Info
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Course 2019-2020 a.y.

20598 - FINANCE WITH BIG DATA

DSBA
Department of Finance

Course taught in English

Go to class group/s: 31

DSBA (8 credits - I sem. - OP  |  SECS-S/06)
Course Director:
SILVIO PETRICONI

Classes: 31 (I sem.)
Instructors:
Class 31: SILVIO PETRICONI


Suggested background knowledge

To feel comfortable in this course, you should have good command of standard data science and machine learning frameworks in Python (pandas, scikit-learn). Ideally, you'd also be somewhat familiar with essential software development methods (unit testing; version control with git).


Mission & Content Summary
MISSION

The object of study in this course is FinTech, a young but rapidly growing industry that is built around innovative digital financial services. Robo-advisors, crowdlenders, blockchains, smart contracts and tokens might well shape the future of financial industry. Nevertheless, it is all but easy to devise and grow profitable FinTech business models as this poses challenging demands not only on technological skills but also on the understanding of relevant competitive, regulatory and financial dimensions. Through a mix of lectures and projects, we develop an in-depth understanding of both the technological and the financial principles that lie at the heart of this emerging industry.

CONTENT SUMMARY
  • Payments, Payment Data and PSD2.
  • Blockchains and Smart Contracts.

  • Innovative Models of Financial Intermediation and Financial Advice.

  • Alternative Data.

  • Beyond FinTech: InsurTech, RegTech.


Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • Understand key technological, strategic and regulatory aspects of new FinTech business models.
  • Identify productive uses of blockchain technology.
  • Illustrate relevant security aspects of trusted and trustless blockchain systems.
  • Describe machine learning techniques for a variety of FinTech applications.
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • Formulate statistical models for FinTech and InsurTech applications using advanced machine learning techniques.
  • Select adequate technologies, data sources and machine learning models to support a particular FinTech business idea.
  • Develop financial applications based on trusted or trustless blockchains.
  • Write, test and debug code efficiently, both in a team setting and as individual developers.

Teaching methods
  • Face-to-face lectures
  • Exercises (exercises, database, software etc.)
  • Individual assignments
  • Group assignments
DETAILS

This course is designed for a high level of participation and interaction. We'll have face-to-face lectures, complemented by hands-on lab classes in which we develop prototypes of what was discussed in the lectures, run simulations, or let our models compete against each other. Furthermore, there are a semester-long project which give you plenty of opportunity to develop and demonstrate your own ideas. Due to the high degree of in-class interactivity and extensive computer work, attendance is strongly recommended.


Assessment methods
  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  • x    
  • Individual assignment (report, exercise, presentation, project work etc.)
  • x    
  • Group assignment (report, exercise, presentation, project work etc.)
  •     x
    ATTENDING STUDENTS

    Your grade is based on individual project assignments (40%), short written quizzes with open- and closed-answer questions (25%) and a final project which can be developed either in individual work or in a group (35%). You may alternatively skip all individual assignments and quizzes, and choose to be evaluated in the same manner as a non-attending student; if you want to choose this path, you must communicate this before the due date of the first individual assignment.

    NOT ATTENDING STUDENTS

    Your grade is based on a comprehensive written final exam with open- and closed-answer questions (65%) and your submission of a final project (35%).


    Teaching materials
    ATTENDING AND NOT ATTENDING STUDENTS

    All relevant teaching materials are made available via Bboard. We use git repositories to keep our code in sync across all class participants; more detailed information about this can be found on the Bboard course page.

    Last change 05/06/2019 21:41