Rejaul Karim, Md. Mustaqim Roshid, Bablu Kumar Dhar, Abdul Waaje
This study explores the evolving role of green financial technology (Fintech) in sustainability-oriented financial innovation, with a particular focus on climate finance, digital innovation, and environmental governance. Using bibliometric methods, we analyze 72 peer-reviewed publications indexed in Scopus from 2019 to 2024 to map the intellectual structure and emerging trends of green Fintech research. Key technological domains, including blockchain-based carbon markets, AI-powered ESG analytics, and green digital payment systems, are frequently associated in the literature with several Sustainable Development Goals (SDGs), notably SDG 13 (Climate Action), SDG 12 (Responsible Consumption and Production), and SDG 8 (Decent Work and Economic Growth). This analysis reveals how digital financial innovations are conceptualized as mechanisms for facilitating access to green capital, strengthening carbon credit ecosystems, and enhancing transparency in climate-aligned investment. However, persistent barriers such as fragmented regulatory frameworks, cybersecurity risks, and digital divides are recurrently identified in the literature as constraints, particularly in emerging economies. Interpreted through Institutional Theory and Stakeholder Theory, the study highlights the importance of coordinated policy innovation, inclusive digital infrastructure, and harmonized ESG standards in shaping the diffusion and governance of green Fintech solutions. By positioning theory as an interpretive lens rather than an empirical test , this research offers a theory-informed, data-driven synthesis that contributes to the growing interdisciplinary discourse on digital finance as a potential enabler of low-carbon, inclusive, and resilient sustainability transitions.
Initial Coin Offerings (ICOs) have emerged as an innovative mechanism for raising capital, particularly for blockchain-based projects. However, the lack of regulatory oversight and the prevalence of low-quality information raise important questions about what truly drives ICO success. While existing literature focuses predominantly on technical and signalling variables, the role of investor decision-making remains theoretically underdeveloped and empirically underexplored. This paper addresses this gap by pursuing two objectives. First, we identify the drivers of ICO success using a probit model applied to an original sample of 535 ICOs conducted between January 2016 and May 2021. Second, we investigate investor decision-making patterns using a novel dataset of 200 active crypto-forum participants over the same period. Our results have three main findings, though with modest statistical strength than initially estimated. (I) Marketing channels are the most consistent predictor of ICO success across the sample period, clearing conventional significance thresholds only in the pooled sample (z = 1.90, p<0.10), with each additional channel raising the probability of soft-cap achievement by approximately 1.0 percentage point. (II) Team presentation and video presentation show no meaningful influence on success in any period. (III) Whitepaper availability is not statistically significant even in pooled sample, reinforcing rather than qualifying its irrelevance as a predictor; the number of accepted cryptocurrency price speculation rather than project fundamentals, consistent with mood and sentiment dominating information-based decision making in ICO markets, though this finding should be read alongside the data limitations discussed in 3.B. These findings contribute to the behavioural finance literature by providing an operational definition of ‘investor mood’ and demonstrating its empirical relevance in crypto markets. We conclude that understanding investor mood is not a secondary question but a necessary complement to technical analysis of ICO success.
Abstract Digital financial fraud and the financial-literacy defences meant to counter it have become a fast-growing, cross-disciplinary research concern, yet the conceptual and intellectual structure of this combined field has not been systematically mapped. This study presents a bibliometric analysis of research at the intersection of digital fraud susceptibility and financial literacy, using metadata retrieved from the open bibliographic database OpenAlex and analysed with VOSviewer. A dataset of 523 documents published between 2015 and 2026 was examined through publication-trend analysis, citation and source analysis, co-authorship analysis, term co-occurrence mapping, and bibliographic coupling. Annual output grew steeply, with roughly 60% of the corpus appearing in 2023–2025, indicating a young and rapidly expanding field. Co-occurrence mapping of 176 concepts produced six substantive thematic clusters: accounting, auditing and forensic controls; financial literacy and consumer psychology; artificial-intelligence and machine-learning fraud detection; digital payments, fintech and cyber-enabled fraud; law, regulation and consumer protection; and information security, privacy and identity. The digital-payments and fintech cluster was the most recent on average, and fintech, digital literacy, financial inclusion and blockchain surfaced as research fronts. Collaboration was highly fragmented, with no large connected co-authorship component, and direct-citation linkage within the corpus was sparse, although bibliographic coupling revealed greater thematic cohesion. The analysis charts the contours of an emerging field and identifies under-integrated areas—particularly the gap between behavioural financial-literacy research and technical fraud-detection research—that warrant coordinated attention. Limitations relating to the OpenAlex concept classifier and single-database coverage are discussed.
