The Road towards the Future: Fintech, AI, and Cryptocurrencies

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Financial Technology and Innovation".

Deadline for manuscript submissions: closed (1 October 2025) | Viewed by 479

Special Issue Editors


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Guest Editor
Department of Economics, Engineering, Society and Business Organization, Deim University of Tuscia Via del Paradiso, 47, 01100 Viterbo, VT, Italy
Interests: climate change; fintech; ESG; derivatives; market & credit risk; risk management; asset allocation; AI in finance and portfolio management; complex networks in finance; NLP in finance; counterparty risk; liquidity risk

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Guest Editor
Department of Statistical Sciences, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
Interests: complex networks in economics and finance bibliometric indicators
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Special Issue Information

Dear Colleagues,

Macroeconomic trends, along with social, regulatory, and technological advancements, have led to significant structural shifts in the banking and financial market landscape. The swift progression of technological innovation is creating a novel competitive scenery, altering the limits and core nature of financial services with breakthroughs like artificial intelligence, machine learning, blockchain, bitcoin, and the internet of things.

The potential benefits for all stakeholders involved in the realm of finance are numerous. These include expanding access to credit and financial services, particularly reaching underserved consumers (financial inclusion), accessing a wider range of customer-tailored products, increasing the availability of up-to-date information, reducing transaction costs, and enhancing market transparency and efficiency.

Regulatory authorities globally are strategically positioned to uphold a delicate equilibrium between fostering innovation and enforcing regulation, with the primary goal of optimizing advantages for investors while reducing potential risks.

The call for papers seeks to gather research from various fintech domains (banking, payments, capital markets, asset management, insurance, internet of things) to provide a multidisciplinary contribution to academic research. We welcome submissions covering a wide array of fintech research topics, such as the examination of the fintech environment, applications of machine learning techniques in finance, text analysis, big data analysis, complex network analysis, neural network approaches, behavioural finance, cryptocurrencies, and smart contracts.

Dr. Anna Maria D'Arcangelis
Prof. Dr. Giulia Rotundo
Guest Editors

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Keywords

  • fintech
  • AI
  • cryptocurrency
  • machine learning

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Published Papers (1 paper)

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Research

17 pages, 875 KB  
Article
Predicting the Risk of Death for Cryptocurrencies Using Deep Learning
by Doğa Elif Konuk and Halil Altay Güvenir
J. Risk Financial Manag. 2025, 18(12), 716; https://doi.org/10.3390/jrfm18120716 - 15 Dec 2025
Abstract
The rapid rise in the popularity of cryptocurrencies has drawn increasing attention from investors, entrepreneurs, and the public in recent years. However, this rapid growth comes with risk: many coins fail early and become what are known as “dead coins”, defined by a [...] Read more.
The rapid rise in the popularity of cryptocurrencies has drawn increasing attention from investors, entrepreneurs, and the public in recent years. However, this rapid growth comes with risk: many coins fail early and become what are known as “dead coins”, defined by a lack of recorded activity for more than a year. This study applies deep learning techniques to estimate the short-term risk of a cryptocurrency’s death. Specifically, three Recurrent Neural Network architectures, Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU), were trained on 18-month time series of daily closing prices and trading volumes using a stratified five-fold cross-validation framework. The models’ predictive performances were compared across input windows ranging from 10 to 180 days. Using the previous 180 days of data as input, GRU achieved the highest point accuracy of 0.7134, whereas BiLSTM exhibited the best performance when evaluated across input sequence lengths varying from 10 to 180 days, reaching an average accuracy of 0.676. These findings show the ability of recurrent architectures to anticipate short-term failure risks in cryptocurrency markets. Theoretically, the study contributes to financial risk modeling by extending time series classification methods to cryptocurrency failure prediction. Practically, it provides investors and analysts with a data-driven early-warning tool to manage portfolio risk and reduce potential losses. Full article
(This article belongs to the Special Issue The Road towards the Future: Fintech, AI, and Cryptocurrencies)
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