A FINancial supervision and TECHnology compliance training programme

Livrables

UNIPV through ABI Lab will provide an intermediate evaluation report on the risk management methodologies developed in the project, based on all received feedbacks, from supervisors, fintechs and banks.

Financial output

Financial reporting to the partners and to the EC.

Technical output

Integration of project deliverables: slides, use cases, feedback and evaluation reports.

Final Evaluation report

ASE Bucuresti, based on the information received from all partners, will provide a final evaluation report on the risk management methodologies developed in the project, based on all received feedbacks, from supervisors, fintechs and banks. This includes the feedbacks collected from the participants to SupTech and RegTech workshops.

Advisory Board report

The Advisory Board composed by five nonEuropean experts after receiving all the project deliverables as well as the feedbacks given by the participants to the project events will provide a final evaluation report

Network Establishment

Establishment of the FIN-TECH network and of the Advisory Board;

Repository of use cases and slides in blockchain

Repository of use cases (including paper, data and code) and slides in blockchain shared during the Suptech and RegTech workshops.

Repository of use cases and slides in artificial intelligence

Repository of use cases and slides (including paper, data and code) in artificial intelligence shared during the Suptech and RegTech workshops.

Repository of research consortium papers (BDA)

Repository of research consortium papers from the Big Data Analytics research

Event feedback repository

Firamis (M1-M15) and ASE Bucuresti (M16-end of the project) is responsible for collecting and sharing feedbacks from the participants to SupTech and RegTech workshops.

Repository of research consortium papers (AI)

Repository of research consortium papers from the Artificial Intelligence research

Research and development environment

The creation of a coding technical infrastructure that is scalable and extendable in a modular approach. The basis for the infrastructure will be open-source projects like R which gives access to developed machine learning projects like Tensorflow, PyTorch, MXNet and H2O. These research and development environments will be made available in a dedicated cloud server environment to manage the code, scripts, GUIs, models, users’ access rights, software interaction and workflows.

Repository of use cases and slides in big data analytics

Repository of use cases (including paper, data and code) and slides in big data analytics shared during the Suptech and RegTech workshops.

Establishment of website and social media channels.
Repository of research consortium papers (BC)

Repository of research consortium papers from Blockchain research

Event participation repository

In this task the work package leader (Firamis M1-M15, ASE Bucuresti M16-end of the project) will promote and monitor the participation of all project participants to conference, workshops and professional events, on the project topics, and the related publications in international scientific journals. Specifically, Firamis is responsible for collecting and sharing updates on participations to conferences and research papers by the project network participants.

Publications

The Cost of Bitcoin Mining Has Never Really Increased

Auteurs: Yo-Der Song, Tomaso Aste
Publié dans: Frontiers in Blockchain, Numéro 3, 2020, ISSN 2624-7852
Éditeur: Frontiers
DOI: 10.3389/fbloc.2020.565497

Information-theoretic measures for nonlinear causality detection: application to social media sentiment and cryptocurrency prices

Auteurs: Z. Keskin, T. Aste
Publié dans: Royal Society Open Science, Numéro 7/9, 2020, Page(s) 200863, ISSN 2054-5703
Éditeur: Royal Society
DOI: 10.1098/rsos.200863

COVID-19 contagion and digital finance

Auteurs: Arianna Agosto, Paolo Giudici
Publié dans: Digital Finance, Numéro 2/1-2, 2020, Page(s) 159-167, ISSN 2524-6984
Éditeur: Springer Pub. Co.
DOI: 10.1007/s42521-020-00021-3

Interpretable Machine Learning for Diversified Portfolio Construction

Auteurs: Markus Jaeger, Stephan Krügel, Dimitri Marinelli, Jochen Papenbrock, Peter Schwendner
Publié dans: The Journal of Financial Data Science, 2020, Page(s) jfds.2021.1.066, ISSN 2640-3943
Éditeur: Institutional Investor Journals Umbrella
DOI: 10.3905/jfds.2021.1.066

Significance, relevance and explainability in the machine learning age: an econometrics and financial data science perspective

Auteurs: Andreas G. F. Hoepner, David McMillan, Andrew Vivian, Chardin Wese Simen
Publié dans: The European Journal of Finance, Numéro 27/1-2, 2021, Page(s) 1-7, ISSN 1351-847X
Éditeur: Chapman & Hall
DOI: 10.1080/1351847x.2020.1847725

