Advanced Statistical Modelling for Fintech, financial inclusion and inequality

The Advanced Statistical Modelling for Fintech, financial inclusion and inequality (AFFINE) training school is a three-day programme that equips participants with the required quantitative and analytical skills, including coding and programming skills, needed to perform analysis in the FinTech or financial services industry, especially analyses of complex data and latent traits such as knowledge of financial instruments and financial inclusion.

The School is designed for COST members, steakeholders, reasearchers, PhD students, and possibly brilliant MSc graduates / final-year students interested in Statistics, Probability, Computer Science, Applied Mathematics, and related areas.

Content
The aim of the Training School AFFINE is to establish a track of high level courses on advanced and cutting-edge topics in Statistics and Probability with application in FinTech and Artificial Intelligence area.
The programme syllabus will enable to understand, execute and possibly develop disruptive financial innovations using appropriate tools and techniques. It aims at acquiring analytical skills to create, manage and interrogate large data sets applicable to the finance sector and build up a critical awareness of current issues in the FinTech landscape. A range of programming tools will facilitate implementations of financial models and allow participants to analyse and evaluate decision making process and data.

Preliminaries
Participants may first acquire the main preliminary information and familiarise with the R environment thanks to the free course offered by the University of Naples Federico II (MOOC- www.federica.eu) Machine Learning e Data Mining in R at
https://www.coursera.org/learn/machine-learning-data-mining-con-r

Detailed schedule
Face-to-face; 3 days

Wednesday, September 13, 2023
Welcome event

• 14.00. Poster session: participants of the Cost Action CA19130 may present their research. Data analysis collected by VMGs surveys will be the starting point for the discussion on the applied component.
• - 14.30. Welcoming speech - Jörg Osterrieder, Chair of the COST Action Fintech and AI in Finance
• 14.45. Introduction of the course (Maria Iannario)
• 15.00. Methods for dimension reduction and clustering in Fintech surveys
  Guest lecture - Angelos Markos, Professor of Data Analysis in the Social Sciences, Democritus University of Thrace
  Main topic: Clustering mixed type nominal/ordinal/interval data, Joint dimension reduction and clustering
• 17.00 Gender differences in the knowledge and use of Fintech products: narrative of a survey

Lecture, Claudia Tarantola, Professor of Statistics, University of Pavia

• 17.30. Laboratory lectures – supervised tutorials as well as individual and team work

Thursday, September 14, 2023

• 9.30. Latent variable models for Financial knowledge
  Guest lecture – Silvia Bacci, Professor of Statistics, University of Florence
  Main topic: Latent variables, Classical Test Theory, Item Response Theory, IRT models for polytomous items

13.00. Lunch

• 14.00. Laboratory lectures – supervised tutorials as well as individual and team work
• 18.00 Social event

Friday, September 15, 2023

• 9.30. Hybrid approach for the analysis of complex data structures
  Guest Lecture – Alfonso Iodice D’Enza, Professor of Statistics, University of Naples Federico II

Main topic: Hybrid methods; Implementations of dimension reduction for continuous, polytomous and mixed data, complex categorical structures

Short lectures

• 11.30 Complex data structures in Finance

Lecture Petre Lameski, Professor at University of Sts. Cyril and Methodius in Skopje, Macedonia

• 12.00. Panel data for assessing FinTech, financial inclusion and income per capita

Lecture Codruta Mare, Professor of Econometrics, Babes-Bolyai University

• 12.30 Financial inclusion: assessing the role of digital payments during the pandemic using single item latent trait response models

Lecture - Researchers by Bank of Italy

The latter contributes to the following objectives of the Action:
Improve the knowledge about financial inclusion among research institutions and infrastructures, businesses and other socio-economic actors
13.30. Lunch

• 14.30. Laboratory lectures – supervised tutorials as well as individual and team work
• 16.30. Summary, feedback, alternative methods, wrap-up (Maria Iannario)