A Systematic Review of Artificial Intelligence Applied to Compliance: Fraud Detection in Cryptocurrency Transactions

Abstract

Rising financial fraud impacts industries, economies, and consumers, creating a need for advanced technological solutions. Compliance frameworks help detect and prevent illicit activities like money laundering, market manipulation, etc. However, with the rise of cryptocurrencies and blockchain, traditional detection methods are ineffective. As a result, Artificial Intelligence (AI) has emerged as a vital tool for combating fraud in the cryptocurrency sector. This systematic review examines the integration of AI in compliance for cryptocurrency fraud detection between 2014 and 2025, analyzing its evolution, methodologies, and emerging trends. Using RStudio (Biblioshiny) and VOSviewer, 353 peer-reviewed studies from leading databases including SciSpace, Elicit, Google Scholar, ScienceDirect, Scopus, and Web of Science were analyzed following the PRISMA methodology. Key trends include the adoption of machine learning, deep learning, natural language processing, and generative AI technologies to improve efficiency and innovation in fraud detection. However, challenges persist, including limited transparency in AI models, regulatory fragmentation, and limited access to quality data, all of which hinder effective fraud detection. The long-term real-world effectiveness of AI tools remains underexplored. This review highlights the trajectory of AI in compliance, identifies areas for further research, and emphasizes bridging theory and practice to strengthen fraud detection in cryptocurrency transactions.

1. Introduction

Financial fraud lies in its persistent and evolving threat across both external and internal domains. External fraud, including credit card fraud, insurance deception, online payment fraud, and loan scams, along with internal fraud, such as manipulation of financial statements, money laundering, and corruption, continue to cause significant economic and organizational harm (Ali et al., 2022; Hamza et al., 2023). Traditional detection methods are increasingly ineffective as fraudsters constantly adapt their tactics, underscoring the need for more sophisticated solutions. Artificial intelligence (AI) methodologies have been applied to both fraud domains, with studies showing that ensemble methods, which combine high detection accuracy with compliance-supporting features, yield the most robust results (Elmougy & Manzi, 2021; Pettersson Ruiz & Angelis, 2021; Sharma & Babbar, 2023). However, despite institutions having the technical capacity to deploy advanced AI systems, collecting sufficient deceptive data for training these algorithms remains costly and resource-intensive. This highlights the critical need for research that explores effective AI-based approaches to enhance fraud detection and mitigate risks in financial systems.

Conversely, compliance frameworks provide standardized procedures, reporting requirements, and oversight mechanisms that help detect and prevent illicit activities such as money laundering, market manipulation, and unauthorized transactions. By integrating compliance measures with advanced technologies, particularly Artificial Intelligence (AI), organizations can enhance real-time monitoring, identify suspicious patterns, and reduce the risk of financial crime (Herian, 2025).

These AI methodologies include machine learning, natural language processing, generative AI, etc., which improve the accuracy and transparency of fraud detection systems, streamline compliance processes and improve predictive accuracy. AI methodologies offer a range of key benefits that significantly enhance fraud detection and compliance efforts. One of the primary advantages is increased accuracy, as AI reduces false positives and ensures legitimate transactions are not wrongly flagged. Real-time detection capabilities allow organizations to monitor and identify suspicious activities instantly, enabling swift responses that help prevent financial losses. AI systems are also highly scalable, efficiently managing large volumes of data and adapting to organizations of all sizes. In terms of cost efficiency, automation minimizes the need for manual reviews, lowering operational expenses while boosting effectiveness. Furthermore, AI supports compliance by automating monitoring and reporting processes, ensuring adherence to evolving standards. Its adaptability allows models to learn from new data, continuously evolving to counter emerging fraud tactics. With predictive capabilities, AI can anticipate potential fraud risks, allowing for proactive risk management. Collectively, these benefits empower organizations to protect their assets, streamline compliance workflows, and stay ahead of sophisticated financial crime.

Regarding the Fintech and blockchain ecosystem, the recent Proposal for a Regulation on Markets in Crypto-Assets (MiCA) marks an important step forward in regulating the sector, including requirements for registration, oversight, and reporting of suspicious activities by crypto-asset service providers. In this context, balancing technological innovation with compliance will be crucial for future success in this field.

Drawing on insights from 353 studies gathered from SciSpace, Elicit, Google Scholar, Web of Science, Scopus and ScienceDirect. This study seeks to provide insights to investors, developers, regulators, and researchers working of technology and policy in global financial markets, focusing on the application of AI methodologies to compliance processes for detecting cryptocurrency fraud.

This systematic review addresses a critical gap in interdisciplinary artificial intelligence applied to compliance research. Its objectives are threefold: (1) to examine the current status of publications about Artificial Intelligence (AI) applied to compliance in the fraud detection in cryptocurrency transactions; (2) to determine what a are the Artificial Intelligence (AI) methodologies applied to compliance in the fraud detection in cryptocurrency transactions; (3) to examine how do machine learning, deep learning algorithms, generative AI and natural language processing contribute to AI-based compliance in cryptocurrency transactions.

The above arguments lead to our central research questions:

• What is the current status of publications about Artificial Intelligence (AI) methodologies applied to compliance in the fraud detection in cryptocurrency transactions?
• What are the Artificial Intelligence (AI) methodologies applied to compliance in the fraud detection in cryptocurrency transactions?
• How do machine learning, deep learning algorithms, generative AI and natural language processing contribute to AI-based compliance in cryptocurrency transactions?

This research offers several important contributions to the academic literature. First, it provides a synthesis of the current state of artificial intelligence methodologies applied to compliance in detecting fraud within cryptocurrency transactions. This overview covers the key methods and techniques in a specialized area that intersect finance and compliance, falling under the broader domains of AI-driven finance and decentralized finance (DeFi). The integration of AI in finance represents a significant technological revolution that is transforming industries across the board. Second, the study identifies current status of the publications, key trends, and research gaps, highlighting areas that require further exploration. The insights derived from this study have the potential to guide future research directions.

This paper is organized as follows: Section 2 discusses the materials and methods; Section 3 outlines the results of the analysis; and Section 4 discusses the implications of the findings. Finally, Section 5 summarizes the conclusions.

2. Materials and Methods

2.1. Methodological Framework

This research adopts a quantitative design, as it is focused in bibliometric analysis, which systematically evaluates the production and dissemination of scientific knowledge. Through the use of statistical tools, bibliometrics represents a rigorous mechanism for quantifying and interpreting patterns in the dissemination of scientific knowledge. Such analysis supports the exploration of research output, contributing to the mapping of intellectual communities, the identification of collaborative networks, and the characterization of research trends (Quevedo-Silva et al., 2016; Chueke & Amatucci, 2015).

2.2. Data Collection

This study adopts the standard approach outlined by Page et al. (2021) under the PRISMA methodology (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), which aims to support transparent and comprehensive reporting of systematic reviews and meta-analyses. PRISMA offers a structured checklist and flow diagram to promote completeness, reproducibility, and clarity in review reporting.

