The Global Risks Alliance (GRA)
Financial technology changes who provides services, how fast they scale and where risk sits. Artificial intelligence in credit and trading, tokenised assets, stablecoins and digital-only providers all test rules written for an older structure of firms. This page explains how regulatory gaps open around new technology, how authorities apply the principle of same activity, same risk, same regulation, and which records show how far frameworks for crypto-assets, AI and operational resilience are implemented.
GRA · Established · service level R0
Fintech regulation is the application and adaptation of financial rules to new providers and technologies, from AI in credit and trading to tokenised assets, stablecoins and digital-only firms. The principle of same activity, same risk, same regulation guides it, while scale can outrun supervision and opaque models concentrate on few providers.
For financial technology regulation, in GRA's spatial-finance reading, the map opens on night lights and population density with built-up and settlement extent from NASA GIBS, framed on World; every other NASA layer, live feed and forecast waits in the layer list.
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NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
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For financial technology regulation, in GRA's spatial-finance reading, the map opens on night lights and population density with built-up and settlement extent from NASA GIBS, framed on World; every other NASA layer, live feed and forecast waits in the layer list.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.
Layers and dates
The working screen behind this subject: its structure, terms and states. Names are placeholders and values illustrative.
Financial technology regulation · Bank supervision · Cycle 2
| Bank B | High | 3 | 9 | Enhanced |
| Bank D | Medium | 2 | 2 | Standard |
| Insurer Alpha | High | 3 | 5 | Enhanced |
| Fintech F1 | Low | 2 | 2 | Light |
share of firms, thematic review
AI, tokenization and digital services. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
A payment, loan or deposit-like product offered by a technology firm may fall outside licences written for banks. Until the perimeter is redrawn, similar risks carry different safeguards, and activity grows where rules are lightest.
Digital distribution can reach millions of customers before a supervisor reviews the product. Runs on digital banks and stablecoins can move faster than the liquidity assumptions built into existing rules.
Machine learning models are hard to explain, and a few cloud, data and model providers serve much of the sector. A fault or bias in a shared component can appear in many firms at once.
Stablecoin reserves sit in bank deposits and short-term government securities, and banks custody and trade crypto-assets. A loss of confidence in a token can force reserve sales and transmit stress back to regulated markets.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Same activity, same risk, same regulation | The principle that equivalent activities should face equivalent rules whoever carries them out. |
| FSB crypto-asset framework | The FSB's high-level recommendations for regulating crypto-asset activities and global stablecoin arrangements. |
| Innovation hub | A regulator's contact point that gives firms guidance on how rules apply to new products. |
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Implementation of the FSB crypto-asset framework | implementation stage per jurisdiction | per peer review | FSB thematic peer review | OFFICIAL |
| Authorised crypto-asset service providers | count on the register by jurisdiction | monthly | MiCA registers | OFFICIAL |
| Travel rule implementation for virtual asset providers | implementation status per jurisdiction | annual | FATF targeted update | OFFICIAL |
| Stablecoin reserve composition | percent of reserves by asset type | monthly | Issuer reserve attestations | DECLARED |
| Regulatory sandboxes and innovation hubs | presence and type by authority | annual | Authority publications | REPORTED |
| Designated critical ICT third-party providers | count of providers under direct oversight | annual | European Supervisory Authorities designation | OFFICIAL |
| Major ICT-related incidents reported | count by category | annual | DORA incident reporting aggregates | OFFICIAL |
| Supervisory statements on AI use in finance | count by authority and topic | annual | Authority publications | REPORTED |
The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.
Fig. 02Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| FSB thematic peer review of the crypto-asset framework | Jurisdictional implementation of crypto-asset and global stablecoin recommendations | Public, terms per FSB |
| ESMA interim MiCA register | Authorised crypto-asset service providers, white papers and non-compliant entities in the EU | Public, reuse with attribution |
| FATF targeted updates on virtual assets | Implementation of FATF standards for virtual assets and their providers | Public, terms per FATF |
| IOSCO policy recommendations for crypto and digital asset markets | Recommendations and implementation monitoring for crypto-asset markets | Public, terms per IOSCO |
| BIS Innovation Hub project reports | Experiments in tokenisation, payments and supervisory technology by central banks | Public, BIS terms of use with attribution |
| European Supervisory Authorities list of critical ICT third-party providers | Providers designated for direct oversight under DORA | Public, reuse with attribution |
| National regulators' sandbox and innovation hub records | Cohorts, eligibility and lessons published by authorities | Public records, terms per regulator |
Financial Regulation Nexus prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Legislatures | Whether to create new licences, extend existing ones or prohibit activities | Perimeter maps, comparative statutes and published peer reviews |
| Financial regulators and central banks | Which firms to authorise, test in sandboxes or bring under direct oversight | Registers, sandbox lessons and incident aggregates |
| Financial Stability Board, BCBS, IOSCO and FATF | Whether standards for crypto-assets, AI and third parties need revision | Implementation reviews and documented gaps |
| Boards of financial firms | How to govern AI models and concentrated technology providers | Supervisory expectations on model risk, outsourcing and operational resilience |
Fintech regulation researchers ask how to govern innovation that crosses existing categories.
A common regulatory principle holds that the same activity creating the same risk should face the same rules. Fintech tests this principle because new business models unbundle and rebundle activities in ways that do not fit existing categories. Some innovations sit partly outside the regulatory perimeter; others raise new risks, such as model risk in AI, operational dependence on technology providers, or legal uncertainty about digital assets.
Regulators have responded with innovation offices, sandboxes, new licensing regimes and guidance on existing rules. Research examines whether these approaches improve outcomes, how they affect competition and whether they create uneven treatment between incumbents and new entrants.
Mapping activities to regulatory categories using policy and institutional science. Legal classifications differ across jurisdictions.
Understanding how systems work, with data engineering expertise. Proprietary systems limit transparency.
Comparing approaches to AI, digital assets and payments. Many regimes are new.
Examining competition and concentration with game theory. Markets change quickly.
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.