Financial Services and Fintech Data Case Study

Financial Services and Fintech Data Case Study

Course Arrangement

In recent years, due to the combination of Finance and Technology, the rules and appearance of fund operations and transactions have been changed. It makes Financial Technology (FinTech) turn into an important issue in countries around the world, and also the focal point in current financial and technology industry. In 2015, the World Economic Forum (WEF) proposed a clear development framework of “six functional areas, eleven groups of innovations”, portraying the financial environment that FinTech may bring to the future. The six functional areas are Lending, investment management, insurance, payment, fundraising, and marketing. In order to response to the wave of FinTech development, the FSC launched a series of financial technology-related policies in 2015 which has been regarded as the first year of Taiwan’s financial technology. Therefore, Stanford, Princeton, MIT, Duke, NYU, NTU Famous schools at home and abroad have opened courses in the field of it. Recently, the technical level of FinTech has developed into data analysis of artificial intelligence which is mainly a product of the development (including massive data analysis, cloud computing, and intelligent hard drive), artificial intelligence, and blockchain technology. it mainly covers the investment, lending, insurance, and credit reporting industries. Related technologies have become the basis for business development and support the innovation of financial products, including new types of insurance and investment products.

Teaching Goals

The goal of the course is designed to enable students to know the current status and future trends of the development of the financial technology field, and identify the needs and pain points of the current financial consumer to propose feasible solutions. Take the case data set as an example to learn how to use the R program to do data processing, and lead the students to solve the problems being raised by the case through the machine (deep) learning method.

Course Syllabus

PART-I Current Status and Future Trends of Fintech
  • Current status of financial technology development
  • Fintech solutions
  • Future development of financial technology
PART-II Fintech Case Study Implementation
  • Robo-advisor implementation
  • Implementation of a case study of insurance customer renewal amount forecast (Source: T-Brain)
  • House Prices: Advanced Regression Techniques個案實作(資料來源：Kaggle) Implementation of a case study of House Prices: Advanced Regression Techniques (Source: Kaggle)
  • Implementation of a case study of Home Credit Default Risk (Source: Kaggle)
  • Implementation of a case study of The Winton Stock Market Challenge (Source: Kaggle)

Self-Made Materials

This course does not use textbooks, we will build on past self-made teaching materials, according to the needs of the curriculum re-preparation of teaching materials. The teaching materials include:

  • Course Website：Integrate self-made teaching materials and online resources to promote mutual learning (HTML)
  • Preview Video：To help students understand online preview content
  • Material：PowerPoint slide of every unit
  • In-class Note：In-class R-Notebook of every unit
  • Homework Note：Homework R-Notebook of every unit
  • Online Simulate Program：Help students understand more complex concepts in an interactive simulation (R: Shiny)
  • Class Video： It is convenient for students to review or make up course. (YouTube)

Business Analytics Platform from College of Management, NSYSU

This course will use more than 300 million data set. Students can use the big data computing resources in Business Analytics Platform from College of Management, NSYSU via the student account, including:

  • All large data sets will be arranged in the Hadoop network file system in advance
  • Big data can be loaded into the Spark Integrated Analysis Engine to process the data by the data frame interface,
  • Big data can also be loaded into the GreenPlum distributed database and processed by the SQL interface
  • In addition to the data frame and SQL interface, the Integrated Analysis Platform also provides data uploading and exploration functions.
  • Through the platform, you can use a high-end interface (Keras) to use a GPU-equipped deep computing host to create artificial intelligence models
  • When the user logs in for the first time, user can use the above resources through the demo program under the root directory (exercise)

Users can upload and analyze corpus data in a semi-automated manner through the Text Analysis platform. Recently, the text platform has stored text data on most pages of the PTT website in the past 10 years. Users can directly use keywords and dates to filter PO Text content and analysis

Online Course

We take the following online courses (MOOC’s) as references:

We will choose appropriate teaching units, homework exercise or case study as preview materials or cite it in our teaching materials from those online course.

Open Resource Reference

This course will collect the data sets form the following open data websites to edit and manage data cases: