Decentralized Intelligence: AI’s Role in Optimizing Cryptocurrency and Digital Asset Ecosystems

This study seeks to develop an innovative infrastructure for digital wallets that addresses the diverse requirements of both corporate and individual users. The main focus is on real-time monitoring of users' interactions on digital asset management software and generating instant actions through the analysis of this data. In this context, the main objectives of the wallet include providing local solutions as an alternative to high transaction fees, ensuring that assets are securely stored on local cloud systems with digital wallets, and developing applications that will meet the needs of Financial Technology companies and its business partners. Thus, it is aimed to increase the security, efficiency, and cost-effectiveness of digital asset transactions and to improve the user experience in digital wallet use in general. As a result, this study aims to provide a comprehensive solution that aims to offer significant advantages such as security, usability, and cost-effectiveness in the field of digital asset management and storage. It is expected that this study will contribute significantly to the expansion of the digital wallet ecosystem and to users' experiences in digital asset management.

In the traditional approach to a digital asset management system, the data processing mechanism is not transparent or visible to the data owners since the data is managed solely by the service provider. With the rapid development of blockchain technology, the above issues can be resolved by leveraging the tamper-resistance and decentralization characteristics of blockchain. However, post the implementation of the EU General Data Protection Rules (GDPR) in 2018, the protection of data owners has taken center stage. This has led to several principles of personal data deletion, such as Storage Limit and the Right to Be Forgotten to conflict with the blockchain. It is also observed that, out of the various smart contracts deployed to manage digital assets, often only specific smart contracts are invoked, while the rest of the deployed smart contracts are rarely invoked, leading to smart contract designs exhibiting similar patterns with very little creativity. This current scenario has motivated us to propose SPChain, a smarter and private GDPR-compliant digital asset management framework enabled by blockchain. In this approach, a decentralized InterPlanetary File System has been adopted to solve the problem of SPOF. In addition, the combination of digital assets with artificial intelligence models has been proposed so as to make digital assets accessible to a larger number of applications and to enable better creativity. In this design, artificial intelligence models have been run in independent, virtualized containers and invoked through smart contracts. The proposed SPChain can be applied to the field of digital art management to provide a complete implementation based on the Hyperledger Fabric. Using this proposed framework, model developers, digital art creators, collectors, service providers, as well as third parties can not only benefit from securely managing digital assets and combining them with AI models, but also from simultaneously complying with the rights stipulated in the GDPR. During the course of the experiments conducted, the latency, throughput, and resource consumption of different functions in the smart contracts have been measured. After adjusting the batch timeout of the block and the maximum number of transactions in a block, the throughputs were observed to be about 500 TPS, with 10 to 15 TPS for reading and writing operations, respectively. The latency ranges were found to range from 0 to 7 seconds, with 2.5 to 5 seconds for reading and writing operations, respectively.

Blockchain is undoubtedly one of the most potentially attractive technologies for the organization of financial services. The significance of the Blockchain technology in a banking system can be determined by three main principles that is; Disintermediation, Transparency and Provenance. The DLT (distributed ledger technology) eliminates the need for trust enforcer in the banking management system. This is made possible by the tamper- proof data in the distributed data records and all the participants in the network have the same version of data record with them at all times and it is unalterable. Blockchain promotes a public database record of all the customer transactions and other data, ensuring a transparent and trustworthy system as the customers have a 24x7 real-time access to it. Blockchain keeps an immutable distributed data ledger of transactions and this result in maintaining a lifecycle of the assets and its ownership from its first transaction to the most recent one. It reduces the chances of occurrence of theft, insurance fraud and misselling of the high valued assets or intellectual properties. Blockchain can be implemented in the banking sector for various applications like maintenance of data confidentiality, cheque clearing system, real time money transfer system (digital currency), supply chain finance, insurance, trade finance, KYC (know your customer), cross-border payments/transactions system, monitoring of consortium accounts, auction system and digital asset management. Blockchain technology can hence enable a decentralized solution for the banking ecosystem. The proposed system enables one platform for both the lenders and the borrowers to initiate their money transaction for lending, borrowing and repayment of the loan money. The transactions made throughout, that is, from the start to the last repayment can be viewed easily on a blockchain network thus increasing the transparency and trust of the customers and it resolves any disputes that can arise in the loan system. Along with this, a KYC registry based on blockchain is also proposed which will help in maintaining the KYC details in one platform which can be accessed by various banks for identification and verification of the customer in an easy and faster manner. It will increase the customer satisfaction as there will be no need for the customer to perform a KYC check every time he/she creates a bank account in any bank as the bank can easily access their KYC details from this system.

Technology advancement and always-on access have transformed the way consumers make purchasing decisions — information-hungry, price-savvy, empowered customers have heightened shopping expectations. Many retailers are struggling to meet the demand for curated customer experience without also evolving away from the status quo silo-based approach to digital asset management (DAM). The ability to effectively manage the entire supply chain workflow and metadata for digital assets is an often overlooked but critical component of a successful omnichannel retailing strategy. This paper examines six sets of common challenges and emerging trends in the transformation of DAM strategy in retail, both upstream of DAM in the supply chain (‘what makes DAM tick’) and downstream of DAM facing consumers (‘what gets them to click’). The paper is an elaboration of a presentation made by the author at Henry Stewart’s DAM LA Conference in Los Angeles on 13th November, 2014.

Integrating blockchain technology with artificial intelligence (AI) i.e., blockchain Intelligence makes an extremely powerful tool that solves many multidimensional problems in several domains. Blockchain technology has the potential to provide links to shared data, transactions, and records in a decentralized, safe, and reliable manner, including the information and decision-making capability of AI which makes machines similar as capable as humans. This study is intended to present an updated systematic review of the integration of Blockchain and AI in various application areas. We have studied and summarized more than 100 research papers to present an updated version of the review. We also discuss the future of Blockchain technologies with AI. By integrating these two technologies results increases the security, efficiency, and productivity of the applications. Past works feature a few possible advantages of integration of Blockchain and AI, yet just give a restricted hypothetical system to depict forthcoming certifiable combination instances of the two advances. We survey and orchestrate surviving exploration on the integration of AI and Blockchain are other ways around to thoroughly build up a future research plan on the fusion of the two innovations. We also proposed an agenda to develop a secure system of cyber threat intelligence information exchange by using features of blockchain and artificial intelligence. This paper mainly focusses on explaining how collaboration of blockchain and AI gives immense boost in latest domains like Cybersecurity, Healthcare, Supply Chain Management, Finance and Banking and Social Media Analytics.

2 - Background

The convergence of blockchain, artificial intelligence (AI), and cloud computing is catalyzing a paradigm shift in developing secure, intelligent, and scalable digital infrastructures. This triad of technologies is increasingly utilized to improve performance, transparency, and trust in engineering-driven and socio-technical environments. This study systematically reviews the evolution, integration strategies, and applications of blockchain, AI, and cloud computing in digital ecosystems. The analysis is based on 108 peer-reviewed studies spanning the years 2012 to 2025. A comprehensive literature analysis was conducted to identify trends, synergies, and sector-specific implementations of these systems. The review explores how their integration supports real-world engineering and operational use cases. Blockchain contributes to decentralized architectures, secure data exchange, and identity verification. AI supports adaptive behavior, autonomous decision-making, and predictive analytics. Cloud computing offers the scalable infrastructure necessary for deployment. Key challenges addressed include interoperability, latency, security trade-offs, and resource allocation. Use cases in digital finance, supply chain management, and industrial automation demonstrate the effectiveness of this integration in building resilient, ethically aligned, and high-performance infrastructures. The findings offer valuable insights and technical considerations for engineers and architects seeking to design next-generation cyber-physical systems that are secure, intelligent, and socially responsive. Clinical Trial Number Not applicable. This article accentuates the extensive use of blockchain, cloud computing, and AI principles, emphasizing their crucial significance in the contemporary digital environment and paths in which blockchain technology may be employed to automate financial transactions, enhance the security of personal data and records, facilitate cross-border transactions, verify identities, execute smart contracts, utilize cryptocurrencies, and manage supply chains. This research systematically assesses the development and practical application of blockchain, cloud computing, and artificial intelligence technologies over the past decade. This study primarily focuses on monitoring the progress, practical applications, and benefits of blockchain technology in the context of financial transactions. This article probes the symbiotic relationship between artificial intelligence, cloud and blockchain technology, analyzing the evolution and fundamental concepts of their integration from 2012 to 2025. This paper also presents a detailed analysis of 108 research works acquitted over the past decade. The study provides a comprehensive examination of the current state and fundamental principles related to the integration of artificial intelligence, cloud, and blockchain technology. This paper offers a comprehensive analysis of the major obstacles confronted by blockchain technology, examining its wide-ranging applications over several industries and its socko role in transaction systems and the domain of cryptocurrencies.

Problem setting. The scientific work is devoted to the study of directions and measures of regulation of digital platforms and digital ecosystems. The functioning of digital platforms and ecosystems has its advantages, but citizens, businesses and the state face risks of information security, inequality of relations, loss of sovereignty. That is why in countries with a developed digital economy, the issue of state regulation of platforms and ecosystems is among the priorities. For Ukraine, which is significantly lagging behind in the application of breakthrough innovations, it is important to study world experience in order to develop its own state policy. analysis of recent researches and publications. The issues of regulation of digital platforms and ecosystems are discussed in the scientific literature. In the studies of scientists, in particular, B. Chew, M. Jacobides, B. Kyra, J. Wu, a wide range of aspects are considered, such as the complexity of developing regulatory solutions in the digital economy due to constant innovations, the specifics of protecting competition, data, and human rights in digital ecosystems, the need for regular rethinking and updating of state influence approaches, the need for big data analytics by regulators to identify patterns of behavior of subjects, the feasibility of building a national digital ecosystem of state regulation. The question of the specifics of the development and implementation of an effective state policy in the field of digital ecosystems for emerging economies remains unexplored. Purpose of the research is to substantiate theoretical provisions, analyze legislation and develop practical recommendations for the formation of a state policy for the regulation of digital platforms and ecosystems in Ukraine. article’s main body. To solve the problems of the ecosystem economy, the European Union adopted a number of regulatory documents regulating the activities of digital platforms, in particular the Law on Digital Markets and the Law on Digital Services. The regulatory requirements of the EU for digital platforms are aimed at reducing the risks of monopolization, unfair competition, security of data and operations, misuse of information. Similar requirements can and should be introduced in developing countries. It is important for all countries to ensure the security and stability of a single digital ecosystem. To measure progress in achieving the EU’s digital development goals by 2030, the EU has introduced monitoring measures. On the basis of monitoring, it is expedient for each country to form state supervision over the functioning of digital ecosystems. The systematic approach to the formation of state policy is as follows. The strategic goal of regulating digital platforms and ecosystems must be aligned with national goals. The chosen goal should be decomposed by levels and directions for systematic processing and implementation of strategic decisions. Policy tasks should be defined taking into account the goals, identified weaknesses, risks and threats. conclusions and prospects for the development. States should respond to the potential and existing dangers of the ecosystem economy through the implementation of a policy for the regulation of digital platforms and ecosystems and its systematic updating. In conditions of insufficient funding and in the case of technological lag, emerging countries can implement effective low-cost public policy by implementing regulatory requirements for digital platforms, state supervision in the field of ecosystem economy, development of regulatory measures taking into account a systemic approach. The perspective of further research is the systematic analysis of risks in digital ecosystems and the development of regulatory measures for their management.

Automated audiovisual migration systems are the only way to migrate large volumes of magnetic media to digital files. Historically, digital asset management (DAM) systems had no way to ingest analog assets en masse. By combining these two technologies, archives and commercial organizations can acquire digital versions of valuable content in a uniform, replicable manner. These legacy media assets can be monetized and repurposed, providing a lasting return on investment. Automated, consistent metadata collection further eases the transition from analog to digital, and enables quick discovery of assets within a DAM system. The evolution of audiovisual migration and restoration to automated systems positively impacts the DAM industry by leveling the field of assets, obviating the distinction between analog and digital, affording new business opportunities for DAM vendors.