For the purposes of data collection, the PRISMA protocol (Moher et al., 2015) was adapted to the research context, structuring the process into four key phases: identification, screening, eligibility, and inclusion (Figure 1). During the identification phase, the Population, Intervention, Comparison, Outcomes, and Context (PICOC) framework was applied to construct the search query (and their combinations): “Fraud detection” AND “Artificial Intelligence” AND “Compliance” AND “Cryptocurrency” AND (“machine learning” OR “deep learning” OR “ natural language processing “ OR “ generative AI), which was used across keywords, titles, and abstracts (Navarrete et al., 2018). The search was performed on 31 August 2025.

This process yielded 2078 records sourced from seven leading academic databases: Scopus (802 articles), Web of Science (44 articles), Elicit (100 articles), SciSpace (298 articles), Google Scholar (44 articles) and ScienceDirect (790 articles).

The selection of these databases was guided by the objective of capturing a comprehensive range of contemporary scientific sources, ensuring the convergence of academic rigor with up-to-date research contributions. Three researchers (L.R., M.O., and A.L) screened each study based on the title and abstract to determine its eligibility for the full-text review stage. Additionally, the reference lists of the selected articles were examined to identify any studies that may have been overlooked during the initial search.

The subsequent screening stage focused on the elimination of duplicate records across the databases, which refined the dataset to 713 distinct articles. Eligibility assessment was then conducted to evaluate the degree of alignment between each article and the research objectives, resulting in a final sample of 353 publications. This review exclusively considered journal articles, chapter book, book, and reviews classified within the subject areas of “Economics, Econometrics and Finance,” “Business, Management and Accounting,” and “Computer Science,” provided they were accessible for download. Furthermore, only works published between 2014 and 2025 were retained, thereby ensuring temporal relevance and consistency with contemporary academic contributions. Filters also included publications written in language (English), and peer-reviewed status. Advanced search options excluded patents, preprints, proceeding journals, posted content and citations, focusing on academic articles and books due to the high risk of bias. To enhance transparency and replicability, the review protocol was registered in the Open Science Framework repository (https://osf.io/h5rj2/overview) accessed on 6 September 2025.

2.3. Analysis Framework

This study employed tools such as VOSviewer and RStudio (Biblioshiny) version 5.0 to assess the performance of different stakeholders in the scientific community and to identify the most influential contributors (Aria & Cuccurullo, 2017; Secinaro et al., 2020). These tools are widely recognized for their effectiveness in analyzing bibliometric data (Ahmed et al., 2022).

The VOSviewer tool (van Eck & Waltman, 2017) was utilized to examine emerging trends in artificial intelligence (AI) methodologies and compliance within the context of cryptocurrency fraud detection. VOSviewer version 1.6.20 (www.vosviewer.com) is a bibliometric software that processes datasets of academic literature, creates knowledge maps, and generates visualizations to illustrate relationships and trends (Huang et al., 2022). Additionally, RStudio was employed through the Bibliometrix package version 5.0 to facilitate bibliometric analyses via a graphical interface.

The evaluation was structured around three broad dimensions: general research trends, journals, and authors. The first stage involved a temporal analysis of the annual publication volume, offering an overview of how scientific production on the subject has evolved over time. This approach made it possible to detect shifts and patterns in scholarly interest. In addition, a geographical perspective was also included by assessing the distribution of publications by country, as emphasized by Ahmed et al. (2022). To determine the most influential journals, Bradford’s Law was applied, which facilitated the identification of the core outlets that make the most substantial contributions to the field (Chueke & Amatucci, 2015). Moreover, the use of Biblioshiny and VOSviewer tool provided additional insights into the thematic landscape of the field, including the most frequently studied topics, areas still requiring further investigation, the historical evolution of research themes, and the topics that remain underexplored.

3. Results

3.1. Publication Trends

Figure 2 provides an overview of the principal information regarding the dataset under study. The bibliometric database spans the period 2014–2025 (until August 2025), comprising 353 publications derived from 238 distinct sources. The field demonstrates a notable annual growth rate of 52.67%, reflecting its rapid development and increasing scholarly interest.

In terms of authorship, a total of 886 researchers contributed to the corpus, of which 257 publications were single authored. Collaboration is evident, with an average of 3.1 co-authors per document, while 7.65% of the works involved international co-authorship, highlighting a moderate level of global research collaboration. Regarding content characteristics, the collection contains 621 author-supplied keywords. The documents have an average age of 1.39 years, suggesting the literature is relatively recent. Citation analysis shows an average of 3.7 citations per document.

Figure 3 presents the annual scientific production, revealing an exponential growth rate in recent years. The results indicate that research on artificial intelligence methodologies applied to compliance in cryptocurrency fraud detection has gained significant momentum since 2022, coinciding with the acceleration of AI applications in the financial sector. The increase in publications in recent years reflects the importance of the topic and the growing academic interest it generates in this field of study.

Before 2020, research into cryptocurrency compliance was limited, with only a handful of studies primarily focused on conceptual frameworks that integrated traditional rule-based systems with basic machine learning techniques. These early efforts largely centered on transaction monitoring and anomaly detection, leveraging supervised learning models such as logistic regression and decision trees to identify suspicious patterns. However, applications specifically targeting cryptocurrencies were rare, reflecting the relatively low regulatory focus and the emerging nature of digital assets at the time.

Between 2020 and 2022, growing awareness of money laundering risks and fraudulent activities in cryptocurrency markets spurred significant advancements in research. Scholars and practitioners began leveraging deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks to detect fraud within increasingly large and complex transaction datasets. A notable development during this period was the emergence of graph-based AI, particularly Graph Neural Networks (GNNs), which enabled the analysis of blockchain transaction networks to identify suspicious clusters and relational patterns. Additionally, this phase saw the integration of AI with compliance and supervisory technologies (RegTech and SupTech), paving the way for more efficient and automated compliance solutions. Between 2022 and 2025, scientific production in the field of artificial intelligence applied to cryptocurrency compliance experienced a significant increase, indicating an intensified academic and practical interest in this interdisciplinary domain. This growth was largely driven by the adoption of advanced AI methodologies, particularly deep learning models and graph-based techniques such as Graph Neural Networks (GNNs), which demonstrated strong capabilities in detecting complex fraud patterns within blockchain transaction data. Concurrently, there was a notable rise in the integration of AI technologies into compliance frameworks, commonly referred to as RegTech and SupTech. These developments facilitated more automated and scalable approaches to compliance monitoring. This period signaled a transition from theoretical exploration to practical implementation.

Figure 4 and Figure 5 visualize the trend by mapping the countries with the highest publication output, considering both internationally co-authored papers and those produced within a single country. The analysis of geographic contributions to AI research shows distinct patterns of scholarly activity and collaboration networks.

The strong representation of institutions from India and the United States among the leading contributors underscores their strategic commitment to incorporating artificial intelligence into financial systems.

The analysis reveals a rapid growth and geographic concentration of research focused on the topic of this study. The results highlight the prominence of China, the United States, and India, which together contribute a significant share of the field’s intellectual output. While the United States has traditionally led in citation impact, India has now taken the lead in publication volume, indicating a shift in the global research landscape (see Figure 4).

Although interest in the field is increasing, there remains a lack of up-to-date frameworks and models capable of addressing emerging types of fraud across diverse scenarios. This gap presents valuable opportunities for future investigation, especially as fraudulent activities become more complex and widespread. Additionally, the field is marked by limited international collaboration, highlighting the need for more cross-border research efforts.