Tokenized representations of cash-like instruments, comprising stablecoins, tokenized money market funds, and tokenized real-world assets, are increasingly positioned as core on-chain financial infrastructure, yet empirical evidence on how these instruments behave in practice remains limited. This paper reports a comparative empirical examination of public transaction-level blockchain data, covering adoption patterns, usage dynamics, and operational characteristics across three parallel case studies: USDC (stablecoin, Circle), BENJI (tokenized money market fund, Franklin Templeton), and BUIDL (tokenized U.S. Treasury, BlackRock via Securitize). On-chain metrics covering issuance and redemption activity, transfer behavior, wallet concentration, velocity proxies, and cross-chain deployment are interpreted against a four-layer reference architecture (asset representation, control-plane governance, settlement and finality, and composability). Results reveal systematic behavioral differences aligned with product intent and governance design: stablecoins function as high-velocity settlement instruments with broad address distribution, while tokenized investment products exhibit batch-oriented issuance, low circulation intensity, and concentrated holdings consistent with institutional custody and regulatory constraints. A live-pipeline extraction for BUIDL on Ethereum over the 90-day window ending 31 January 2026 yields a holder-level Gini coefficient of 0.8706 with a bootstrap 95% confidence interval of [0.7672, 0.9208] and a top-ten concentration share of 98.96%. Cross-chain deployment expands access but preserves reliance on dominant settlement layers. These patterns constitute an evidence-based framework for evaluating tokenized finance as production-grade financial market infrastructure.
Decentralised finance (DeFi) is a relatively new trend in finance that uses blockchain, smart contracts, and distributed ledger technology to offer financial services in a decentralised manner. Although scholars have made many theoretical advances in decentralised finance in recent years, knowledge of its theoretical structure and future research areas remains limited. This is why this study provides a bibliometric analysis of 1002 articles on DeFi published in Scopus between 2012 and 2026. The analysis uses performance analysis and a science mapping approach based on citation analysis, co-authorship, bibliographic coupling and keyword co-occurrence analysis. The results reveal a remarkably high annual growth rate of 39.34% and DeFi’s dynamism and interdisciplinary nature. The three main countries involved in DeFi research are the USA, China, and the UK. Management Science, Energy Economics and Technological Forecasting and Social Change became the main scientific journals for disseminating knowledge about DeFi. Analysis of thematic changes showed a transition of scientific interests from blockchain and cryptocurrencies to new topics, like artificial intelligence, sustainability, governance, and financial inclusion. Overall, the current study provides a better understanding of the intellectual, conceptual, and social basis of DeFi and highlights possible research areas in the use of artificial intelligence in DeFi, decentralised governance, and sustainable digital financial system development.
This informative document explores the evolving digital asset landscape, covering cryptocurrency, NFTs, blockchain technology, Web3, and emerging market trends. It provides readers with practical insights into digital ownership, market developments, and the importance of research when evaluating opportunities in the growing blockchain economy. Collective Shift
Decentralized Finance (DeFi) refers to an open financial ecosystem built on blockchain technology that does not require the participation of centralized institutions. The technology and operational mechanisms it employs represent a significant "paradigm mismatch" with the current financial regulatory framework. This paper examines the comprehensive impact of DeFi on existing financial regulation from multiple perspectives, including the blurring of regulatory authority and a lack of accountability; the difficulty in identifying regulatory targets and the ambiguity in determining their nature; the ineffectiveness of regulatory rules and the absence of relevant provisions; overlapping jurisdictions, and difficulties in enforcement. Through a comparative study of regulatory experiences in the United States, Europe, and other regions, this paper proposes solutions such as shifting the existing regulatory philosophy toward functional regulation, embedding compliance requirements into the underlying technology at the institutional level, and strengthening international cooperation at the operational level, while also discussing the specific context in China. This paper identifies a threefold paradigm mismatch between decentralized finance and traditional financial regulation, giving rise to multiple regulatory challenges such as difficulties in holding entities accountable, ambiguity in defining regulatory targets, ineffective regulatory rules, and obstacles to cross-border enforcement. A comparison of regulatory practices in the U.S. and Europe reveals that it is difficult for any single country to independently manage the risks associated with globalized DeFi.
Tapasi Bhattacharjee, Amalendu Singha Mahapatra, Dipika Pramanik
Educational crowdfunding has emerged as a promising approach to provide educational resources to underprivileged communities. Conventional systems often suffer from a lack of transparency, weak accountability, inefficient allocation of funds, and inadequate traceability of resource use. To address these issues, the present study proposes an intelligent and efficient educational supply chain management system, “EduDonateBlock.” It uses a blockchain-based crowdfunding framework to ensure transparency, accountability, and efficiency. Decentralization, immutability, and verifiable transactions are supported in educational campaigns. The entire workflow is decomposed into modular smart contracts. These are the identity and access contract (IAC), campaign and donation contract (CDC), verification and allocation contract (VAC), and supply chain and tracking contract (SCTC). These contracts are designed to ensure traceability, accountability, and efficient resource allocation among donors, educational institutions, and administrators. The mathematical framework of EduDonateBlock determines the optimal level of blockchain transparency. This minimizes the Total Expected Cost (TEC) of smart-contract operations. Numerical analysis identifies an optimal transparency level of 87.16% on-chain integration. This finding underscores the economic trade-off between transaction costs and the benefits of automation, operational efficiency, and reduced fraud risk. The proposed framework achieves a campaign success probability of 89.45% and an institutional payoff of Rs. 11,335.99. Furthermore, executing smart contracts requires 0.0044 ETH, and the average latency remains at 6.25 s. The simulation results show that EduDonateBlock offers a more efficient, reliable, and transparent solution for decentralized educational crowdfunding and socially impactful digital supply chains.
This study maps the development, collaboration patterns, citation structure, and thematic evolution of research on blockchain technology in the waqf sector. A bibliometric analysis of 417 Scopus-indexed publications published from 2006 to 12 July 2024 was performed using Bibliometrix in RStudio and VOSviewer. The analysis covered publication trends, influential sources and contributors, country productivity, citation impact, collaboration networks, and keyword co-occurrence. The results show increasing scholarly attention to the intersection of blockchain, Islamic finance, fintech, and waqf management. Malaysia and Indonesia emerged as the most productive and most cited countries, while an international co-authorship rate of 29.74% indicated moderate cross-border collaboration. Keyword analysis revealed that the field is anchored in Islamic finance, fintech, blockchain, and waqf, with growing attention to cash waqf, crowdfunding, financial inclusion, digital transformation, smart contracts, cybersecurity, and technology adoption. However, these patterns demonstrate scholarly attention and thematic associations rather than empirical proof of blockchain’s operational benefits in waqf institutions. This study identifies priority gaps in empirical implementation, Shariah governance, stakeholder adoption, technical feasibility, and socioeconomic impact evaluation of blockchain-enabled waqf systems.
Abstract: The evolution of monetary systems has transformed human civilization from simple barter exchanges to sophisticated digital financial ecosystems powered by blockchain technology. This review examines how barter systems evolved into con-temporary virtual currencies across history and assesses how cryptocurrencies fit into the circular economy. The study explores the shortcomings of conventional monetary systems and looks at how decentralized, transparent, and effective forms of economic transaction have been made possible by digital currencies like Bitcoin. Additionally, the study examines how blockchain technology might be used to support waste reduction, sustainability, resource efficiency, and transparent supply chain management. The study also assesses the difficulties posed by virtual currencies, such as market volatility, cybersecurity threats, regulatory ambiguity, and environmental issues pertaining to cryptocurrency mining. The review identifies significant research gaps and future prospects for incorporating virtual currencies into sustainable economic systems by synthesizing the body of existing work. The results indicate that through openness, decentralization, and technological innovation, blockchain-enabled financial systems have a great deal of potential to promote circular economy goals. Keywords: Virtual Currency, Cryptocurrency, Bitcoin, Blockchain, Circular Economy, Sustainable Finance, Digital Economy, Decentralization, Green Finance, FinTech, Supply Chain Management
The tokenization of Real-World Assets (RWAs) represents a paradigm shift in bridging traditional financial instruments with decentralized infrastructures. However, as the market transitions from proof-of-concept to institutional scale, it faces a critical structural bottleneck: the "walled garden" liquidity crisis. Driven by stringent regulatory requirements, tokenized assets are currently deployed across fragmented, permissioned blockchain networks utilizing static, hard-coded compliance logic. This siloed architecture inherently restricts cross-chain mobility, fracturing secondary market liquidity and necessitating redundant authentication processes across jurisdictions. This paper proposes a comprehensive architectural framework to resolve the interoperability trilemma inherent in regulated digital assets. By synthesizing recent advancements in cross-chain messaging protocols and Zero-Knowledge Proofs (ZKPs), we present a model for dynamic compliance. This framework utilizes Decentralized Identifiers (DIDs) and off-chain verifiable credentials to decouple regulatory logic from underlying asset ledgers, enabling seamless asset transfer across heterogeneous blockchains without compromising privacy or jurisdictional adherence. Ultimately, this research provides a technical and regulatory roadmap for policymakers and protocol developers to foster a unified, globally liquid market for tokenized RWAs.
Abstract This research examines the adoption of blockchain and fintech innovation in emerging markets, focusing on the drivers, barriers, and regulatory dynamics. Using a cross-sectional quantitative survey of 114 fintech leaders and entrepreneurs across 60 emerging-market countries, the research examines perceptions of blockchain’s role in cost reduction, efficiency, and financial inclusion through decentralized finance (DeFi), tokenized assets, and digital wallets. Findings show respondents broadly agree that blockchain fosters new business models and competitive advantage, perceive strong benefits in transparency, cost reduction, and efficiency, and hold favorable views of regulatory support, clear guidelines, and sandboxes, while still recognizing regulatory, organizational, and technological barriers to adoption. Fintech leaders reported significantly higher familiarity and stronger belief in blockchain’s potential than entrepreneurs. The study applies Institutional Theory, the Technology–Organization–Environment framework, and Disruptive Innovation Theory to highlight policy, organizational, and technological implications. Because the sample was purposive and responses were uniformly positive, the findings describe the perceptions of engaged practitioners rather than statistically generalisable or audited adoption outcomes.
As the blockchain and decentralized finance (DeFi) ecosystems continue to expand and mature, rug pull scams involving meme coins are occurring with increasing frequency, posing a threat to the security of investors' assets and the healthy development of the industry. Rug Pull scams are characterized by extremely low deployment costs, covert execution, rapid fund transfers, and high detection difficulty. Traditional manual reviews or fixed rules struggle to meet real-time early warning requirements, and existing detection methods generally suffer from issues such as a single feature dimension, inadequate handling of class imbalance, and weak model generalization and interpretability. To address these shortcomings, this paper focuses on the detection of Ethereum-based rug pull scams. First, we clarify their definitions, types, and harm mechanisms, and construct a multi-dimensional feature system based on dimensions such as malicious smart contract design, on-chain transaction anomalies, liquidity manipulation, and social media disclosures. Next, using the "Second Uncle Coin"(token symbol: BOBU) case as an example, we reconstruct the attack process and derive quantitative detection metrics. Subsequently, a risk detection model based on a Multi-Layer Perceptron (MLP) is designed. We employ a combined strategy of SMOTE oversampling and Focal Loss to address the issue of sample imbalance, dynamically search for optimal thresholds to balance precision and recall, and incorporate gradient pruning and early stopping to enhance training stability. Experiments show that the model achieves an accuracy of 0.927, an F1 score of 0.787, and an AUC-ROC of 0.952 on the test set, outperforming traditional methods. Finally, a visualizable web-based detection system is developed using the Flask framework, enabling batch risk assessment, high-risk ranking display, and result export functions.
This paper investigates whether prediction market settlements create incentives for temporary price pressure in Bitcoin spot markets. Using high-frequency data from February 2025 to January 2026 and actual contract-level data from Polymarket and Kalshi to identify economically relevant contract strikes, we document basis divergence between settlement oracle exchanges (Coinbase) and non-constituent exchanges (Binance) during expiry windows. Employing a difference-in-differences framework with month fixed effects, we find that a one standard deviation increase in strike proximity is associated with a 6.7 basis point constituent exchange price deviation during settlement windows. The estimate is precise under the baseline minute-level HAC specification, while exact paired-month permutation inference based on 12 settlement events yields p=0.0256; equal-weight event aggregation produces a larger negative estimate, indicating event heterogeneity. Monthly directional patterns are suggestive, though stricter event-level and above-versus-below-strike tests provide mixed evidence on directional asymmetry. Taken together, these findings provide reduced-form evidence consistent with settlement-related incentives and may raise broader settlement-design considerations for decentralized financial systems. However, the analysis does not directly observe trader intent or the underlying mechanism.
This chapter explores the transformative potential of blockchain and artificial intelligence (AI) in revolutionizing green finance. It begins by examining the role of digital transformation in driving sustainable financial practices, highlighting the integration of blockchain and AI. The chapter delves into blockchain's applications in enhancing transparency, traceability, and security within green finance, particularly through smart contracts and decentralized finance solutions. It further discusses AI's contributions to improving risk assessment, ESG evaluation, and combating greenwashing. The synergies between blockchain and AI are explored, showing how their combined use optimizes sustainability-focused investments. Additionally, the chapter addresses regulatory and ethical considerations surrounding these technologies. Finally, it discusses emerging trends and opportunities in green finance, providing insights into the future of sustainable financial systems driven by technological innovation.
The rapid increase in cryptocurrency adoption among Generation Z in Indonesia has raised concerns regarding investment decision-making in highly volatile digital asset markets. This study examines the influence of herding behavior, social media exposure, and fear of missing out (FOMO) on cryptocurrency investment decisions, with financial literacy as a moderating variable. A quantitative approach was employed using survey data from 200 Generation Z cryptocurrency investors in Pontianak City. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The results show that herding behavior and social media significantly influence investment decisions. Fear of missing out also affects investor decision-making. Financial literacy moderates the relationship between herding behavior and investment decisions as well as between social media and investment decisions, but does not moderate the relationship between FOMO and investment decisions. These findings indicate that cryptocurrency investment decisions among Generation Z are influenced by social interactions and emotional biases.
Diana Bonilla Guzmán, Sofía de las Nieves García Gámez, Rubén Mora-Ruano, Alvaro-Antonio Salas-Suárez
This study aims to identify the extent to which a country's level of governance implicitly determines and encourages the use of cryptocurrencies, and the main elements associated with the use of alternative currencies to traditional ones. The methodology used is a descriptive analysis of the variables, an econometric analysis through an ANOVA, and the application of a truncated regression model, which aims to bring the research closer to the possible correlation between governance indicators and the rate of adoption of cryptocurrencies. The study concludes that countries with low levels of governance are directly related to the greater adoption of cryptocurrencies. To the best of our knowledge, this study is the first to analyse the relationship between cryptocurrency adoption and institutional governance by comparing two regions with different levels of development. The research is limited by the existence of other factors that influence the analytical framework of cryptocurrency adoption, but the availability of data has allowed the present study to focus on governance aspects. Now, despite the fact that the governance indicators present a global analysis in terms of their measurement, the relevant aspects of each country are not specified. The adoption of cryptocurrencies in some countries may not be strongly related to governance aspects but rather to the friendly regulations that have been implemented.
Financial Technology (FinTech) is reshaping the worldwide financial industry by introducing innovations like digital transactions, artificial intelligence (AI), blockchain, mobile banking, data analysis, and integrated finance. These advancements are improving the effectiveness, openness, and availability of financial services, fostering financial inclusion, and decreasing reliance on traditional banking systems. This research investigates how FinTech plays a crucial role in stimulating innovation, inclusivity, and digital change in the financial landscape. It also delves into the opportunities arising from digital financial services and the obstacles related to cybersecurity, data protection, adhering to regulations, and ethical considerations. The research is grounded in an examination of recent literature, industry studies, and policy papers to grasp present trends and future advancements in FinTech. The results indicate that FinTech has emerged as a vital facilitator of sustainable financial expansion and economic progress. The research offers valuable perspectives for scholars, decision-makers, financial organizations, and industry professionals to comprehend the direction of digital finance.
Cryptocurrency has emerged as one of the most significant developments to accompany the digitization of global finance, and its footprint in India has expanded rapidly despite an unsettled regulatory environment. This paper examines how Indian investors perceive the opportunities and risks associated with cryptocurrency and blockchain technology, and evaluates whether their level of awareness shapes that perception. A structured questionnaire survey was administered to 158 respondents drawn from different age groups, educational backgrounds, occupations, and income levels in Karnataka, and the resulting data were analyzed using percentage analysis, frequency distribution, and the Chi-square test of independence. The findings indicate that a large majority of respondents, particularly those aged 21-30, view cryptocurrency and blockchain as tools capable of improving transparency, financial inclusion, and entrepreneurship, while simultaneously expressing concern over price volatility, cybersecurity threats, and unclear taxation rules. The Chi-square test confirmed a statistically significant association between investor awareness and perception of cryptocurrency (calculated value 19.41 against a critical value of 9.488 at 4 degrees of freedom and the 5 percent level of significance), leading to rejection of the null hypothesis. The study concludes that a clear, balanced regulatory framework combined with investor-education initiatives would allow India to capture the innovation potential of digital assets while containing the risks associated with their adoption.
Alternative finance platforms, including crowdfunding, peer-to-peer lending, equity-based platforms, and token-based fundraising mechanisms, have become important channels for financing entrepreneurial, social, and investment-oriented initiatives. Yet their reliance on digital intermediation, dispersed participation, and information asymmetry creates opportunities for fraud, undermining trust, investor protection, and platform sustainability. This study provides a systematic review of fraud detection and prevention in alternative finance, with crowdfunding emerging as the most extensively represented empirical domain. Methodologically, the paper combines a PRISMA-guided systematic literature review with a hybrid topic-modeling strategy that integrates neural topic modeling and probabilistic refinement, thereby supporting both transparent corpus selection and data-driven thematic synthesis. The findings show that Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and blockchain-based mechanisms are recurrently discussed as promising tools for detecting, preventing, or mitigating fraud. AI and ML approaches are mainly used to identify anomalies, suspicious textual patterns, behavioral signals, and transaction irregularities, while blockchain-based approaches are associated with transparency, traceability, smart contracts, and conditional fund release. The review also shows that fraud differs across alternative finance models, ranging from campaign misrepresentation and intentional and premeditated non-delivery in crowdfunding to borrower or platform misreporting in lending-based models and misleading disclosures or white-paper manipulation in ICO/STO contexts. A central challenge across the literature is the scarcity of labeled fraud data, which limits the use and benchmarking of supervised ML models. Overall, this study contributes by linking a reproducible hybrid SLR methodology to a structured synthesis of fraud types, platform-specific vulnerabilities, and AI-, ML-, and blockchain-based detection strategies in alternative finance.
This paper seeks to develop an empirically tested theoretical model that measures the block chain related awareness, confidence, and perceived relevance regarding finances among employees in the Turkish financial services sector. From the existing literature on blockchain adoption, the acceptance of fintech, and trust-based investment behavior, the authors developed an initial item pool consisting of 14 items. Content validation was done through experts followed by a pilot. Primary data was collected from 450 finance professionals working in the banking, treasury, risk, and accounting departments of different companies within Istanbul. The questionnaire was filled out by the respondents during the period March to April 2025 and was distributed online. Internal consistency was calculated using Cronbach’s alpha coefficient, while the structure of the underlying scale was investigated by Principal component analysis with oblique rotation. This analysis was complemented with item analysis through corrected item-total correlations and calculation of communalities. The data quality for conducting factor analysis were validated by KMO and Bartlett’s test of sphericity. From the results of the two-factor solution, the total variance explained was 85.83%. The first factor covered perceptions pertaining to blockchain awareness and informational engagement while the second predominately covered confidence in blockchains financial functionality and trustworthiness. The final structure is comprised of 14 items that have high loadings and little redundancy. The results indicate that the scale is not only clear-cut conceptually and statistically, but also provides a consistent measure for further studies regarding the perception and acceptance of technology in the finance domain.
As the growth of the FinTech platforms continues, there is an increasing demand for intelligent, secure and traceable solutions that can provide real-time detection of fraudulent transactions and shield financial records from manipulation. In this research, an Artificial Intelligence-powered blockchain framework, combining machine learning for fraud detection and permissioned blockchain for validation, was proposed. It was found that ensemble models performed better than a linear baseline. The overall best balance of precision, specificity and F1 score was obtained with the Random Forest model, and the highest precision–recall was obtained with the Extra Trees model, with fraud recall slightly better. In addition, feature-importance analysis revealed a small number of transaction attributes, which were anonymised, that most significantly affected fraud classification. The chosen model was then connected to a prototype of a chained hash blockchain that preserved the hashes of transactions, the time, the predicted probability of fraud, the validation result, and the version of the model. Through hash inconsistency, the prototype was able to detect any transaction modifications which might have been made on purpose and successfully ensured ledger integrity. The results illustrate how both AI and blockchain technologies complement each other. AI is effective in detecting fraud accurately and on time, and blockchain enhances the traceability, auditability and tamper resistance of transactions.