Distributed Ledger Technologies (DLTs), including but not limited to Blockchain, have become key enablers of digital transformation across sectors. Their core attributes — immutability, traceability, decentralization, and tamper resistance — make them well-suited for secure and transparent data management. While initially linked to cryptocurrencies, DLTs are now applied in diverse domains such as identity verification, supply chain management, secure voting, and digital asset certification.In parallel, rapid advances in artificial intelligence (AI), particularly in Generative AI, are reshaping how data is generated, interpreted and applied. AI enables new forms of automation and decision-making, but it also introduces risks related to misinformation, digital fraud and security vulnerabilities. The convergence of DLT and AI offers a promising framework to address these challenges. As decentralized trust infrastructures, DLTs can enhance the transparency and integrity of AI systems by validating identities, certifying data provenance and securing sensitive assets such as contracts, medical records and intellectual property.This integration fits within the broader BIBI paradigm – Blockchain, AI, Big Data and the Internet of Things (IoT), where DLTs provide the foundation for secure, distributed coordination and auditability. Coupled with AI, they enable automated decision-making over large-scale, heterogeneous data, particularly from IoT devices, with applications in manufacturing, logistics and health care. This synergy also unlocks advanced functionalities, including decentralized identity, verifiable credentials and trusted automation, facilitating use cases such as tokenized assets and secure digital certification.Nevertheless, several technical and ethical challenges remain. The increasing computational demands of AI raise concerns about energy consumption, an issue already encountered in early DLT deployments. Energy-efficient consensus mechanisms, such as Proof-of-Stake and Byzantine Fault Tolerance, offer valuable insights for developing more sustainable AI infrastructures. Another key challenge is interoperability, as most DLT platforms operate in isolation. Enhancing interoperability would enable seamless data and asset exchange across heterogeneous ledgers, contributing to a more integrated and user-centric digital ecosystem.Looking forward, the convergence of DLT and AI holds significant potential for intelligent smart contracts, advanced traceability in critical sectors such as food safety and health care, and the development of digital twins for real-time simulation and optimization of organizational processes. This cross-disciplinary innovation can drive not only technological and economic growth but also democratic access to trusted, privacy-preserving, and sustainable digital systems.The Special Issue “Distributed Ledger Technologies and Artificial Intelligence” aims to offer a dedicated forum for researchers exploring the intersection of these two rapidly evolving fields. A total of 13 submissions were received, and following a rigorous double-blind peer review process, with at least three reviews per manuscript, three articles were selected for publication.The first article, “Energy: Reducing Latency in IoT DLTs for AI-Driven Real-Time Solutions” (Moya Perez et al., 2025), addresses the challenges of integrating IoT networks with DLT and AI, focusing on latency and energy efficiency. The authors propose Energy, a novel consensus algorithm designed for public DAG-based DLTs, which reduces or eliminates Proof-of-Work requirements. Experimental results demonstrate substantial improvements in low-payload scenarios common in IoT, enabling real-time AI model updates and efficient, secure data flow in large-scale environments.The second paper, “eFLEET: A Framework in Federated Learning for Enhanced Electric Transportation” (Ruiz de Gauna et al., 2025), presents a federated learning approach for optimizing autonomous electric vehicle routing in urban settings. Using traffic image analysis through computer vision and a DAG-based privacy layer, the framework selects optimal routes based on real-time and predictive data. The experiments show how the system improves traffic planning, enhances scalability and contributes to energy efficiency and pollution reduction in smart cities.The third contribution, “Enhancing the Viewing, Browsing and Searching of Knowledge Graphs with Virtual Properties” (Dibowski, 2024), introduces virtual properties as a user-interface enhancement for knowledge graph exploration. Defined using SHACL and evaluated via SPARQL queries, virtual properties enrich class scopes beyond direct neighbors. Successfully implemented at Bosch for over 100,000 users, this novel approach improves usability and expressiveness in knowledge-driven applications and sets a new direction for SHACL-based UI development.The authors would like to express their sincere gratitude to all the authors who submitted their contributions and to the anonymous reviewers for their thoughtful and rigorous evaluations. The authors also extend their appreciation to Professor Honghao Gao, Editor-in-Chief of IJWIS (Emerald), for his support in the publication of this special issue. The authors believe the selected papers represent cutting-edge research in this area and will be of great interest to the DLT and AI research communities.

