Building SupTech capabilities for on-chain analysis will be central to how supervisors close the information gap with fast-moving crypto and DeFi markets. By treating public blockchains as a new supervisory data layer rather than an exotic niche, authorities can move toward real-time, intelligence-driven oversight of digital asset risks.
The supervisory challenge in decentralised markets
Most supervisory playbooks assume that financial activity is intermediated by licensed entities that generate prudential and conduct data through regulatory returns, transaction reports, and inspections. In blockchain-based markets, value often moves peer-to-peer through smart contracts and self-hosted wallets, with no central gatekeeper obligated to submit granular reports.
Decentralised exchanges, automated market makers, and lending protocols fragment liquidity and risk across multiple chains and tokens. Cross-border, pseudonymous transactions complicate customer due diligence, sanctions screening, and jurisdictional oversight, creating blind spots in AML/CFT and market integrity frameworks. The net result is growing information asymmetry: sophisticated market participants and analytics vendors can see risk signals on-chain long before supervisors do.
What is on-chain analysis?
On-chain analysis refers to extracting, structuring, and interpreting data directly from distributed ledgers to understand how digital assets move through blockchain ecosystems. It typically involves parsing raw block and transaction data, mapping transaction flows, clustering related wallets, and detecting abnormal patterns or high-risk counterparties.
Basic blockchain explorers provide human-readable access to individual transactions, blocks, and addresses but offer limited analytics or risk scoring. Commercial blockchain analytics platforms enrich raw on-chain data with entity attribution, typology libraries, and AML/CFT risk indicators, often via APIs. A supervisory analytics infrastructure goes further by integrating multi-chain data into a data lake, layering AI-based anomaly detection, and connecting outputs to existing SupTech dashboards and early warning systems.
Why SupTech matters for blockchain supervision
SupTech has already been used to move from periodic, template-based reporting to more timely, granular supervision in banking and securities markets. Extending this to blockchains allows authorities to monitor digital asset networks continuously rather than relying solely on firm-submitted reports or ex-post enforcement actions.
With appropriate tools, supervisors can identify concentrated positions, unusual trading patterns, or liquidity strains in near real time. On-chain SupTech supports core risk-based supervision objectives: prioritising high-risk entities and activities, sharpening thematic reviews (for example, stablecoin exposures), and feeding evidence into supervisory judgments on governance, risk management, and capital planning for crypto-exposed institutions.
Core SupTech capabilities for on-chain supervision
Data ingestion. Direct node connectivity to major public blockchains ensures independent access to full transaction histories and mempool data, reducing reliance on regulated firms’ reporting. API integration with third-party blockchain analytics providers allows supervisors to leverage entity labels, risk scores, and typology libraries while retaining control over supervisory models. Cross-chain monitoring is essential as activity migrates between mainnets, sidechains, and rollups, often to exploit regulatory arbitrage or lower scrutiny.
Entity resolution. Wallet clustering techniques help group addresses likely controlled by the same actor — for example, centralised exchanges, mixers, or OTC desks. Address attribution maps known services (exchanges, DeFi protocols, sanctioned entities, darknet markets) to clusters, improving interpretability of flows. Linking wallets to regulated entities and crypto-asset service providers (CASPs) allows supervisors to overlay on-chain behaviour with licensing, fit-and-proper, and risk assessment data.
Transaction intelligence. Flow analysis reconstructs multi-hop paths of funds, helping trace the origin and destination of suspicious activity. Token movement tracking highlights large inflows and outflows to exchanges, stablecoin issuers, and key liquidity pools that may signal stress. Smart contract interaction monitoring identifies which protocols and functions — for example, leverage, staking, cross-chain bridges — regulated entities or high-risk wallets are using.
Risk detection. Market abuse indicators such as wash trading, spoofing, or pump-and-dump behaviour can be inferred from repeated self-matching or cyclical flows around decentralised exchanges. Sanctions evasion patterns include routing via mixers, privacy coins, and high-risk services labelled by analytics providers or law enforcement. Liquidity pool instability and sudden stablecoin redemptions are visible as sharp changes in pool composition, on-chain reserves, or mint/burn activity.
Supervisory use cases
AML / financial crime. Supervisors can use on-chain analytics to trace flows linked to ransomware, fraud, or sanctioned entities, and assess whether banks and CASPs have effective monitoring and reporting controls.
Market conduct. Analytics can flag suspicious trading around listing events or governance votes, supporting investigations into manipulation and insider abuse in token markets.