3.2. Most Relevant Sources

Identifying the most influential publication sources is essential for understanding how knowledge is disseminated within this field. Figure 6 presents the ten journals that have contributed a minimum of two articles to the bibliographic database. Among these, IEEE and Finance Accounting Research Journal emerge as the leading sources, accounting for 18 and 6 publications, respectively.

An important component of bibliometric analysis is Bradford’s Law (Bradford, 1985), which organizes journals according to their publication output by dividing them into three zones, each accounting for approximately one-third of the total number of articles. The first zone, which includes the journals presented in Figure 4, represents the core sources with the highest productivity in the field. According to Bradford’s Law, early research on emerging topics typically concentrates within a small group of highly specialized and influential journals. As the field matures, these journals tend to attract a growing volume of submissions, reinforcing their central role in knowledge dissemination.

3.3. Leading Institutions

Figure 7 shows the leading institutions in AI finance research based on publication volume according to the affiliations of the researchers. By sponsoring leading researchers, these institutions play a pivotal role in advancing artificial intelligence methodologies for compliance and fraud detection in cryptocurrency transactions, as well as contributing broadly to AI-driven financial research. The results indicate that most researchers are affiliated by Indian institutions such as Chandigarh University, Rodrigues Institute of Technology, Uttaranchal University, IIMT University, Christ University, Karunya Institute of Technology and Sciences, Lovely Professional University. India’s dominance in institutional leadership reflects its prioritization of AI research and investment.

The key insight is that national research priorities shape universities’ research agendas and the flow of knowledge within institutional networks (Vuković et al., 2025). As interest in AI continues to grow, an increasing number of institutions are expected to integrate AI into their research and funding strategies. Overall, the bibliometric results underscore the rapid growth and regional concentration of research on AI-driven financial fraud prevention. Despite increasing scholarly attention, the field still lacks cohesive theoretical frameworks and practical integration models, presenting important avenues for future investigation.

3.4. Most Cited Articles

An essential aspect of bibliometric analysis is the examination and identification of the most frequently cited articles, as they significantly shape the field. Figure 8 show the most global documents and

Table 1. Articles with the highest number of citations.

Article	Reference	Citations	Major Contributions and Objectives	Top Criticisms or Issues Reviewed	Main Methodological Aspects
Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances	Hilal et al. (2022)	648	Review prevalent anomaly detection techniques; emphasize semi-/unsupervised learning advances; spotlight emerging methodologies; establish foundational resource	No explicit criticisms or implementation challenges discussed	Systematic literature review; focus on comparative advantages of semi-/unsupervised techniques
Artificial intelligence and machine learning in finance: A bibliometric review	Ahmed et al. (2022)	409	Review AI/ML literature (2011–2021); identify thematic domains; map contributors and trends	Limited journal scope; no methodological critique; focus on mapping over evaluation	Bibliometric approach; use of RStudio, VOSviewer, Excel; trend, citation, co-authorship analyses
Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: a review	Z. Chen et al. (2018)	335	Comprehensive review of ML techniques for AML suspicious transaction detection; synthesis of supervised, unsupervised, and hybrid models; focus on scalable, adaptive, and explainable solutions.	Challenges include imbalanced datasets, evolving money laundering tactics, data scarcity, high false positives, and limited model explainability affecting compliance.	Analysis of supervised, unsupervised, and hybrid ML models; emphasis on feature engineering, handling imbalanced data, evaluation metrics, and ensuring computational efficiency.
Regulating Cryptocurrencies: A Supervised Machine Learning Approach to De-Anonymizing the Bitcoin Blockchain	Sun Yin et al. (2019)	307	Supervised ML model to de-anonymize Bitcoin entities; high predictive accuracy with practical prototype	Limited to 12 categories; reliance on labeled data; anonymity and ethical concerns; generalizability questioned	Gradient Boosting; large dataset of labeled clusters; cross-validated accuracy & F1; prototype implementation
Explainable Artificial Intelligence (XAI) from a user perspective: A synthesis of prior literature and problematizing avenues for future research	Haque et al. (2023)	229	SLR on user-centric XAI; identifies explanation dimensions & user impacts; develops integrated framework & research agenda	Focused on user gaps; lacks empirical testing; calls for studies across varied user segments and contexts	Qualitative synthesis; framework creation for future research
Banking on AI: mandating a proactive approach to AI regulation in the financial sector	Truby et al. (2020)	204	Advocacy for proactive AI regulation; balanced view of AI’s benefits and risks; risk–benefit analysis across actors and jurisdictions	Regulatory lag; risk of reactive over-regulation; challenges of global alignment	Normative policy analysis; risk-benefit framework; comparative jurisdictional evaluation
Generative AI: A systematic review using topic modelling techniques	Gupta et al. (2024)	162	Provides a systematic review of Generative AI (GAI) literature from 1985 to 2023, analyzing a corpus of 1319 Scopus records; identifies the prevailing themes and topic clusters in the GAI research landscape	Pinpoints challenges and research gaps in GAI, particularly in areas such as explainability, robustness, cross-modal and multi-modal generation, interactive co-creation, data privacy, and security	Employs topic modeling techniques likely including BERTopic on a comprehensive bibliographic dataset
Application of Beneish M-Score Models and Data Mining to Detect Financial Fraud	Herawati (2015)	161	Investigates the effectiveness of the Beneish M-Score model and data mining techniques in detecting financial fraud in financial statements.	Highlights limitations of traditional audit procedures in identifying fraud; emphasizes need for analytical tools like M-Score to improve detection accuracy.	Uses Beneish M-Score model (based on financial ratios) and applies data mining approaches to analyze company financial reports for fraud risk classification.
Meta-learning approaches for learning-to-learn in deep learning: A survey	Tian et al. (2022)	155	Reviews meta-learning techniques that enhance deep learning performance with limited data by leveraging prior knowledge. It categorizes key approaches and highlights their real-world applications across various domains.	Highlights that despite growing interest and advances, there remains limited exploration of meta-learning in real-world contexts indicating a gap between theoretical development and practical applications.	Employs a systematic review methodology, detailing classical algorithms and recent enhancements across the three meta-learning categories.
A framework for understanding artificial intelligence research: insights from practice	Bawack et al. (2021)	154	Proposes a classification framework to better align information systems (IS) research with contemporary AI practices. It seeks to bridge the gap between academic studies and industry implementations.	Reveals a misalignment: practitioners view AI in varying ways as a field of study, a concept, an ability, or a system whereas academic IS research predominantly treats AI as an ability, with limited exploration of its adoption, usage, or impact.	Conducted a review of 103 practitioner documents (from 25 Fortune 500 tech companies) to derive the framework, then applied it to classify 110 IS research publications on AI to expose existing knowledge gaps.

presents the ten most-cited articles, with the top three being Hilal et al. (2022), Ahmed et al. (2022), and Z. Chen et al. (2018).

The three papers collectively focus on the use of AI and machine learning in financial fraud detection, emphasizing the importance of data-driven methods and anomaly detection. Despite differing scopes ranging from general fraud to bibliometric trends, to anti-money laundering, they all highlight the potential of intelligent systems and the ongoing need for innovation to combat evolving financial crimes. Also, the three papers differ in their scope, methodology, and time coverage. Hilal et al. (2022) focuses on technical methods for anomaly detection in financial fraud, Ahmed et al. (2022) provides a bibliometric overview of AI/ML research in finance, and Z. Chen et al. (2018) offers a specialized review of machine learning techniques for anti-money laundering. While Hilal et al. (2022) and Z. Chen et al. (2018) emphasize technical strategies, Ahmed et al. (2022) analyzes publication trends. Their temporal coverage also varies, with Ahmed et al. (2022) spanning 2011–2021, Hilal et al. (2022) including updates through 2022, and Z. Chen et al. (2018) focusing on research prior to 2018.

Figure 9 show the “Most Cited References” section refers to the most cited references within the set of documents you are analyzing. It serves to identify the key theoretical or methodological sources that support research in that field.

As shown in

Table 2. Top 10 most influential journals.

Journal	Category/Quartile	Quartile	Article	Citations
Expert Systems with Applications	Operations Research & Management Science, Computer Science, Artificial Intelligence, Engineering, Electrical & Electronic	Q1	Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances	648
Research in International Business and Finance	Business, Finance	Q1	Artificial intelligence and machine learning in finance: A bibliometric review	409
Knowledge and Information Systems	Computer Science, Artificial Intelligence, Computer Science, Information Systems	Q2	Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: a review	335
Journal of Management Information Systems	Information Science & Library Science, Computer Science, Information Systems, Management	Q1	Regulating Cryptocurrencies: A Supervised Machine Learning Approach to De-Anonymizing the Bitcoin Blockchain	307
Technological Forecasting and Social Change	Regional & Urban Planning, Business	Q1	Explainable Artificial Intelligence (XAI) from a user perspective: A synthesis of prior literature and problematizing avenues for future research	229
Law and Financial Markets Review	Law	Q2	Banking on AI: mandating a proactive approach to AI regulation in the financial sector	204
Data and Information Management	Management Information Systems, Library and Information Sciences	Q3	Generative AI: A systematic review using topic modelling techniques	162
Procedia-Social and Behavioral Sciences	Management, Social Science	Title discontinued as of 2019	Application of Beneish M-Score Models and Data Mining to Detect Financial Fraud	161
Neurocomputing	Computer Science, Artificial Intelligence	Q1	Meta-learning approaches for learning-to-learn in deep learning: A survey	155
Journal of Enterprise Information Management	Information Science & Library Science	Q1	A framework for understanding artificial intelligence research: insights from practice	154

, we observe from the field classification that only one of the top 10 ranked journals is finance-based. This suggests that AI research in finance is more commonly published in interdisciplinary journals or those primarily focused on technology and systems. Research in International Business and Finance stands out as the most active finance-specific journal in AI-related compliance into cryptocurrency fraud research.

3.5. Keywords Analysis

The keyword co-occurrence analysis, that is, the frequency with which two or more keywords appear together in a set of academic documents or publications, facilitates the identification of connections between concepts and topics within a research domain by examining the frequency with which specific keywords appear together. A higher rate of keyword co-occurrence indicates a stronger relationship between research themes, helping to uncover key topics and emerging trends in the field.

The research themes are organized into five distinct clusters (Figure 10), centered around the terms “fraud detection,” “machine learning,” “artificial intelligence,” “risk management,” and “regulatory compliance”, which form the foundation of this article’s discussion. Machine learning plays a pivotal role in financial fraud detection by processing large datasets to uncover patterns and anomalies that may signal compliance risks or fraudulent behavior. The largest cluster, depicted in red, revolves around the terms “machine learning”, “cryptocurrency”, and “blockchain”, which form the core discussion of this article.

The terms “artificial intelligence” and “compliance” appear in the second-largest cluster (green) and represents a broad domain that includes machine learning as a fundamental subset (Chhatwani, 2022). AI includes capabilities such as natural language processing, complex problem solving, and decision-making (Huang et al., 2022), whereas machine learning focuses primarily on learning from and adapting to previous experiences. While AI encompasses a range of capabilities such as natural language processing, advanced problem-solving, and autonomous decision-making (Huang et al., 2022), machine learning specifically concentrates on systems that learn from past data and adapt over time (Baabdullah et al., 2024).

Artificial intelligence enhances this process by leveraging real-time data analysis to detect unusual patterns and potentially fraudulent activities (Sengupta & Das, 2023). In parallel, fraud detection, illustrated by a smaller cluster (lilac), risk management (yellow) and regulatory compliance (blue) offer considerable potential themes in combating fraud.

To provide a more holistic perspective of the major keywords in the literature, a treemap of all relevant keywords with their occurrence frequency is shown in Figure 11 using the Biblioshiny package from RStudio. The treemap shows the number of counts and percentages in relation to the whole set of author keywords to identify the most common keywords in AI literature. The treemap analysis confirms the VOSviewer visualization that machine learning, fraud detection, regulatory compliance, artificial intelligence, etc. dominate the literature. According to Figure 11, also a visual tool from biblioshiny (RStudio), shows the most frequent keywords in a set of academic publications. It helps identify the main research topics within a bibliographic dataset. As shown in Figure 12, a word cloud based on research related to the topic of this investigation highlights prominent terms such as: “artificial intelligence,” “fraud detection,” “regulatory compliance,” “risk management,” and “machine learning.” The larger a word appears in the cloud, the more frequently it occurs in the dataset analyzed.

To safeguard financial institutions, their clients, and the overall financial system requires the implementation of robust fraud detection mechanisms. Artificial intelligence plays a key role by analyzing data in real time to uncover unusual patterns and detect potentially fraudulent activities (Sengupta & Das, 2023). Moreover, technologies such as blockchain and machine learning represented in a cluster (red), hold promise in this context.

3.6. Further Analysis

The research focus in AI finance applied to compliance literature has evolved in line with contemporary trends and scientific relevance. By analyzing author keywords, we can trace the thematic evolution of research topics over time. Figure 13 presents the trend analysis of the dataset, illustrating how these topics have developed. The analysis complements the keyword overlay visualization, highlighting shifts in research interest across years. Each bubble represents the year a topic reached peak popularity in the literature, the larger the bubble, the higher the frequency of that keyword during that period. The greatest concentration of keywords (represented by the largest bubbles) occurs in 2025, when artificial intelligence, fraud detection, and compliance emerged as dominant themes in AI-driven finance research. In contrast, earlier trends from 2024 primarily focused on natural language processing, risk assessment, and other AI methodologies commonly applied in financial fraud detection.

As shown in Figure 13, the topic of artificial intelligence in cryptocurrency fraud detection is relatively new, opening up opportunities for new lines of research. For instance, the methodologies used in this area are still underdeveloped, and recent studies have been mostly theoretical in nature, such as literature reviews. However, the practical application and validation of these methodologies remain subjects yet to be fully explored.

Figure 14 presents a thematic map generated from an analysis of author keywords using the Leiden clustering algorithm. The map highlights two primary themes, each characterized by varying density (level of development) and centrality (relevance within the research field). Research that comprehensively integrates “artificial intelligence”, “fraud detection”, and “machine learning” exhibits both high density and high centrality, occupy central positions in research themes, demonstrating above-average levels of both centrality and density. This suggests that these themes are well-established and fundamental to understanding the field and serve as the main drivers of interest in this field. However, they are not considered to have significant potential for future research expansion.

