Machine Learning and Deep Learning are spreading at incredible pace in finance. However, the related energy consumption lacks in coherence with the sustainability requirements of banks and financial institutions. Particularly, as the energy required to run complex algorithms increases, more CO2 is released because of the power grid relying mostly on fossil fuels. While techniques are arising to mitigate the carbon footprint of AI, Fintech is facing the challenge of integrating such strategies within the peculiarities of the domain, which requires timeliness, data protection and compliance. In this work, we present the state-of-the-art Green AI techniques, reporting the available experimental results, and critically evaluating their applicability in the financial domain. This contribution is useful for financial institutions, Fintech startups and regulatory bodies who wants to measure and mitigate the impact of AI in the field, decarbonize old or new AI-based products and services, and assess the effectiveness of decarbonization efforts.

The Internet of Things (IoT) is set to play a significant role in the future development of smart cities, which are designed to be environmentally friendly. However, the proliferation of these devices, along with their frequent replacements and the energy required to power them, contributes to a significant environmental footprint. In this paper we provide scientific evidences on the advantages of using an energy design pattern named ‘Batch Operations’ (BO) to optimize energy consumption on mobile devices. Big ICT companies like Google already batch multiple API calls instead of putting the device into an active state many times. This is supposed to save tail energy consumption in communication peripherals. To confirm this, we set up an experiment where we compare energy consumption and carbon emission when BO is applied to two communication peripherals on Android mobile device: 4G and GPS. Results show that (1) BO can save up to 40% energy when sending HTTP requests, resulting in an equivalent reduction in $C{O}_2$ emissions. (2) no advantages for the GPS interface.

One-Time Program (OTPr) is a cryptographic construct designed to ensure the single execution of a given computer program. In their early theoretical conception in 2008, OTPrs were defined as a new paradigm in data privacy and information security scenarios. Over the years, several practical approaches — both hardware and software-based — have been proposed: garbled circuits, One-Time Memory (OTM), cryptographic algorithms, Trusted Execution Environment (TEE), and Blockchain systems. Moreover, methods based on quantum computing and quantum properties have been recently added to the frame. Nonetheless, a comprehensive overview of OTPrs is still missing up to this date. In this article, we present a systematic review of OTPrs literature, composed of 42 relevant articles. The primary studies explore theoretical foundations, practical implementations, and new possible applications of OTPrs across various domains. Our work provides researchers and software engineers with a complete picture of possible OTPrs components, and suggests a holistic methodology for experimenting with this new computing paradigm. Results show that OTPrs can find applications in novel scenarios responding to some of the most recent needs of both consumers and industry, despite the complexity of reproducing and applying such technology in the consumer market. Finally, an analysis of current research challenges is provided, which represents a starting point for researchers who want to contribute to the field.

As the world becomes increasingly digitalised, there are growing concerns about the use of big data and machine learning techniquesto monitor and control citizens’ spending habits. This is particularly the case regarding Central Bank Digital Currencies (CBDC), whichare being trialled by an increasing number of countries. There is a perception that such currencies could violate privacy due to thecentralisation of money liability. The aim of this research is to assess whether a universal e-coin level tracking service of money and public expenditures, available to everyone and inspired by Internet of Things (IoT) architectures and standards, could instil trust ininstitutions while increasing the acceptance of CDBCs. The research methodology comprises three key elements: (i) the conceptualisation and implementation of an IoT-based CBDC, (ii) a qualitative, technical and compliance assessment with regard to the specific reference to the Digital Euro (D€) project, and (iii) a survey we conducted among 351 respondents to ascertain the potential for CBDC acceptance within Italy. The results demonstrate that the prototype is a viable concept despite storage limitations. Furthermore, 73.83 percent of respondents who initially expressed scepticism indicated that they would be more inclined to adopt the CDBC instrument if a universal track-and-trace tool of money were made available.

Current methods for calculating sustainability metrics of network links are based on fixed-rate estimations. We present a context-aware model of energy consumption, also providing an open-source tool that enables developers to use the Internet more efficiently. In a proof of concept, we collected and analyzed a two-month dataset covering 43 AWS and Microsoft endpoints.