Latent factor models for credit scoring in P2P systems

Auteurs: Daniel Felix Ahelegbey, Paolo Giudici, Branka Hadji-Misheva
Publié dans: Physica A: Statistical Mechanics and its Applications, Numéro 522, 2019, Page(s) 112-121, ISSN 0378-4371
Éditeur: Elsevier BV
DOI: 10.1016/j.physa.2019.01.130

Cyber risk ordering with rank-based statistical models

Auteurs: Paolo Giudici, Emanuela Raffinetti
Publié dans: AStA Advances in Statistical Analysis, 2020, ISSN 1863-8171
Éditeur: Springer Pub. Co.
DOI: 10.1007/s10182-020-00387-0

Neural networks and arbitrage in the VIX

Auteurs: Joerg Osterrieder, Daniel Kucharczyk, Silas Rudolf, Daniel Wittwer
Publié dans: Digital Finance, Numéro 2/1-2, 2020, Page(s) 97-115, ISSN 2524-6984
Éditeur: Springer Pub. Co.
DOI: 10.1007/s42521-020-00026-y

Comparing Performance of Machine Learning Algorithms for Default Risk Prediction in Peer to Peer Lending

Auteurs: Yanka Aleksandrova
Publié dans: TEM Journal, 2021, Page(s) 133-143, ISSN 2217-8333
Éditeur: UIKTEN - Association for Information Communication Technology Education and Science,Serbia.
DOI: 10.18421/tem101-16

Risk-return modelling in the p2p lending market: Trends, gaps, recommendations and future directions

Auteurs: Miller-Janny Ariza-Garzón, María-Del-Mar Camacho-Miñano, María-Jesús Segovia-Vargas, Javier Arroyo
Publié dans: Electronic Commerce Research and Applications, Numéro 49, 2021, Page(s) 101079, ISSN 1567-4223
Éditeur: Elsevier BV
DOI: 10.1016/j.elerap.2021.101079

Libra or Librae? Basket based stablecoins to mitigate foreign exchange volatility spillovers

Auteurs: Paolo Giudici, Thomas Leach, Paolo Pagnottoni
Publié dans: Finance Research Letters, 2021, Page(s) 102054, ISSN 1544-6123
Éditeur: Elsevier BV
DOI: 10.1016/j.frl.2021.102054

Key Roles of Crypto-Exchanges in Generating Arbitrage Opportunities

Auteurs: by Audrius Kabašinskas and Kristina Šutienė
Publié dans: Entropy, Numéro 23(4), 2021, ISSN 1099-4300
Éditeur: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/e23040455

Matrix Evolutions: Synthetic Correlations and Explainable Machine Learning for Constructing Robust Investment Portfolios

Auteurs: Jochen Papenbrock, Peter Schwendner, Markus Jaeger, Stephan Krügel
Publié dans: The Journal of Financial Data Science, Numéro 3/2, 2021, Page(s) 51-69, ISSN 2640-3943
Éditeur: Institutional Investor Journals Umbrella}
DOI: 10.3905/jfds.2021.1.056

Network Based Scoring Models to Improve Credit Risk Management in Peer to Peer Lending Platforms

Auteurs: Paolo Giudici, Branka Hadji-Misheva, Alessandro Spelta
Publié dans: Frontiers in Artificial Intelligence, Numéro 2, 2019, ISSN 2624-8212
Éditeur: Frontiers
DOI: 10.3389/frai.2019.00003

High Frequency Price Change Spillovers in Bitcoin Markets

Auteurs: Giudici, Pagnottoni
Publié dans: Risks, Numéro 7/4, 2019, Page(s) 111, ISSN 2227-9091
Éditeur: MDPI
DOI: 10.3390/risks7040111

Sentiment Analysis of European Bonds 2016–2018

Auteurs: Peter Schwendner, Martin Schüle, Martin Hillebrand
Publié dans: Frontiers in Artificial Intelligence, Numéro 2, 2019, ISSN 2624-8212
Éditeur: Frontiers
DOI: 10.3389/frai.2019.00020

Fintech Risk Management: A Research Challenge for Artificial Intelligence in Finance

