FINTECH: TECHNOLOGY FOR FINANCE AND INSURANCE

Modern finance could not exist without the support of technologies from other areas. Among such technologies, a role of increasing importance is played by the so-called Artificial Intelligence, in its various meanings. The course aims to provide knowledge on intelligent methodologies, generally inspired by the problem solving skills typical of higher living beings, for the solution of problems of interest in the financial context. For example, one presents intelligent metauristics which are inspired to the principles of natural evolution for solving complex portfolio selection problems, one introduces predictive methods which are inspired to the working of the biological brain, and one presentes techniques for financial trading based on techniques of machine learning which are inspired to the ways of learning of the higher living beings.
Software tools are also used in the course to implement such methodologies.

1. Knowledge and understanding:
1.1. technically knowing the intelligent methodologies presented in the course;;
1.2. understanding and being able to use such methodologies for solving financial problems.

2. Ability to apply knowledge and understanding:
2.1. applying the appropriate intelligent methodologies for solving operational problems;
2.2. implementing financial calculations through software tools.

3. Judgement skill:
3.1. interpreting the results coming from the computations;
3.2. understanding pros and cons of the intelligent methodologies learned.

Keeping fresh in mind the educational objectives of the following teachings of the bachelor's degree programme in the economic area: Mathematics - 1, Mathematics - 2, Computing Skills for Economics. Furthermore, it is preferable to have some software programming experience.

- Introduction to Artificial Intelligence in finance and in insurance.
- Intelligent metaheuristics for complex optimization and financial/insurance applications.
- Supervised learning (Perceptron, Adaline, Madaline and Multi Layer Perceptron) and financial/insurance applications.
- Group Method of Data Handling and financial/insurance applications.
- Reinforcement Learning and financial/insurance applications.
- Implementations in Matlab.

- Teaching materials available at the web page of the e-learning platform Moodle. [Reference materials]

- Alpaydin E. (2014) Introduction to Machine Learning. The MIT Press [Integrative reading]

The exam consists in three homeworks and in an oral examination.
The homeworks: 1) must be carried out in couple; 2) are valid for the whole academic year and not beyond; 3) their carrying out must be sent no later than a pre-established deadline (the way of sending and the deadline will be indicated during the course).
Regarding the oral examination: 1) it must be carried out individually; 2) it is divided into three parts: in the first part one has to critically present a research article; in the second part one has to apply one or more methodologies learned during the course to reply the results presented in the research paper; in the third part one has has to answer some questions about one topic among three arguments dealt with in the course, all three chosen by the student her/himself.
Concerning the evaluation: 1) each homework is worth 0 to 3 possible points, for a total from 0 to 12 points; 2) the oral examination is worth 0 to 18 possible points.
The sum of the points obtained from the homeworks and from the oral examination constitutes the final mark.

The course is articulated into:
a) lectures;
b) implementation and use of intelligent methdologies through software tools;
c) individual study.
Students are strongly encouraged to actively attend classes.

English

Site of the course present on the e-learning platform Moodle.

oral

This subject deals with topics related to the macro-area "Poverty and inequalities" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development