Conversely, topics associated with “risk management”, “decision making”, and “banking” exhibit low density but low centrality, indicating that, while these areas are not well-developed, they may not be highly influential in future research. These areas can be considered as declining in research activity, which is logical since traditional finance topics such as banking, risk management, and decision-making are being replaced by decentralized finance (DeFi), where technology plays a key role in improving risk management and decision-making processes.

Multiple correspondence analysis (MCA) offers a robust approach for identifying potential directions for future research. As shown in Figure 15, the proximity of keywords within this analysis reflects their frequent co-occurrence in scholarly publications. Keywords represented by points in close spatial proximity tend to appear together more often, whereas larger distances indicate less frequent associations. This method provides an effective means of mapping the conceptual and contextual relationships among key terms, utilizing Dim 1 and Dim 2 values to generate a structured representation of the research landscape (Payer et al., 2024).

The resulting keyword map identifies a single, prominent cluster, represented in red. This cluster consists of closely related keywords, where spatial proximity indicates frequent co-occurrence within scholarly articles. The red cluster underscores the core concepts about compliance in cryptocurrency fraud detection, with a particular emphasis on machine learning and artificial intelligence, while “financial fraud detection” and “artificial intelligence” occupy central positions. The predominance of this cluster is noteworthy, as it consolidates topics that are extensively addressed in research on cryptocurrency-related financial fraud and that exhibit a high degree of interconnection. In this context, Krishna and Boddu (2023) highlight that the role of artificial intelligence in preventing financial fraud is increasingly consolidated, driven primarily by the continuous effort to enhance these techniques and the ongoing pursuit of integration with other technologies to strengthen security measures.

As illustrated in Figure 15, there is considerable potential for further research into the synergistic integration of natural language processing, random forests, decision trees, decentralized finance, and learning algorithms in the detection and prevention of financial fraud. The combination of these technologies could enable the development of more sophisticated fraud detection mechanisms, allowing financial institutions to respond more effectively to emerging threats. Additionally, advancements in data mining techniques offer promising opportunities to enhance fraud prediction and prevention capabilities, adapting dynamically to evolving fraudulent schemes. Nevertheless, it is important to note that many of these emerging technologies remain at an early stage of practical implementation, limiting their current applicability.

3.7. AI Methodologies Applied to Compliance

The integration of Artificial Intelligence (AI) methodologies into fraud detection frameworks has fundamentally redefined compliance strategies across a broad range of sectors. Key AI-driven techniques including machine learning (ML), deep learning (DL), natural language processing (NLP), and Generative AI (GenAI), have demonstrated substantial efficacy in enhancing the precision, accuracy and interpretability of fraud detection systems. This unending evolution of fraudulent practices requires detection systems to remain flexible and responsive, spurring preference for advanced computational methods (Kamuangu, 2024).

These advanced technologies enable real-time anomaly detection, adaptive risk scoring, and context-aware analysis of transactional data, thereby aligning detection mechanisms with evolving regulatory mandates. Moreover, the deployment of AI facilitates automated transaction monitoring and accelerates compliance workflows, contributing to a more resilient and secure financial infrastructure (Turksen et al., 2024; von Rueden et al., 2023).

Artificial intelligence encompasses a range of techniques that play a crucial role in strengthening financial compliance, particularly in fraud detection and prevention.

Table 3. AI Methodologies vs. Compliance Applications.

AI Methodology	Compliance Application	Key Benefit
Machine Learning	Transaction monitoring, anomaly detection	Adaptive, reduces false positives
Deep Learning	Complex fraud pattern recognition	High accuracy in large, complex datasets
NLP	Communication analysis, adverse media screening	Detects scams, enhances due diligence
Generative AI	Synthetic data, scenario simulation	Prepares for emerging fraud tactics

presents an overview of key artificial intelligence (AI) methodologies applied in financial compliance and fraud detection in cryptocurrency transactions. It highlights four main approaches: Machine Learning, Deep Learning, Natural Language Processing (NLP), and Generative AI.

A review of the existing research reveals that Machine Learning (ML) approaches can identify fraud patterns over time from large datasets minimizing false alerts (Miser & Sarioguz, 2024). This is particularly useful in crypto environments, where fraud tactics change rapidly. However, the main trade-off is the complexity and the need for large, high-quality datasets to train the models (Islam et al., 2024). If the dataset is biased or incomplete, the model may produce inaccurate predictions or fail to detect new types of fraud. This differs from traditional methods like Rule-Based Systems, which are usually less complex and more interpretable. They rely on predefined rules, for example, flagging transactions that exceed certain thresholds. However, they may struggle to detect new fraud patterns, especially when fraudsters adapt quickly (Pérez-Cano & Jurado, 2025; Carcillo et al., 2019). Among algorithms such as Support Vector Machines, Random Forests, Logistic Regression, Decision Tree, K-Nearest Neighbors (KNN), Naive Bayes and Neural Networks.

ML techniques outperform rule-based systems in dynamic environments where fraudulent activities evolve rapidly, such as in cryptocurrency transactions. In the case of Random Forest, SVM works well in environments where labeled data is abundant, e.g., past fraudulent transactions. According to Alotibi et al. (2022), both Deep Neural Networks (DNNs) and the Random Forest classifier demonstrated high accuracy in fraud detection, with notable improvements in reducing false positives compared to other models. Notably, the Random Forest classifier outperformed DNN, achieving an F1-score of 0.99. However, they require a lot of labeled examples of fraud to avoid overfitting (Pérez-Cano & Jurado, 2025; Carcillo et al., 2019). Unsupervised learning methods such as anomaly detection and clustering are well-suited for identifying previously unseen or novel types of fraud. However, they often come with a higher rate of false positives and typically require careful calibration and fine-tuning to be effective. Pérez-Cano and Jurado (2025) argues that anomaly detection algorithms can effectively identify fraudulent activity in Bitcoin transactions without the need for labeled data. Moreover, incorporating graph-based methods significantly improves detection performance.

Evidence from prior studies shows that deep learning models such as neural networks excel at identifying complex patterns in transaction data, even when fraudulent behavior is hidden in subtle or sophisticated ways, due to its advanced pattern recognition capabilities (Shoetan & Familoni, 2024). They are especially effective in uncovering complex schemes like money laundering or wash trading. However, these models require significant computational resources and large volumes of data to perform well. Additionally, their “black box” nature makes them difficult to interpret, reducing transparency and making it harder to understand how decisions are made (Alotibi et al., 2022). Graph Neural Networks (GNNs), in particular, enhance fraud detection by modeling transactional relationships to reveal hidden connections and suspicious activity. Yet, their scalability remains a concern, as analyzing large-scale transaction networks can be computationally intensive (Yu et al., 2023). Among Advanced techniques such as Generative Adversarial Networks (GANs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Extreme Gradient Boosting (XGBoost), and Graph Neural Networks (GNNs) (Janiesch et al., 2021; Abdar et al., 2021).

Graph-based techniques are particularly effective for uncovering complex schemes such as money laundering, Sybil attacks, and Ponzi schemes, where criminals operate through intricate address networks. Core methods include Graph Convolutional Networks (GCNs) for relational analysis and community detection algorithms to find suspiciously interconnected groups (M. Chen et al., 2020; Ren et al., 2022). Buu and Kim (2023) demonstrated that their DP-GCN model significantly outperforms earlier GCN approaches on real Ethereum data achieving a 32.54-point F1 improvement and 4.28-point AUC increase highlighting the power of advanced graph-based models for detecting malicious behavior in cryptocurrency transactions.

Regarding the application of Natural Language Processing (NLP), current research highlights its application in analyzing unstructured textual data related to cryptocurrency transactions, such as whitepapers, transaction annotations, and user communications (Paripati & Agarwal, 2023). Models such as BERT or GPT are used for text analysis. NLP is particularly effective for compliance-related tasks in the cryptocurrency space, such as analyzing customer communications to identify suspicious language, ensuring regulatory adherence, and reviewing legal documents or contracts. These methods are especially valuable for KYC (Know Your Customer) and AML (Anti-Money Laundering) compliance (Azzutti et al., 2022; Ahern, 2021). However, challenges arise in interpreting unstructured text and filtering out irrelevant information. Additionally, the specialized jargon and code-like language often found in crypto-related documents can lead to misinterpretations by NLP systems. (Hafez et al., 2025). Key techniques include Named Entity Recognition (NER) to detect potentially fraudulent entities (Grishman & Sundheim, 1996; Toprak & Turan, 2023) and Sentiment Analysis to uncover phrases or interactions that may signal fraud or market manipulation (Sureshbhai et al., 2020).

Generative AI enables institutions to simulate fraud scenarios and create synthetic datasets, offering proactive testing against evolving threats (Joshi et al., 2025; Khang et al., 2025; Ssetimba et al., 2024). Techniques like LightGBM, Large Language Models (LLMs), and SHAP enhance these capabilities (Sengupta & Das, 2023; Pranto et al., 2022; Baabdullah et al., 2024). Generative models surpass discriminative and rule-based methods by learning joint feature and graph distributions, capturing multi-scale behaviors, and detecting novel anomalies, especially when labeled data is scarce (Packin, 2024). They also support synthetic data augmentation to create realistic illicit scenarios, broadening training datasets and enhancing the robustness of supervised models against atypical fraud (Saxena & Thakur, 2024; Vavikar & Ostrowski, 2025; Kulkarni et al., 2025). LLMs aid fraud analysis by translating graph data into narratives, generating hypotheses, and expanding labels through OSINT, streamlining investigations. However, key challenges include ensuring synthetic data realism to avoid degrading classifier performance, which requires rigorous validation (Qiao et al., 2024). Explainability issues arise since likelihood scores and LLM narratives may lack legal clarity, risking regulatory rejection. Adversarial fragility is a concern, with attackers potentially evading detection, highlighting the need for adversarial training and human oversight. Notably, the increasing use of generative AI by criminals has accelerated efforts by defenders to deploy similar generative tools for fraud prevention. LLM hallucinations can produce false leads, so outputs must be evidence-based and validated. Computational costs and latency limit real-time use, suggesting offline augmentation and model distillation as solutions. Finally, evaluation demands operational metrics and real-world testing since traditional measures may mislead (Kulkarni et al., 2025; Saxena & Thakur, 2024).

The application of Artificial Intelligence (AI) in compliance frameworks reflects differing philosophies across jurisdictions (Lee, 2020). According to

Table 4. Regional Approaches to AI in Compliance.

AI Methodology	EU (Risk-Based, Structured)	U.S. (Decentralized, Sectoral)	China (Centralized, Control-Oriented)
Machine Learning/Deep Learning	Must adhere to transparency, bias checks, documentation	Used under BSA/KYC regulations; must provide human oversight	Strict deployment monitoring; must ensure data compliance and social alignment
NLP	High transparency, explanation, and governance standards	Used in communications/adverse media; must avoid bias	Closely monitored content; labeling/monitoring enforced
Generative AI	Labeling, data documentation, and risk-tiered oversight	Voluntary watermarking encouraged; oversight varies by state	Heavily regulated; must label content and pass security screenings

, which presents a comparative analysis of AI methodologies for compliance across regions. This study has focused on the three main international centers such as the USA, the European Union and China.

Table 4 examines three primary AI methodologies such as Machine Learning/Deep Learning, Natural Language Processing (NLP), and Generative AI through the compliance perspectives of the European Union (EU), the United States (U.S.), and China. Findings indicate that the EU adopts a risk-based and structured framework, emphasizing transparency, explainability, bias mitigation, and detailed documentation, consistent with its emerging AI Act. The EU Artificial Intelligence Act introduces a risk-based regulatory framework for AI systems, categorizing them into unacceptable, high, limited, and minimal risk levels. High-risk applications such as financial fraud detection are subject to stringent requirements, including third-party conformity assessments, comprehensive technical documentation, post-market monitoring, transparency obligations, and rigorous governance structures (Act, E. A. I., 2024; Vuković et al., 2025).

Conversely, the Markets in Crypto-Assets (MiCA) regulation, adopted by the European Union in 2023 and scheduled for progressive implementation between 2024 and 2025, represents a significant step toward the establishment of a harmonized legal framework for crypto-assets across EU member states (Cardo, 2025; Miravalls, 2021). The regulation aims to govern the issuance, offering, and distribution of crypto assets, while also imposing specific compliance requirements on service providers such as cryptocurrency exchanges, digital wallet operators, and stablecoin issuers (López-Ibor, 2025). By introducing these measures, MiCA seeks to enhance investor protection, ensure market integrity, and contribute to the overall stability of the financial system. Furthermore, the regulation addresses the potential misuse of crypto assets for illicit activities, including money laundering and the financing of terrorism, thereby reinforcing the EU’s commitment to maintaining security and transparency within the rapidly evolving digital finance landscape (Kuznetsova, 2024).

In contrast, the U.S. applies a sectoral and decentralized model, embedding AI oversight within existing compliance domains such as Bank Secrecy Act (BSA) and Know Your Customer (KYC) compliance, while allowing significant state-level variation, particularly in the governance of generative systems (Azzutti et al., 2022; Ahern, 2021; Hafez et al., 2025). China pursues a centralized, control-oriented approach, requiring strict monitoring, mandatory labeling, and ideological alignment to national security imperatives.

Explainable AI (XAI) has been introduced as a mechanism through which authorities can gain greater transparency and access to relevant information. At the same time, Regulatory Technology (RegTech) is increasingly recognized as a promising avenue for improving the efficiency of compliance processes in AI. Complementing these developments, regulatory sandboxes provide structured environments where innovative applications can be tested under supervised conditions, thereby fostering experimentation while mitigating systemic risks (Boukherouaa et al., 2021; Lee, 2020). Within the expanding application of artificial intelligence to compliance in the field of cryptocurrencies, academic research on compliance frameworks is expected to grow steadily. Such contributions will not only provide new insights into the ongoing transformation of the financial system but will also foster critical discussions on how to reconcile technological innovation with ethical considerations.

4. Discussion

The analysis confirms a sharp increase in publications on AI-driven financial applied to compliance in the context of cryptocurrency fraud detection over the past decade. The field grew at an average annual rate of 53%, with a pronounced surge after 2022. This trend underscores growing academic and industry attention to combating fraud with advanced technologies. Moreover, the literature review demonstrated that the implementation of artificial intelligence models in ensuring compliance within cryptocurrency transactions offers substantial strengths while simultaneously posing notable challenges.

According to the present literature review, research has shifted from traditional detection mechanisms which have become increasingly ineffective and obsolete to the widespread adoption of approaches based on artificial intelligence (AI) and machine learning (ML). Algorithms such as Random Forest, Support Vector Machines (SVM), and Decision Trees are commonly employed to detect fraudulent activities by identifying anomalies within transaction data.

In this context, AI offers adaptive mechanisms to detect fraudulent behavior through real-time monitoring and predictive analysis, especially in environments where fraudulent actors continuously refine and evolve their methods (West & Bhattacharya, 2016). However, the constant evolution of fraudulent practices necessitates detection systems that maintain adaptability and responsiveness, encouraging the use of advanced computational methods such as neural networks and ensemble learning approaches (Vuković et al., 2025; Kamuangu, 2024). Nonetheless, AI-driven fraud detection systems encounter significant challenges, including complex classification problems, rapidly evolving fraud strategies, data privacy concerns, and the high costs associated with developing and maintaining sophisticated analytical infrastructures.

This literature review shows the evolution from simplistic models applied to compliance such as Legal-URN and Eunomos (Boella et al., 2014) toward comprehensive, multi-layered approaches. Models outperform traditional methods by effectively identifying complex fraudulent patterns (Mallela et al., 2020; Shoetan & Familoni, 2024; Ssetimba et al., 2024). M. Chen et al. (2020) suggest that Explainable AI (XAI) has gained prominence as a critical factor in ensuring compliance and building user trust by making AI algorithms more interpretable. Boukherouaa et al. (2021) and Lee (2020) indicate that Regulatory Technology (RegTech) is also emerging as a potential solution to streamline AI compliance, while regulatory sandboxes are facilitating innovation and testing in controlled environments.

Among the most effective detection models, supervised learning techniques such as random forests, logistic regression, decision trees, support vector machines, and Naïve Bayes classifiers are widely used (Cutler et al., 2012; Salman et al., 2024; Ravisankar et al., 2011; Hernandez Aros et al., 2024). Ensemble methods, particularly like CNN-LSTM, Ensemble Stacking and gradient boosting machines, have demonstrated outstanding performance. Kamuangu (2024) highlights gradient boosting and deep-learning-based autoencoders as leading models in current research, while Hernandez Aros et al. (2024) report that random forests remain dominant in supervised learning applications, and autoencoders excel in unsupervised approaches. The literature suggests that ensemble methods and deep learning models consistently deliver superior accuracy and performance, making them especially promising for strengthening compliance systems and detecting complex fraud within cryptocurrency ecosystems. However, model explainability also represents a challenge to the adoption of machine learning in credit decision-making. Industry professionals often use tools such as Shapley Additive Explanations (SHAP) to improve model interpretability, which is crucial for ensuring compliance and maintaining stakeholder trust.

Typically, financial technology is linked with financial inclusion. In this context, blockchain implementations and cryptocurrencies, including Bitcoin, have helped mitigate currency-related risks (Paul et al., 2019). Arner et al. (2020) argue that AI and fintech are key drivers of financial inclusion due to two main factors: first, the implementation of electronic Know Your Customer (e-KYC) protocols which simplify account opening processes to allow integration into the financial system; and second, electronic payment systems which facilitate smoother financial transactions. Prioritizing the development of integrative frameworks that harness AI, blockchain, and fintech innovations while addressing ethical governance concerns, transparency requirements, and compliance challenges is necessary in this sphere.

Global perspectives on the regulation of artificial intelligence (AI) exhibit considerable variation. Efforts to address ethical dilemmas, regulatory inconsistencies, and privacy concerns associated with AI applications reveal divergent regional approaches, with limited empirical or theoretical literature available to rigorously assess their relative effectiveness (Vuković et al., 2025; Lee, 2020). The European Banking Institute advocates for a robust and centralized governance framework to mitigate systemic risks and compliance fragmentation particularly within cross-border FinTech operations whereas the United States has adopted a more decentralized, market-driven model, raising concerns regarding standardization and regulatory harmonization (Azzutti et al., 2022; Ahern, 2021).

This divergence has also given rise to the problem of regulatory arbitrage, wherein AI-based platforms for tokenization, crowdfunding, and cryptocurrencies may create unfair advantages for certain users. However, evaluating the effectiveness of AI compliance remains inherently challenging due to the absence of universally accepted performance metrics. Emerging trends indicate a movement toward risk-based frameworks that emphasize flexibility, proportionality, and adaptability in the governance of AI within the financial sector (Vuković et al., 2025).

4.1. Research Gaps in AI for Compliance

The application of Artificial Intelligence (AI) to compliance has grown exponentially. However, despite notable advances in RegTech and LegalTech, substantial theoretical, methodological, and ethical gaps persist, constraining the effective and responsible advancement of AI in this domain.

Existing literature exhibits significant conceptual deficiencies, particularly the absence of a unified theoretical framework. The existing literature on AI and compliance remains fragmented (Straub et al., 2023). Although diverse approaches have emerged across disciplines such as law, ethics, engineering, and risk management, a comprehensive conceptual framework integrating these domains has yet to be developed. Consequently, there is still no clear and unified definition of what constitutes an algorithmic compliance system (Goellner et al., 2024).

Current literature also reveals significant methodological limitations. Algorithmic opacity and limited explainability present major challenges in AI applications for auditing, transaction monitoring, and risk assessment. These models often operate as black boxes, limiting traceability and compromising the transparency required by regulations such as the GDPR and the Financial Services Directive. According to Müller (2025), the need for explainability often conflicts with the drive for optimizing predictive accuracy and operational efficiency. Moreover, the absence of standardized and high-quality data represents a significant barrier to effective AI implementation. As noted by Vidhya et al. (2023), data collection plays a critical role, as modern deep learning algorithms depend more heavily on large-scale data collecting than classification techniques. These AI models often require extensive legal, financial, and behavioral datasets that may contain incomplete, biased, or outdated records. Such limitations in data quality and structure undermine model accuracy, consistency, and compliance (Foidl et al., 2022). Additionally, the absence of universally recognized evaluation criteria and benchmarking frameworks for assessing AI performance in compliance settings presents a significant challenge. This gap impedes the systematic comparison of systems and the verification of performance claims, particularly within high-stakes, real-world applications (Oveisi et al., 2024). Finally, the field is largely characterized by theoretical and qualitative analyses, with a marked deficiency in empirical studies of the effectiveness of AI in compliance contexts. Few studies systematically evaluate whether AI tools improve compliance outcomes such as reducing regulatory violations, improving audit quality, or enhancing efficiency. This gap is particularly problematic given the limited evidence available on the cost–benefit trade-offs of AI-based compliance systems, and it is unclear whether these technologies demonstrably mitigate risk more effectively than conventional approaches. Existing empirical investigations predominantly rely on case studies or vendor-reported results, which often suffer from methodological limitations such as potential biases and lack of neutrality, rigor, or generalizability (Fedyk et al., 2022; Deshpande, 2024).

The literature also indicates a need for more comprehensive approaches to ethical implications. The increasing integration of artificial intelligence (AI) and machine learning (ML) in cryptocurrency trading has given rise to a range of complex ethical challenges, necessitating a comprehensive examination of responsible and adaptive compliance frameworks. Ozioko (2024) highlights the need for adaptive legal frameworks, enhanced transparency, and ethical safeguards to ensure the responsible use of AI in finance. Alibašić (2023) investigates the necessity for novel methods that address the unique characteristics of digital assets alongside existing legalities, such as those about fraud and insider trading (Tsamados et al., 2022). In parallel, the deployment of AI-based compliance systems often reinforces existing resource asymmetries. Small and medium-sized enterprises (SMEs), nonprofit organizations, and public sector institutions frequently lack the financial and technical resources necessary to implement such technologies effectively. This disparity contributes to an uneven regulatory playing field, where well-resourced firms may not only comply more easily but also gain competitive advantages, thereby exacerbating systemic inequalities in access to compliance tools and protections (Ayinaddis, 2025; Kumari, 2024).

4.2. Perspectives and Future Trends

The integration of artificial intelligence and compliance in cryptocurrency fraud detection is progressing rapidly, with emerging trends and challenges set to play a pivotal role in shaping the industry’s trajectory.

Beyond artificial intelligence, emerging technologies such as blockchain, cloud computing, quantum computing, and big data analytics are exerting an increasingly significant influence on the financial sector, often functioning as complementary tools that expand the capabilities of AI systems (Tatineni, 2023). Among these, blockchain is particularly noteworthy for its synergy with AI in enhancing transaction security, supporting decentralized finance (DeFi) applications, and improving transparency in auditing and compliance processes (Vuković et al., 2025).

Quantum computing holds exceptional potential to revolutionize risk analysis, portfolio optimization, and the detection of fraudulent activities through its unparalleled computational capabilities (Deodoro et al., 2021). Quantum computing offers transformative potential for compliance and fraud detection in cryptocurrency ecosystems by enabling advanced data processing and pattern recognition beyond classical computational limits. Through quantum machine learning and graph-based quantum algorithms, it could enhance the detection of complex fraud networks, optimize anti-money laundering (AML) processes, and improve real-time transaction monitoring (Balarabe, 2025). However, its application remains largely theoretical due to technological immaturity, data encoding challenges, and regulatory uncertainty. As research progresses, the integration of quantum computing into AI-driven compliance frameworks may redefine how institutions detect and mitigate financial crime in decentralized systems.

At the same time, augmented intelligence, emphasizing the partnership between AI systems and human oversight, is becoming increasingly influential. It is especially valuable in areas like risk management and ethical decision-making, where accountability must complement AI-driven efficiency (Lui & Lamb, 2018).

However, these advancements also bring about significant challenges. As AI systems become increasingly sophisticated, ensuring transparency, fairness, and compliance with data privacy regulations is more critical than ever. Explainable AI (XAI) frameworks will be fundamental to making AI-driven decisions interpretable and trustworthy, allowing both regulators and users to better manage the growing complexity of AI applications (Alapati & Valleru, 2023). Furthermore, data security remains a major concern, as AI systems in the financial sector process large volumes of sensitive information, making them prime targets for cyberattacks (Vuković et al., 2025).

5. Conclusions

This systematic review of artificial intelligence (AI) applied to compliance highlights the rapid evolution of AI technologies and their transformative impact across multiple sectors of the financial domain, particularly in the detection of financial fraud in cryptocurrency transactions while also acknowledging the substantial challenges that remain. AI has significantly improved the efficiency, accuracy, and interpretability of financial fraud detection systems. The identified trends reveal an increasing reliance on machine learning algorithms, natural language processing, and blockchain technologies to enhance operational efficiency and drive innovation in risk management and fraud detection. These AI-driven models not only outperform traditional approaches in identifying complex fraudulent patterns but have also demonstrated effectiveness in detecting suspicious transaction behaviors and flagging illicit activities such as money laundering and pump-and-dump schemes. Moreover, they play a critical role in supporting compliance efforts, particularly in relation to Anti-Money Laundering (AML) and Know Your Customer (KYC) requirements.

However, this study also emphasizes several key challenges that must be addressed to fully harness AI’s potential in compliance, particularly within the context of cryptocurrency fraud detection. The lack of transparency inherent in many AI models, which often function as black boxes, makes it difficult for regulators and compliance professionals to interpret or justify system outputs especially in legal or enforcement settings. Additionally, the global and decentralized nature of cryptocurrencies introduces significant fragmentation; AI-based compliance systems must operate across a patchwork of jurisdiction-specific regulations, often lacking clear guidance on interoperability, data sharing, and governance. The effectiveness of these systems is further challenged by limitations in data quality and access, as robust fraud detection depends on high-quality, real-time transaction data. Blockchain data alone is often insufficient without supplemental off-chain information, such as IP addresses or exchange records. Moreover, as AI capabilities evolve, so too do the tactics of criminal actors, requiring continuous retraining and innovation to sustain system effectiveness. Finally, despite the growing implementation of AI tools in compliance, there remains a notable absence of peer-reviewed empirical evidence demonstrating their long-term regulatory accuracy or effectiveness in real-world cryptocurrency fraud scenarios.

The findings of this study carry significant implications across multiple domains. For industry professionals, the results suggest the need to adopt AI technologies that efficient, transparent and explainable, key factors in building trust with both users and authorities. From a policy perspective, the review emphasizes the urgent need for comprehensive and adaptive compliance frameworks that safeguard consumer interests balancing with technological innovation. Academically, the research reveals promising directions for further inquiry, including the exploration of ethical dimensions in AI to compliance and the design of hybrid models that integrate diverse AI for enhanced performance. Future research should focus on bridging the gap between theoretical advancements and real-world applications to ensure that AI contributes to sustainable financial systems.

Limitations

Like any study, ours has limitations that must be acknowledged. First, the literature search was restricted to six primary academic databases (SciSpace, Elicit, Google Scholar, Scopus, Web of Science, and ScienceDirect). Although these sources provide extensive coverage, it is possible that relevant studies indexed in alternative repositories, such as conference proceedings or regional databases, were inadvertently excluded. This database selection, combined with specific inclusion criteria, introduces the potential for selection bias. Second, our review focused on publications in English, which may have resulted in the omission of valuable research published in other languages. This language bias could particularly underrepresent findings from non-English-speaking regions, where notable contributions to fraud detection and AI applications may exist within distinct contextual frameworks. Third, the timeframe of 2014–2025 captures the recent decade of research but may exclude important earlier foundational studies that shaped the theoretical and methodological underpinnings of the field. Finally, the bibliometric approach employed inherently prioritizes metadata and citation metrics, which tend to emphasize quantitative visibility over qualitative depth. Consequently, certain nuanced developments, as well as proprietary or unpublished innovations within financial institutions, fall beyond the analytical boundaries of this review.

These limitations suggest caution in generalizing the results. Future research could mitigate these issues by expanding the range of databases, incorporating multi-language publications, and complementing bibliometric insights with qualitative or mixed-method analyses.