A FINancial supervision and TECHnology compliance training programme

Leistungen

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.

Veröffentlichungen

The Cost of Bitcoin Mining Has Never Really Increased

Autoren: Yo-Der Song, Tomaso Aste
Veröffentlicht in: Frontiers in Blockchain, Ausgabe 3, 2020, ISSN 2624-7852
Herausgeber: Frontiers
DOI: 10.3389/fbloc.2020.565497

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

Autoren: Z. Keskin, T. Aste
Veröffentlicht in: Royal Society Open Science, Ausgabe 7/9, 2020, Seite(n) 200863, ISSN 2054-5703
Herausgeber: Royal Society
DOI: 10.1098/rsos.200863

COVID-19 contagion and digital finance

Autoren: Arianna Agosto, Paolo Giudici
Veröffentlicht in: Digital Finance, Ausgabe 2/1-2, 2020, Seite(n) 159-167, ISSN 2524-6984
Herausgeber: Springer Pub. Co.
DOI: 10.1007/s42521-020-00021-3

Interpretable Machine Learning for Diversified Portfolio Construction

Autoren: Markus Jaeger, Stephan Krügel, Dimitri Marinelli, Jochen Papenbrock, Peter Schwendner
Veröffentlicht in: The Journal of Financial Data Science, 2020, Seite(n) jfds.2021.1.066, ISSN 2640-3943
Herausgeber: 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

Autoren: Andreas G. F. Hoepner, David McMillan, Andrew Vivian, Chardin Wese Simen
Veröffentlicht in: The European Journal of Finance, Ausgabe 27/1-2, 2021, Seite(n) 1-7, ISSN 1351-847X
Herausgeber: Chapman & Hall
DOI: 10.1080/1351847x.2020.1847725

Latent factor models for credit scoring in P2P systems

Autoren: Daniel Felix Ahelegbey, Paolo Giudici, Branka Hadji-Misheva
Veröffentlicht in: Physica A: Statistical Mechanics and its Applications, Ausgabe 522, 2019, Seite(n) 112-121, ISSN 0378-4371
Herausgeber: Elsevier BV
DOI: 10.1016/j.physa.2019.01.130

Cyber risk ordering with rank-based statistical models

Autoren: Paolo Giudici, Emanuela Raffinetti
Veröffentlicht in: AStA Advances in Statistical Analysis, 2020, ISSN 1863-8171
Herausgeber: Springer Pub. Co.
DOI: 10.1007/s10182-020-00387-0

Neural networks and arbitrage in the VIX

Autoren: Joerg Osterrieder, Daniel Kucharczyk, Silas Rudolf, Daniel Wittwer
Veröffentlicht in: Digital Finance, Ausgabe 2/1-2, 2020, Seite(n) 97-115, ISSN 2524-6984
Herausgeber: 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

Autoren: Yanka Aleksandrova
Veröffentlicht in: TEM Journal, 2021, Seite(n) 133-143, ISSN 2217-8333
Herausgeber: 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

Autoren: Miller-Janny Ariza-Garzón, María-Del-Mar Camacho-Miñano, María-Jesús Segovia-Vargas, Javier Arroyo
Veröffentlicht in: Electronic Commerce Research and Applications, Ausgabe 49, 2021, Seite(n) 101079, ISSN 1567-4223
Herausgeber: Elsevier BV
DOI: 10.1016/j.elerap.2021.101079

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

Autoren: Paolo Giudici, Thomas Leach, Paolo Pagnottoni
Veröffentlicht in: Finance Research Letters, 2021, Seite(n) 102054, ISSN 1544-6123
Herausgeber: Elsevier BV
DOI: 10.1016/j.frl.2021.102054

Key Roles of Crypto-Exchanges in Generating Arbitrage Opportunities

Autoren: by Audrius Kabašinskas and Kristina Šutienė
Veröffentlicht in: Entropy, Ausgabe 23(4), 2021, ISSN 1099-4300
Herausgeber: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/e23040455

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

Autoren: Jochen Papenbrock, Peter Schwendner, Markus Jaeger, Stephan Krügel
Veröffentlicht in: The Journal of Financial Data Science, Ausgabe 3/2, 2021, Seite(n) 51-69, ISSN 2640-3943
Herausgeber: 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

