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Scheda Riassuntiva
Anno Accademico 2020/2021
Scuola Scuola di Ingegneria Industriale e dell'Informazione
Insegnamento 055643 - FINTECH
Docente Marazzina Daniele
Cfu 8.00 Tipo insegnamento Monodisciplinare

Corso di Studi Codice Piano di Studio preventivamente approvato Da (compreso) A (escluso) Insegnamento
Ing Ind - Inf (Mag.)(ord. 270) - MI (481) COMPUTER SCIENCE AND ENGINEERING - INGEGNERIA INFORMATICA*AZZZZ055644 - FINTECH
Ing Ind - Inf (Mag.)(ord. 270) - MI (487) MATHEMATICAL ENGINEERING - INGEGNERIA MATEMATICA*AZZZZ055643 - FINTECH

Obiettivi dell'insegnamento

The aim of the course is to make students familiar with the new frontiers of Fintech. Financial technology (Fintech) is the introduction of new technologies and innovations in the traditional financial methods and services. Within the financial services industry, some of the used technologies include artificial intelligence (AI), big data, and blockchain. The aim of the course is to show use cases were these techniques are applied to specific financial problems, overcoming traditional methods. 

The course is divided in two parts: 50% on blockchain and big data, and (50%) on machine learning applications on financial problems.

 


Risultati di apprendimento attesi


Lectures and coding sessions will allow students to acquire the following competences:

- Knowledge and understanding

know some advanced concept of stochastic calculus, which are essential in quantitative finance;
know how to choose modeling assumptions to solve a financial problem properly.

- Ability in applying knowledge and understanding

know how to write a code/algorithm to solve a financial problem, exploiting the developed theoretical knowledge.

- Making judgements

find proper modeling assumption to describe a financial asset (like interest rate, volatility, commodities, etc.);
be able to judge the possible financial risks raising from using wrong modeling assumption (like misprice of financial products).

- Communication skills

to be able to express mathematical and financial concepts in a clear and rigorous way.


Argomenti trattati

Bitcoin and Blockchain Technologies  

Hash functions and elliptic curve cryptography; Blockchain and Merkle tree; Distributed consensus and mining; Transactions; Bitcoin Core; Wallets; Token; Stable coins.
 
BigData

How to use data to prevent financial problems: lapse risk in the insurance sector.
How to use data to obtain credit rate for small and medium enterprise. 

Machine Learning

How to face problems in capital markets and the energy sector via a Machine Learning approach.

 

 


Prerequisiti

Students are required to know the following topics.

- finance: knowledge of financial products (basis of credit risk, risk management techniques and derivatives).

- basic knowledge of Python (Object-oriented programming, usage of basic libraries, e.g: Pandas, NumPy, SciPy).


Modalità di valutazione

The exam consists of:
a) projects on the fintech topics;
b) projects presentation and oral exam.

The objective of projects is to let students work in groups, applying the approaches and principles taught in class. Projects will be assigned through the semester. Project artifacts are expected to be released at fixed deadlines that will be defined by the time the project will be assigned. The evaluation of projects will be based on the produced artifacts (documentation, code, …).

The oral exam is mandatory.

The exam has the goal of checking whether the student has acquired the following skills:
- knowledge of advanced concepts of stochastic calculus, which are essential in quantitative finance;
- knowledge of how to choose modeling assumptions to solve a financial problem properly;
- ability to write a code/algorithm to solve a financial problem, exploiting the developed theoretical knowledgements;
- ability to find proper modeling assumption to describe a financial asset (like interest rate, volatility, commodities, etc.);
- ability to express mathematical and financial concepts in a clear and rigorous way.


Bibliografia

Forme didattiche
Tipo Forma Didattica Ore di attività svolte in aula
(hh:mm)
Ore di studio autonome
(hh:mm)
Lezione
24:00
36:00
Esercitazione
0:00
0:00
Laboratorio Informatico
24:00
36:00
Laboratorio Sperimentale
0:00
0:00
Laboratorio Di Progetto
32:00
48:00
Totale 80:00 120:00

Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua Inglese
Disponibilità di materiale didattico/slides in lingua inglese
Disponibilità di libri di testo/bibliografia in lingua inglese
Possibilità di sostenere l'esame in lingua inglese
Disponibilità di supporto didattico in lingua inglese
schedaincarico v. 1.6.5 / 1.6.5
Area Servizi ICT
16/06/2021