Info
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Course 2020-2021 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).


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. 

  • Machine Learning in FinTech.

  • Platform Finance: Rethinking Financial Intermediation and Financial Advice.

  • 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
  • Assess and develop smart contracts.
  • 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 with trustless blockchains.
  • Write and audit financial smart contracts.

Teaching methods
  • Face-to-face lectures
  • Online lectures
  • Individual assignments
  • Group assignments
  • Interactive class activities (role playing, business game, simulation, online forum, instant polls)
DETAILS

This course is designed for a high level of participation and interaction. We'll have online and face-to-face lectures, complemented by hands-on lab classes in which we develop prototypes of what was discussed in the lectures. We will run simulations and let our models compete against each other. Furthermore, there is a semester-long project which gives you plenty of opportunity to develop and demonstrate your own ideas. 


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 AND NOT ATTENDING STUDENTS

    With the purpose of measuring the acquisition of the above-mentioned learning outcomes, students’ assessment is based on three components:

    1. Short quizzes (25% of the final grade), open and closed questions aimed to assess students’ understanding of the core material of the course. 
    2. Individual project assignments (40% of the final grade) which aim to test students’ ability to apply the concepts from class in practice. 
    3. A final team project (35% of the final grade) aimed to validate students' ability to work as part of a team, think critically and make valuable contributions that draw on the skills acquired in class. 

     


    Teaching materials
    ATTENDING AND NOT ATTENDING STUDENTS

    All relevant teaching materials are made available via BBoard. 

    Last change 28/08/2020 15:46