Stablecoin monitoring. Under regimes such as MiCA and emerging US stablecoin rules, on-chain data can complement disclosures and attestations by tracking issuance, redemptions, and reserve wallet flows.
DeFi risk monitoring. Authorities can observe concentration of liquidity, leverage, and collateral across protocols, informing systemic risk assessments and perimeter discussions.
Cross-border flows. Combining on-chain data with off-chain information helps map capital movements routed through digital assets, including potential circumvention of capital flow measures or sanctions.
Architecture of a SupTech blockchain analytics platform
A conceptual supervisory platform for on-chain analysis can be built around several layers.
A blockchain data node layer consists of full nodes and data providers for priority chains, potentially shared regionally or via international hubs. A data lake and analytics engine stores normalised transaction, address, and contract data, integrated with traditional regulatory data and news or social media feeds where relevant. An entity resolution engine provides clustering, attribution, and identity-linkage services, including integration with KYC/CDD data from supervised institutions. An anomaly detection and AI layer uses machine learning and network analytics to detect unusual behaviour, risk hotspots, or emerging typologies, building on early SupTech experiments such as BIS Project Ellipse. Supervisory dashboards provide role-based interfaces that surface risk scores, alerts, network maps, and institution-level exposures for frontline supervisors and policy teams.
AI and machine learning can enhance pattern recognition by identifying non-obvious correlations between on-chain behaviour, macro-financial conditions, and institution-specific vulnerabilities. Over time, models can be trained on enforcement outcomes, suspicious transaction reports (STRs), and historical stress events to refine alert thresholds and reduce false positives.
Integration with existing supervisory frameworks
On-chain analytics should be embedded into, not separate from, mainstream supervisory frameworks. For risk-based supervision, blockchain intelligence becomes another input to risk scoring models, thematic reviews, and the setting of supervisory plans and capital add-ons for crypto-exposed entities.
In AML/CFT, supervisors can use on-chain insights to challenge institutions’ transaction monitoring, sanctions screening, and customer risk assessment frameworks — as reflected in expectations from authorities such as NYDFS and under MiCA. Market surveillance teams can integrate digital asset indicators into broader market abuse detection, while prudential supervisors monitor how crypto exposures interact with liquidity risk, operational resilience, and concentration risk. Integrating on-chain data with regulatory reporting platforms and SupTech tools such as Ellipse supports more holistic, cross-market views of risk.
Challenges in building SupTech capabilities
Pseudonymity and privacy remain core constraints: while wallets are visible, linking them reliably to real-world identities often requires cooperation from intermediaries and raises data protection questions. Data quality issues arise from contract upgrades, token migrations, and idiosyncratic protocol designs that complicate normalisation and comparability.
Regulators also face internal capability gaps, needing data scientists, blockchain engineers, and supervisors who understand DeFi mechanics and tokenomics. Infrastructure for running nodes, storing high-frequency data, and operating analytics at scale can be costly, particularly for smaller authorities. Over-reliance on third-party analytics vendors creates vendor concentration, opacity around methodologies, and potential conflicts if those vendors also serve supervised firms.
International regulatory approaches
Authorities are already experimenting with SupTech and blockchain analytics. The BIS Innovation Hub has launched projects and prototypes — including Project Ellipse, developed jointly with the Monetary Authority of Singapore (MAS), Financial Network Analytics (FNA), and Accenture — that integrate granular supervisory data and advanced analytics to support more forward-looking supervision. Ellipse was designed to combine structured and unstructured regulatory data in a single platform to help authorities identify potential risks to individual banks and the broader banking system; it is one model for a data-driven supervisory analytics platform, though its design was not specific to crypto or on-chain data.
MAS has been an early mover in DLT through Project Ubin, a multi-phase collaborative project exploring blockchain and DLT for payments and securities settlement that ran from 2016 to 2020, and through its collaboration with the BIS Innovation Hub Singapore Centre on supervisory analytics. In the EU, MiCA and ESMA work emphasise transaction monitoring and the use of both on- and off-chain data for oversight of crypto-asset service providers. In the US, NYDFS Superintendent Adrienne A. Harris issued guidance on September 17, 2025 extending blockchain analytics expectations to all New York banking organisations engaged in or contemplating virtual currency activity, building on earlier 2022 guidance to licensed virtual currency entities. The UK FCA has invested in data and SupTech capabilities and, following Parliament making the Financial Services and Markets Act 2000 (Cryptoassets) Regulations 2026 on February 4, 2026, is building a new full-authorisation regime for cryptoasset firms expected to come into force on October 25, 2027.