The article considers the methodology of business process development in the digital business transformation. The aim of the study was to determine the dependence of sustainable enterprise development on the organisation of stakeholder engagement in the context of the digital ecosystem and the organisation of effective digital asset management.Taking into account the aim of the study, the main objectives are to study international experience of managing business development in digital transformation, determine the impact of ecosystems on business organisation and management of business processes and sustainability, and develop recommendations for business model formation and its alignment with the ecosystem market needs.The study resulted in the development of a sustainability framework in the digital ecosystem and proposals for digital ecosystem configurations that focus on giving and generating “value” when applying new ways and methods of “platform” interaction in the digital economy. Sustainability in digital transformation and ecosystematic business organisation depends on how digital assets are managed and approaches used to resource management. This process requires consideration of the methodology for business process development and research into changes in the external environment, identifying the possibility of integrating actors into the digital ecosystem development processes and defining tools for business process management.

This study explores the transformative role of artificial intelligence (AI), particularly machine learning (ML), in enhancing the detection and prevention of money laundering and fraud within the U.S. financial industry. The study aims to analyze how AI-driven techniques can significantly improve the accuracy, efficiency, and scalability of fraud detection systems. The study focuses on examining various machine learning algorithms, including supervised techniques like logistic regression and decision trees, as well as unsupervised methods such as clustering and anomaly detection. These techniques are utilized to analyze historical data, detect patterns, and identify suspicious transactions or fraudulent behaviors in real-time. The research method includes a comprehensive review of existing case studies and literature on AI applications in fraud detection, highlighting successful implementations of ML models in financial institutions. The findings reveal that machine learning models, such as random forests and support vector machines, have proven effective in detecting and preventing fraudulent activities with high precision and recall rates. Furthermore, the integration of AI with real-time data analysis capabilities enables continuous monitoring and immediate detection of irregularities. The study concludes that financial institutions in the U.S. must leverage AI advancements to enhance risk management systems, improve fraud detection, and mitigate the risks of money laundering. By adopting machine learning algorithms, financial organizations can stay ahead of emerging threats, ensuring the security of their operations and customer assets. Keywords: Artificial Intelligence, Machine Learning, Money Laundering, Fraud Detection, U.S. Financial Industry.

Until now most Digital Asset Management (DAM) solutions have been sold as licensed software applications. While Software-as-a-Service (SaaS) model DAM systems have been available since the dawn of DAM as a software category, market acceptance was hampered in the past by internet connectivity constraints and the high cost of online storage. The concurrent spread of broadband access and the steep drop in the price of spinning disk storage over the past several years has created conditions more favorable to a direct comparison of the hosted service and licensed software options for DAM solutions. The hosted service delivery option emerges as the superior choice in this comparison, setting the stage for explosive growth of the DAM SaaS category, especially in the corporate communications and marketing customer segment of the market.

Many grants, including grants from the National Science Foundation (NSF) require recipients to utilize data management plans; some grants, like those given by NSF, require recipients to place their data in searchable data repositories. It is difficult to find one data repository that meets every researcher's needs. To solve this problem, we utilized Purdue's Data Curation Profile Toolkit to interview researchers at our university and assist in developing data profiles/plans. These profiles provided the Digital Asset Management (DAM) project team with valuable information about the researchers' data needs. The DAM project team created a matrix of the researchers' data needs and used the matrix to evaluate the credentials of 24 Digital Asset Management Systems (DAMS). The team was able to select the DAMS that met the needs of researchers. Using this method, any university could select a DAMS and be confident that it would meet the needs of the researchers it was intended to serve.

CMS Watch's Digital & Media Asset Management Report – excerpted here – is a 200+ page guide to digital asset management (DAM), first published in May 2008, and now in its second edition. The report features head-to-head comparisons of over 20 DAM solutions, as well as a comprehensive overview of the business case for DAM and DAM best practices. The foundation of our research was dozens of interviews with everyday users of DAM systems, as well as extensive consultations with industry veterans. To order your full copy of this report, please visit http://www.cmswatch.com .