GRA · Financial Regulation Nexus
Spatial financeWhere capital meets hazard, exposure and transition
Financial technology regulation
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.
Financial technology regulation in seven parts.
- 01The questionFinancial technology regulation: how the risk develops
- 02IndicatorsWhat is measured, and how well
- 03SourcesWhere the evidence comes from
- 04DecisionsWho decides, and what the evidence serves
- 05Core questionsWhat experts ask
- 06Mechanisms and driversSame activity, same risk, same rules
- 07Methods and dataHow the evidence is built, and where it stops
Financial technology regulation: how the risk develops.
AI, tokenisation 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.
Same activity, different rules
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.
Scale outruns supervision
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.
Opaque models, concentrated providers
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.
Tokens tied to traditional finance
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.
Supervisory review console.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Supervisory review console
Financial technology regulation · Bank supervision · Cycle 2
Firms by risk rating
| Bank B | High | 3 | 9 | Enhanced |
| Bank D | Medium | 2 | 2 | Standard |
| Insurer Alpha | High | 3 | 5 | Enhanced |
| Fintech F1 | Low | 2 | 2 | Light |
Findings
Climate expectations met
share of firms, thematic review
What is measured, and how well.
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 | Published statistics |
| Authorised crypto-asset service providers | count on the register by jurisdiction | monthly | MiCA registers | Published statistics |
| Travel rule implementation for virtual asset providers | implementation status per jurisdiction | annual | FATF targeted update | Published statistics |
| 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 | Published statistics |
| Major ICT-related incidents reported | count by category | annual | DORA incident reporting aggregates | Published statistics |
| Supervisory statements on AI use in finance | count by authority and topic | annual | Authority publications | REPORTED |
Policy paths and what they do to the risk.
How three families of instruments change a risk outcome over time with adoption lags, coverage and compliance, the policy scenarios behind transition and physical risk assessments. Scenario: named assumptions about the future, not a forecast.
Controls
- Baseline trend of the outcome
- 2.0% a year
- Coverage of the instrument
- 70%
- Compliance
- 75%
- Effect size, against the teaching value
- 100%
- Years ahead
- 20 years
Readouts
- Largest reduction at the horizon
- Regulation
- Its reduction against the baseline
- 21%
Numbers, methods and sources
| Year | Baseline | Regulation | Economic incentive | Information and capacity |
|---|---|---|---|---|
| 0 | 100.0 | 99.5 | 98.8 | 99.1 |
| 1 | 102.0 | 101.2 | 100.1 | 99.8 |
| 2 | 104.0 | 102.5 | 101.1 | 99.9 |
| 3 | 106.1 | 103.5 | 101.6 | 100.0 |
| 4 | 108.2 | 103.7 | 101.8 | 100.7 |
| 5 | 110.4 | 103.2 | 101.7 | 102.1 |
| 6 | 112.6 | 102.0 | 101.6 | 103.9 |
| 7 | 114.9 | 100.4 | 101.6 | 105.9 |
| 8 | 117.2 | 99.2 | 102.1 | 108.0 |
| 9 | 119.5 | 98.6 | 102.9 | 110.1 |
| 10 | 121.9 | 98.9 | 104.2 | 112.3 |
| 11 | 124.3 | 99.7 | 105.7 | 114.5 |
| 12 | 126.8 | 101.1 | 107.4 | 116.8 |
| 13 | 129.4 | 102.7 | 109.4 | 119.2 |
| 14 | 131.9 | 104.5 | 111.4 | 121.6 |
| 15 | 134.6 | 106.5 | 113.5 | 124.0 |
| 16 | 137.3 | 108.5 | 115.7 | 126.5 |
| 17 | 140.0 | 110.7 | 118.0 | 129.0 |
| 18 | 142.8 | 112.9 | 120.4 | 131.6 |
| 19 | 145.7 | 115.1 | 122.8 | 134.2 |
| 20 | 148.6 | 117.4 | 125.2 | 136.9 |
Methods
- Tools of government Hood (1983), The Tools of Government, Macmillan
- Carrots, sticks and sermons Bemelmans-Videc, Rist and Vedung (1998), Transaction
- Diffusion of innovations Rogers (2003), Diffusion of Innovations, Free Press
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page's map can draw:
- Borders Natural Earth
- Cities Natural Earth
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Financial Regulation Nexus in Foundry
- Tested at Financial Regulation Nexus in Universe
- Codified in Finance, Insurance and the Economy in Standards
- Published in Financial Regulation Nexus in Reports
- Taught in Faculty of Finance and Economics in Academy
Where the evidence comes from.
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 |
What financial technology regulation means for exposure, loss and capital.
The exposure, loss, scenario and capital views a risk officer, supervisor or ministry of finance would ask for, with the frameworks they report against. Structures follow supervisory and actuarial practice. Values are illustrative: never prices, ratings or advice.
Disclosure crosswalk
Where evidence on financial technology regulation fits the disclosure and prudential frameworks institutions report against.
| Pillar | TCFD | ISSB IFRS S2 | Prudential (BCBS, IAIS) | Evidence this page can hold |
|---|---|---|---|---|
| Governance | Board oversight of climate-related risks | Governance requirements | Governance and risk appetite principles | Who owns the risk and how it is escalated |
| Strategy | Risks, opportunities and resilience | Scenario analysis and resilience | Strategy and business model principles | Scenario design and transmission paths |
| Risk management | Identify, assess, manage | Risk management processes | Internal control and ICAAP or ORSA | Exposure maps and loss views |
| Metrics and targets | Metrics used and targets set | Cross-industry and industry metrics | Data, metrics and reporting principles | Indicator definitions and units |
Who decides, and what the evidence serves.
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 |
Outputs this topic produces
- Perimeter maps showing which activities fall under which licence in each jurisdiction
- Comparative briefs on crypto-asset, stablecoin and AI rules
- Learning material on supervising third-party and model concentration
- Implementation tracking guides built on authority registers
What experts ask.
Fintech regulation researchers ask how to govern innovation that crosses existing categories.
- Does an innovation fit existing regulatory categories, or does it require new rules?
- How should AI used in credit, insurance and advice be governed for fairness, explainability and accountability?
- How do tokenised assets and digital asset services fit into securities, payments and prudential frameworks?
- How do innovation offices and sandboxes affect regulatory learning and market entry?
- How do technology firms' entry into finance affect competition, concentration and operational risk?
Same activity, same risk, same rules.
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.
How the evidence is built, and where it stops.
- 01
Perimeter analysis
Mapping activities to regulatory categories using policy and institutional science. Legal classifications differ across jurisdictions.
- 02
Technology assessment
Understanding how systems work, with data engineering expertise. Proprietary systems limit transparency.
- 03
Comparative regulation
Comparing approaches to AI, digital assets and payments. Many regimes are new.
- 04
Market structure analysis
Examining competition and concentration with game theory. Markets change quickly.
How it is studied, and what the methods cannot say.
- 01Policy, institutional and decision scienceRegulatory impact assessment · Comparing supervisory architecturesApplied here
- 02Financial economics and macro-financeCosts and benefits of capital requirements · Tracing leakage of macroprudential measuresApplied here
- 03Data engineering, interoperability and stewardshipMapping templates to a common model · Entity resolution with identifiersApplied here
- 04Game theory and mechanism designHome-host cooperation in resolution · Regulatory competition between jurisdictionsApplied here
Questions and answers.
What does same activity, same risk, same regulation mean?
Is a stablecoin a deposit?
What is a regulatory sandbox?
Does the platform assess fintech products?
Are digital assets regulated?
How is AI in finance governed?
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