Snapshot of emerging approaches
| Jurisdiction/body | Focus of initiatives | SupTech/on-chain angle |
| BIS Innovation Hub | SupTech/RegTech prototypes (Ellipse, real-time data) | Open-source analytics, AI for supervision, cross-border data models |
| MAS (Singapore) | DLT experiments (Project Ubin, 2016–2020), supervisory analytics platforms | Integrating granular data, adapting prototypes for domestic supervision |
| EU (MiCA/ESMA) | CASP framework, transaction monitoring expectations | Emphasis on on-chain plus off-chain surveillance and reporting |
| US / NY DFS | Guidance on blockchain analytics for banks and virtual currency entities (2022 and 2025) | Use of analytics tools for AML, sanctions, and risk management |
| UK FCA | Full cryptoasset authorisation regime (from October 2027), data-first supervision | SupTech investment to detect market abuse and monitor firms |
The future: real-time supervisory intelligence
Looking ahead, on-chain SupTech is likely to evolve toward continuous, real-time monitoring of major public blockchains and tokenised markets. AI-driven risk engines could generate dynamic alerts on emerging threats — such as coordinated exploit attempts, cross-chain bridge attacks, or destabilising runs on stablecoins.
As tokenisation expands and CBDC pilots mature, supervisors may seek integrated dashboards that cover traditional banks, DeFi protocols, stablecoins, and CBDC infrastructures in a single systemic-risk view. Cross-border data-sharing arrangements and common analytics standards will be critical to avoid fragmentation and to manage risks that are inherently transnational. Authorities that invest early in robust on-chain analytics capabilities will gain a structural informational advantage over both supervised entities and bad actors, strengthening their ability to safeguard financial stability in an increasingly decentralised landscape.
References
Bank for International Settlements. (2021, January 22). BIS Innovation Hub sets out work programme and launches Innovation Network. https://www.bis.org/press/p210122.htm (Note: The original document cited January 28, 2021 — the correct press release date is January 22, 2021.)
Bank for International Settlements. (2022, March 29). Project Ellipse: An integrated regulatory data and analytics platform (BIS Other Papers No. 48). https://www.bis.org/publ/othp48.htm
Bank for International Settlements. (2022, March 31). Ellipse: Regulatory reporting and data analytics platform. https://www.bis.org/about/bisih/topics/suptech_regtech/ellipse.htm
Bank for International Settlements Innovation Hub. BIS Innovation Hub suptech and regtech. https://www.bis.org/about/bisih/topics/suptech_regtech.htm
DFS New York. (2025, September 17). DFS Superintendent Adrienne A. Harris extends blockchain analytics guidance to New York State banking organizations. https://www.dfs.ny.gov/reports_and_publications/press_releases/pr20250917
European Securities and Markets Authority. Markets in Crypto-Assets Regulation (MiCA). https://www.esma.europa.eu/esmas-activities/digital-finance-and-innovation/markets-crypto-assets-regulation-mica
Monetary Authority of Singapore. Project Ubin: Central bank digital money using distributed ledger technology. https://www.mas.gov.sg/schemes-and-initiatives/project-ubin (Project ran 2016–2020 across five phases; the original document cited December 31, 2017, which corresponds to an early phase publication rather than the project as a whole.)
Sullivan & Cromwell LLP. (2025, September 21). NYDFS issues guidance on use of blockchain analytics. https://www.sullcrom.com/insights/memo/2025/September/NYDFS-Issues-Guidance-Use-Blockchain-Analytics
Troutman Pepper. (2025, September 22). New York issues guidance on blockchain analytics for banking institutions. https://www.troutmanfinancialservices.com/2025/09/new-york-issues-guidance-on-blockchain-analytics-for-banking-institutions/
UK Financial Conduct Authority. (2026, February 4). Cryptoasset firms: Authorisation, supervision and enforcement. https://www.fca.org.uk/firms/new-regime-cryptoasset-regulation/authorisation-supervision-enforcement (The new cryptoasset regime is expected to come into force on 25 October 2027.)
XBRL International. (2022, April 7). Project Ellipse completes prototype supervisory analytics platform. https://www.xbrl.org/news/project-ellipse-completes-prototype-supervisory-analytics-platform/
XBRL International. (2025, June 9). FNA is working with BIS, MAS and Accenture on “Suptech” Project Ellipse. https://www.xbrl.org/news/fna-is-working-with-bis-mas-and-acceture-on-suptech-project-ellipse/
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