Autoren: Paolo Giudici, Branka Hadji-Misheva, Alessandro Spelta
Veröffentlicht in: Frontiers in Artificial Intelligence, Ausgabe 2, 2019, ISSN 2624-8212
Herausgeber: Frontiers
DOI: 10.3389/frai.2019.00003

High Frequency Price Change Spillovers in Bitcoin Markets

Autoren: Giudici, Pagnottoni
Veröffentlicht in: Risks, Ausgabe 7/4, 2019, Seite(n) 111, ISSN 2227-9091
Herausgeber: MDPI
DOI: 10.3390/risks7040111

Sentiment Analysis of European Bonds 2016–2018

Autoren: Peter Schwendner, Martin Schüle, Martin Hillebrand
Veröffentlicht in: Frontiers in Artificial Intelligence, Ausgabe 2, 2019, ISSN 2624-8212
Herausgeber: Frontiers
DOI: 10.3389/frai.2019.00020

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

Autoren: Paolo Giudici
Veröffentlicht in: Frontiers in Artificial Intelligence, Ausgabe 1, 2018, ISSN 2624-8212
Herausgeber: Frontiers
DOI: 10.3389/frai.2018.00001

Lead Behaviour in Bitcoin Markets

Autoren: Ying Chen, Paolo Giudici, Branka Hadji Misheva, Simon Trimborn
Veröffentlicht in: Risks, Ausgabe 8/1, 2020, Seite(n) 4, ISSN 2227-9091
Herausgeber: MDPI
DOI: 10.3390/risks8010004

Spatial Regression Models to Improve P2P Credit Risk Management

Autoren: Arianna Agosto, Paolo Giudici, Tom Leach
Veröffentlicht in: Frontiers in Artificial Intelligence, Ausgabe 2, 2019, ISSN 2624-8212
Herausgeber: Frontiers
DOI: 10.3389/frai.2019.00006

Can Cryptocurrencies Preserve Privacy and Comply With Regulations?

Autoren: Geoff Goodell, Tomaso Aste
Veröffentlicht in: Frontiers in Blockchain, Ausgabe 2, 2019, ISSN 2624-7852
Herausgeber: Frontiers
DOI: 10.3389/fbloc.2019.00004

Cryptocurrency market structure: connecting emotions and economics

Autoren: Tomaso Aste
Veröffentlicht in: Digital Finance, Ausgabe 1/1-4, 2019, Seite(n) 5-21, ISSN 2524-6984
Herausgeber: Springer Verlag
DOI: 10.1007/s42521-019-00008-9

A Decentralised Digital Identity Architecture

Autoren: Goodell, Geoff; Aste, Tomaso
Veröffentlicht in: Frontiers in Blockchain , 2 , Article 17. (2019), Ausgabe 1, 2019, ISSN 2624-7852
Herausgeber: Frontiers
DOI: 10.3389/fbloc.2019.00017

Crypto price discovery through correlation networks

Autoren: Paolo Giudici, Gloria Polinesi
Veröffentlicht in: Annals of Operations Research, 2019, ISSN 0254-5330
Herausgeber: 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

Autoren: Andrew Burnie, Emine Yilmaz, Tomaso Aste
Veröffentlicht in: Frontiers in Blockchain, Ausgabe 3, 2020, ISSN 2624-7852
Herausgeber: Frontiers
DOI: 10.3389/fbloc.2020.00001

Initial Coin Offerings: Risk or Opportunity?

Autoren: Anca Mirela Toma, Paola Cerchiello
Veröffentlicht in: Frontiers in Artificial Intelligence, Ausgabe 3, 2020, ISSN 2624-8212
Herausgeber: Frontiers
DOI: 10.3389/frai.2020.00018

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

Autoren: Miller Janny Ariza-Garzon, Javier Arroyo, Antonio Caparrini, Maria-Jesus Segovia-Vargas
Veröffentlicht in: IEEE Access, Ausgabe 8, 2020, Seite(n) 64873-64890, ISSN 2169-3536
Herausgeber: 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

Autoren: Javier Arroyo, Francesco Corea, Guillermo Jimenez-Diaz, Juan A. Recio-Garcia
Veröffentlicht in: IEEE Access, Ausgabe 7, 2019, Seite(n) 124233-124243, ISSN 2169-3536
Herausgeber: Institute of Electrical and Electronics Engineers Inc.
DOI: 10.1109/access.2019.2938659