Auteurs: Paolo Giudici
Publié dans: Frontiers in Artificial Intelligence, Numéro 1, 2018, ISSN 2624-8212
Éditeur: Frontiers
DOI: 10.3389/frai.2018.00001

Lead Behaviour in Bitcoin Markets

Auteurs: Ying Chen, Paolo Giudici, Branka Hadji Misheva, Simon Trimborn
Publié dans: Risks, Numéro 8/1, 2020, Page(s) 4, ISSN 2227-9091
Éditeur: MDPI
DOI: 10.3390/risks8010004

Spatial Regression Models to Improve P2P Credit Risk Management

Auteurs: Arianna Agosto, Paolo Giudici, Tom Leach
Publié dans: Frontiers in Artificial Intelligence, Numéro 2, 2019, ISSN 2624-8212
Éditeur: Frontiers
DOI: 10.3389/frai.2019.00006

Can Cryptocurrencies Preserve Privacy and Comply With Regulations?

Auteurs: Geoff Goodell, Tomaso Aste
Publié dans: Frontiers in Blockchain, Numéro 2, 2019, ISSN 2624-7852
Éditeur: Frontiers
DOI: 10.3389/fbloc.2019.00004

Cryptocurrency market structure: connecting emotions and economics

Auteurs: Tomaso Aste
Publié dans: Digital Finance, Numéro 1/1-4, 2019, Page(s) 5-21, ISSN 2524-6984
Éditeur: Springer Verlag
DOI: 10.1007/s42521-019-00008-9

A Decentralised Digital Identity Architecture

Auteurs: Goodell, Geoff; Aste, Tomaso
Publié dans: Frontiers in Blockchain , 2 , Article 17. (2019), Numéro 1, 2019, ISSN 2624-7852
Éditeur: Frontiers
DOI: 10.3389/fbloc.2019.00017

Crypto price discovery through correlation networks

Auteurs: Paolo Giudici, Gloria Polinesi
Publié dans: Annals of Operations Research, 2019, ISSN 0254-5330
Éditeur: Kluwer Academic Publishers
DOI: 10.1007/s10479-019-03282-3

Analysing Social Media Forums to Discover Potential Causes of Phasic Shifts in Cryptocurrency Price Series

Auteurs: Andrew Burnie, Emine Yilmaz, Tomaso Aste
Publié dans: Frontiers in Blockchain, Numéro 3, 2020, ISSN 2624-7852
Éditeur: Frontiers
DOI: 10.3389/fbloc.2020.00001

Initial Coin Offerings: Risk or Opportunity?

Auteurs: Anca Mirela Toma, Paola Cerchiello
Publié dans: Frontiers in Artificial Intelligence, Numéro 3, 2020, ISSN 2624-8212
Éditeur: Frontiers
DOI: 10.3389/frai.2020.00018

Explainability of a Machine Learning Granting Scoring Model in Peer-to-Peer Lending

Auteurs: Miller Janny Ariza-Garzon, Javier Arroyo, Antonio Caparrini, Maria-Jesus Segovia-Vargas
Publié dans: IEEE Access, Numéro 8, 2020, Page(s) 64873-64890, ISSN 2169-3536
Éditeur: Institute of Electrical and Electronics Engineers Inc.
DOI: 10.1109/access.2020.2984412

Assessment of Machine Learning Performance for Decision Support in Venture Capital Investments

Auteurs: Javier Arroyo, Francesco Corea, Guillermo Jimenez-Diaz, Juan A. Recio-Garcia
Publié dans: IEEE Access, Numéro 7, 2019, Page(s) 124233-124243, ISSN 2169-3536
Éditeur: Institute of Electrical and Electronics Engineers Inc.
DOI: 10.1109/access.2019.2938659

A Statistical Classification of Cryptocurrencies

Auteurs: Pele, D.T., Wesselhöfft, N., Härdle, W.K., Kolossiatis, M., Yatracos, Y.
Publié dans: Journal of Empirical Finance, Numéro 5 per year, 2020, Page(s) under review to this journal, ISSN 0927-5398
Éditeur: Elsevier BV

Will they repay their debt? Identification of borrowers likely to be charged off

Auteurs: Caplescu, RD., Panaite, AM., Pele, DT, Strat, VA.
Publié dans: Management & Marketing. Challenges for the Knowledge Society, Numéro 4 per year, 2020, Page(s) is under review to the mentioned journal, ISSN 2069-8887
Éditeur: Editura Economica

Fin vs. tech: are trust and knowledge creation key ingredients in fintech start-up emergence and financing?

