FINTECH: TECHNOLOGY FOR FINANCE AND INSURANCE

How are automated trading systems designed? How can Artificial Intelligence support portfolio selection and financial forecasting? Which technologies are transforming the worlds of finance and insurance?

This course provides a practical and application-oriented introduction to some of the most innovative technologies currently used in the FinTech industry. Particular emphasis is placed on Artificial Intelligence and Machine Learning, two fields that are reshaping the way complex problems related to investment, risk management, and decision support are addressed.
Throughout the course, students will explore intelligent methodologies inspired by learning and adaptation mechanisms observed in nature. Topics include evolutionary and swarm-intelligence-based metaheuristics for portfolio optimization, Artificial Neural Networks for financial forecasting, and Reinforcement Learning techniques for the development of automated trading strategies.
The course goes beyond theory. Students will have the opportunity to use dedicated software tools to implement and experiment with the methodologies presented, gaining hands-on experience and skills that are valuable both in industry and in research. No prior expertise in Artificial Intelligence is required: the course is designed to guide students step by step through the technologies that are helping shape the future of finance and insurance.

1. Knowledge and understanding:
1.1. To grasp the theoretical aspects of intelligent methodologies and machine learning techniques presented in the course;
1.2. To comprehend, apply, and, when necessary, adapt such methodologies and techniques for solving financial problems.

2. Ability to apply knowledge and understanding:
2.1. To identify and apply appropriate intelligent methodologies and machine learning techniques for operational problem-solving;
2.2. To set up problem-solving processes and perform necessary computations using software tools.

3. Judgement skill:
3.1. To interpret financial implications of the computational results;
3.2. To understand the merits and limitations of learned intelligent methodologies and machine learning techniques.

Having clear the contents of the following courses in the undergraduate programs in the economic field: Mathematics-1, Mathematics-2, Computing Skills for Economics. Additionally, having some experience in software programming is advisable.

- Introduction to Artificial Intelligence and to Machine Learninf in finance and in insurance.
- Intelligent metaheuristics for complex optimization and financial/insurance applications.
- Supervised learning (Decision Tree, Random Forest, Perceptron, and Multi Layer Perceptron) and financial/insurance applications.
- Reinforcement Learning and financial/insurance applications.
- Elements of Natural Language Processingand financial/insurance applications.
- Elements of Group Method of Data Handling 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 assessment consists of three homework assignments and an oral examination.
Homework assignments:
1) must be completed in groups of two or three students;
2) are valid only for the current academic year;
3) must be submitted via Moodle no later than a specified deadline. Submission procedures and deadlines will be communicated during the course.
Oral examination:
1) must be taken individually;
2) consists of three parts: In the first part, students are required to critically present a research article using slides. Instructions on how to select the research article will be provided during the course; In the second part, students are required to present, again using slides, the results obtained by applying one or more of the methodologies learned during the course in order to replicate the findings reported in the selected research article; In the third part, students must answer one question selected by the instructor from three questions proposed by the student. The questions must concern topics covered during lectures and in the assigned course materials.
Assessment criteria:
1) each homework assignment is worth between 0 and 4 points, for a total of 0 to 12 points;
2) the oral examination is worth between 0 and 18 points.
Optionally, students may complete additional activities, which will be presented during the course, for an additional score ranging from 0 to 2 points.
The final grade is determined by the sum of the points obtained from the homework assignments, the oral examination, and any optional additional activities.

oral

The instructor is responsible for ensuring the authenticity and originality of all examinations and coursework. In cases of suspected academic misconduct, an additional on-site assessment may be required during the exams, which may differ from the standard format.

Evaluation grid:
- 18–22: Incomplete and inaccurate completion of homework assignments; barely sufficient critical thinking skills applied to the understanding of applications, methods, models, and related content presented in the specialized literature; minimal or no ability in the development and implementation of software related to such applications, methods, and models; barely sufficient knowledge of the topics covered in class and in the assigned teaching materials.
- 23–26: Complete but not entirely accurate completion of homework assignments; fair critical thinking skills applied to the understanding of applications, methods, models, and related content presented in the specialized literature; modest ability in the development and implementation of software related to such applications, methods, and models; fair knowledge of the topics covered in class and in the assigned teaching materials.
- 27–30L: Complete and rigorous completion of homework assignments; excellent critical thinking skills applied to the understanding of applications, methods, models, and related content presented in the specialized literature; good ability in the development and implementation of software related to such applications, methods, and models; in-depth knowledge of the topics covered in class and in the assigned teaching materials.

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.

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

This subject deals with topics related to the macro-area "Human capital, health, education" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development