How AI is transforming regulatory oversight in cryptocurrency market

Despite differing regulatory philosophies, all regions recognize AI’s value in transactional transparency, anomaly detection, and early fraud prevention. The study identifies a clear shift from manual compliance review to automated monitoring powered by AI models capable of parsing massive, pseudonymous datasets in real time.

A multinational team of researchers has mapped the accelerating role of artificial intelligence in detecting fraud within cryptocurrency systems. Their study, titled “A Systematic Review of Artificial Intelligence Applied to Compliance: Fraud Detection in Cryptocurrency Transactions,” was published in the Journal of Risk and Financial Management.

The research presents the first comprehensive bibliometric and systematic analysis of how machine learning, deep learning, and data-driven modeling are revolutionizing compliance, anti-fraud mechanisms, and regulatory oversight in crypto ecosystems. By synthesizing insights from 353 peer-reviewed publications between 2014 and 2025, the authors show that AI now sits at the center of the global strategy to monitor illicit blockchain activity and strengthen financial integrity.

AI reshaping compliance in the digital currency era

The fusion of AI and blockchain has moved from theoretical promise to operational necessity. The authors report a 53% annual growth rate in academic output since 2022, signaling surging global attention to the technology’s compliance potential.

Countries such as India, the United States, and China dominate research contributions, reflecting diverse regulatory approaches to balancing innovation with risk. The United States favors a sectoral, decentralized framework under existing anti-money laundering (AML) and Bank Secrecy Act provisions, while the European Union has moved toward harmonized, risk-based governance through the AI Act and MiCA regulations. In contrast, China has adopted a centralized model, focusing on strict data labeling and continuous monitoring of AI applications.

Despite differing regulatory philosophies, all regions recognize AI’s value in transactional transparency, anomaly detection, and early fraud prevention. The study identifies a clear shift from manual compliance review to automated monitoring powered by AI models capable of parsing massive, pseudonymous datasets in real time.

Machine learning models such as Random Forest, Support Vector Machines, and XGBoost, along with deep learning architectures like Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, are cited as leading tools for analyzing blockchain patterns and tracing illicit transfers across complex transaction graphs.

Machine intelligence versus crypto crime

The paper highlights how AI outperforms traditional rule-based systems in detecting money laundering, terrorist financing, and pump-and-dump schemes within decentralized finance (DeFi) markets. By integrating graph-based algorithms and predictive learning, AI systems can map hidden relationships between wallets, identify address clusters, and flag suspicious sequences in near real time.

In particular, the authors spotlight Graph Neural Networks (GNNs) for their ability to learn the topology of blockchain transaction networks. These models allow investigators to visualize criminal patterns that evade linear analytical tools. Likewise, Natural Language Processing (NLP) techniques strengthen Know Your Customer (KYC) and anti-money laundering (AML) frameworks through automated entity recognition, risk classification, and adverse media screening.

The review also highlights the emergence of Generative AI for synthetic data production and simulation of fraudulent transaction scenarios, offering a means to train models without exposing sensitive financial data. However, the authors caution that generative techniques raise new concerns around data realism, governance, and auditability, which regulators must address to maintain public trust.

While the technological advancements are evident, the paper underscores that successful fraud detection relies equally on data quality, transparency, and algorithmic explainability. Many models struggle to justify their decisions to compliance officers or auditors, leading to what the researchers describe as the “black-box problem”, a key barrier to legal adoption in high-stakes financial contexts.

Bridging innovation and regulation

The authors argue that the next frontier in AI-driven compliance lies in harmonizing technological innovation with regulatory accountability. Their bibliometric mapping reveals five dominant research clusters:

• Fraud Detection Algorithms – Development of scalable, interpretable detection models.
• Blockchain Analytics – Tracing illicit asset flows through public ledgers.
• Risk Management Frameworks – Modeling systemic risk and compliance exposure.
• Governance and Regulation – Crafting cross-border standards for AI accountability.
• Ethical and Societal Impacts – Addressing fairness, privacy, and oversight challenges.

Across these clusters, the review finds that ensemble learning, combining multiple algorithms, consistently outperforms single-model systems. Such hybrid methods can detect fraud with higher accuracy while minimizing false positives, a persistent challenge in transaction screening.

However, the researchers warn that uneven access to AI resources and talent could widen the “compliance divide” between large financial institutions and smaller firms. They call for open data initiatives, shared benchmarks, and cross-border collaboration to prevent regulatory asymmetry in global cryptocurrency oversight.

The study also points out the growing need for explainable AI (XAI), transparent systems capable of justifying their outputs to regulators and auditors. Without interpretability, compliance models risk losing legal validity under frameworks like the EU’s AI Act, which requires traceability and human accountability in algorithmic decision-making.