20598 - FINANCE WITH BIG DATA
Course taught in English
Go to class group/s: 31
Lezioni della classe erogate in forma blended (in parte online e in parte in presenza)
To feel comfortable in this course, you should have good command of standard data science and machine learning frameworks in Python (pandas, scikit-learn).
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.
- 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.
- 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.
- 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.
- Face-to-face lectures
- Online lectures
- Individual assignments
- Group assignments
- Interactive class activities (role playing, business game, simulation, online forum, instant polls)
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.
With the purpose of measuring the acquisition of the above-mentioned learning outcomes, students’ assessment is based on three components:
- Short quizzes (25% of the final grade), open and closed questions aimed to assess students’ understanding of the core material of the course.
- Individual project assignments (40% of the final grade) which aim to test students’ ability to apply the concepts from class in practice.
- 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.
All relevant teaching materials are made available via BBoard.