Auteurs: Theodor Florian Cojoianu, Gordon L. Clark, Andreas G. F. Hoepner, Vladimir Pažitka, Dariusz Wójcik
Publié dans: Small Business Economics, 2020, ISSN 0921-898X
Éditeur: Kluwer Academic Publishers
DOI: 10.1007/s11187-020-00367-3

Peer-to-peer loan acceptance and default prediction with artificial intelligence

Auteurs: J. D. Turiel, T. Aste
Publié dans: Royal Society Open Science, Numéro 7/6, 2020, Page(s) 191649, ISSN 2054-5703
Éditeur: The Royal Society
DOI: 10.1098/rsos.191649

On the Improvement of Default Forecast Through Textual Analysis

Auteurs: Paola Cerchiello, Roberta Scaramozzino
Publié dans: Frontiers in Artificial Intelligence, Numéro 3, 2020, ISSN 2624-8212
Éditeur: Frontiers
DOI: 10.3389/frai.2020.00016

Network Models to Enhance Automated Cryptocurrency Portfolio Management

Auteurs: Paolo Giudici, Paolo Pagnottoni, Gloria Polinesi
Publié dans: Frontiers in Artificial Intelligence, Numéro 3, 2020, ISSN 2624-8212
Éditeur: Frontiers
DOI: 10.3389/frai.2020.00022

Investing with cryptocurrencies – evaluating their potential for portfolio allocation strategies

Auteurs: Alla Petukhina, Simon Trimborn, Wolfgang Karl Härdle, Hermann Elendner
Publié dans: Quantitative Finance, 2021, Page(s) 1-29, ISSN 1469-7688
Éditeur: Institute of Physics Publishing
DOI: 10.1080/14697688.2021.1880023

Shapley-Lorenz eXplainable Artificial Intelligence

Auteurs: Paolo Giudici, Emanuela Raffinetti
Publié dans: Expert Systems with Applications, Numéro 167, 2021, Page(s) 114104, ISSN 0957-4174
Éditeur: Pergamon Press Ltd.
DOI: 10.1016/j.eswa.2020.114104

Rise of the machines? Intraday high-frequency trading patterns of cryptocurrencies

Auteurs: Alla A. Petukhina, Raphael C. G. Reule, Wolfgang Karl Härdle
Publié dans: The European Journal of Finance, Numéro 27/1-2, 2021, Page(s) 8-30, ISSN 1351-847X
Éditeur: Chapman & Hall
DOI: 10.1080/1351847x.2020.1789684

Predictability and pricing efficiency in forward and spot, developed and emerging currency markets

Auteurs: Valerio Potì, Richard Levich, Thomas Conlon
Publié dans: Journal of International Money and Finance, Numéro 107, 2020, Page(s) 102223, ISSN 0261-5606
Éditeur: Pergamon Press Ltd.
DOI: 10.1016/j.jimonfin.2020.102223

Evaluation of multi-asset investment strategies with digital assets

Auteurs: Alla Petukhina, Erin Sprünken
Publié dans: Digital Finance, Numéro 3/1, 2021, Page(s) 45-79, ISSN 2524-6984
Éditeur: Springer Verlag
DOI: 10.1007/s42521-021-00031-9

Explainable Machine Learning in Credit Risk Management

Auteurs: Niklas Bussmann, Paolo Giudici, Dimitri Marinelli, Jochen Papenbrock
Publié dans: Computational Economics, 2020, ISSN 0927-7099
Éditeur: Kluwer Academic Publishers
DOI: 10.1007/s10614-020-10042-0

Default count-based network models for credit contagion

Auteurs: Arianna Agosto, Daniel Felix Ahelegbey
Publié dans: Journal of the Operational Research Society, 2020, Page(s) 1-14, ISSN 0160-5682
Éditeur: Palgrave Macmillan Ltd.
DOI: 10.1080/01605682.2020.1776169

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