2017): 隨著武漢肺炎疫情, 金融, 監理, 行動, 社群, AI雲端的發展，金融科技與 ABCD 已成為下一波提升生產力, 進化生產關係, 與創新性突破的重要元素。
3. 金融科技與監理之博弈: 雖然博弈論在1994年才獲頒Nobel Prize, 其實應用在美蘇歐已有70年，初期借用數學, 聯立方程與電腦科學, 今日則是加上ABCD: AI, Blockchain, Cloud, big Data.
4. 本課程將從分析模式、系統模式、應用模式面向探討金融科技及其博弈與 ABCD 系統。
因為不能在真空中做象牙塔式的AI，我們開場先選定一個應用: 金融科技 ABCD.
Course Description (English version):
1. FinTech & RegTech (Regulatory Technology) are like spear and shield; Both need to be studied in each other's context. FinTech and RegTech are booming.
2. FinTech ABCD = AI, Blockchain, Cloud, Big Data (Deloitte 2017): As FinTech, RegTech, mobile, social networks and AI cloud are booming, FinTech ABCD have become key element in boosting the productivity and innovation in all industries.
3. Game theory applied to FinTech and RegTech.
4. This course on FinTech consists of three aspects: Analytics, System, and Applications. Because LAP (Large application platforms such as Facebook, Android, Google Search) is important trend today, the emphasis is FinTech and RegTech, which is a system with platform and ecosystem in view.
Because System AI means that we cannot do AI in vacuum, we first select one application: FinTech ABCD.
Course Goal or "課程目標":
1. 正本清源: 金融科技本質及其博弈。金融科技與監理科技的矛與盾。
2. 創新科技: ABCD: AI, Blockchain, Cloud, big Data.
3. 關鍵趨勢: FinTech & RegTech 博弈，應用於金融, 法律, 貨幣, 經貿, 輿論, 軍事, 情報, 外交等。
4. 掌握金融科技與監理之博弈。
本課程的目標在於讓修課同學：培養同學全面掌握金融科技ABCD 應用系統之基本能力，鼓勵同學投入發展FinTech創新應用系統，有機會與國際級企業家合作落實創新構想於實際企業案例。
More intro are at 台大 fintech.
Professor: 廖世偉 (SW Liao)
Teaching assistants:
Email: abc@csie.ntu.edu.tw
Text Book
To be published in 2020.
Reference Book
Quantitative Trading, by Xin Guo, T. Lai, Howard Shek, Sam Wong, 2016.
Mining of Massive Datasets, by J. Leskovec, A. Rajaraman, J. Ullman, 3rd edition, 2019.
Grade (Note that Homework and project submissions are done via https://ceiba.ntu.edu.tw)
1. Homework: 15%
2. Midterm: 20%
3. Final project: 25%
4. Other projects: 30%
5. Quiz: 10%