A Statistical Classification of Cryptocurrencies

Autoren: Pele, D.T., Wesselhöfft, N., Härdle, W.K., Kolossiatis, M., Yatracos, Y.
Veröffentlicht in: Journal of Empirical Finance, Ausgabe 5 per year, 2020, Seite(n) under review to this journal, ISSN 0927-5398
Herausgeber: Elsevier BV

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

Autoren: Caplescu, RD., Panaite, AM., Pele, DT, Strat, VA.
Veröffentlicht in: Management & Marketing. Challenges for the Knowledge Society, Ausgabe 4 per year, 2020, Seite(n) is under review to the mentioned journal, ISSN 2069-8887
Herausgeber: Editura Economica

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

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

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

Autoren: J. D. Turiel, T. Aste
Veröffentlicht in: Royal Society Open Science, Ausgabe 7/6, 2020, Seite(n) 191649, ISSN 2054-5703
Herausgeber: The Royal Society
DOI: 10.1098/rsos.191649

On the Improvement of Default Forecast Through Textual Analysis

Autoren: Paola Cerchiello, Roberta Scaramozzino
Veröffentlicht in: Frontiers in Artificial Intelligence, Ausgabe 3, 2020, ISSN 2624-8212
Herausgeber: Frontiers
DOI: 10.3389/frai.2020.00016

Network Models to Enhance Automated Cryptocurrency Portfolio Management

Autoren: Paolo Giudici, Paolo Pagnottoni, Gloria Polinesi
Veröffentlicht in: Frontiers in Artificial Intelligence, Ausgabe 3, 2020, ISSN 2624-8212
Herausgeber: Frontiers
DOI: 10.3389/frai.2020.00022

Investing with cryptocurrencies – evaluating their potential for portfolio allocation strategies

Autoren: Alla Petukhina, Simon Trimborn, Wolfgang Karl Härdle, Hermann Elendner
Veröffentlicht in: Quantitative Finance, 2021, Seite(n) 1-29, ISSN 1469-7688
Herausgeber: Institute of Physics Publishing
DOI: 10.1080/14697688.2021.1880023

Shapley-Lorenz eXplainable Artificial Intelligence

Autoren: Paolo Giudici, Emanuela Raffinetti
Veröffentlicht in: Expert Systems with Applications, Ausgabe 167, 2021, Seite(n) 114104, ISSN 0957-4174
Herausgeber: Pergamon Press Ltd.
DOI: 10.1016/j.eswa.2020.114104

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

Autoren: Alla A. Petukhina, Raphael C. G. Reule, Wolfgang Karl Härdle
Veröffentlicht in: The European Journal of Finance, Ausgabe 27/1-2, 2021, Seite(n) 8-30, ISSN 1351-847X
Herausgeber: Chapman & Hall
DOI: 10.1080/1351847x.2020.1789684

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

Autoren: Valerio Potì, Richard Levich, Thomas Conlon
Veröffentlicht in: Journal of International Money and Finance, Ausgabe 107, 2020, Seite(n) 102223, ISSN 0261-5606
Herausgeber: Pergamon Press Ltd.
DOI: 10.1016/j.jimonfin.2020.102223

Evaluation of multi-asset investment strategies with digital assets

Autoren: Alla Petukhina, Erin Sprünken
Veröffentlicht in: Digital Finance, Ausgabe 3/1, 2021, Seite(n) 45-79, ISSN 2524-6984
Herausgeber: Springer Verlag
DOI: 10.1007/s42521-021-00031-9

Explainable Machine Learning in Credit Risk Management

Autoren: Niklas Bussmann, Paolo Giudici, Dimitri Marinelli, Jochen Papenbrock
Veröffentlicht in: Computational Economics, 2020, ISSN 0927-7099
Herausgeber: Kluwer Academic Publishers
DOI: 10.1007/s10614-020-10042-0

Default count-based network models for credit contagion

Autoren: Arianna Agosto, Daniel Felix Ahelegbey
Veröffentlicht in: Journal of the Operational Research Society, 2020, Seite(n) 1-14, ISSN 0160-5682
Herausgeber: Palgrave Macmillan Ltd.
DOI: 10.1080/01605682.2020.1776169

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