Sector dependency maps
Registers of third-party arrangements are combined to show which providers support many firms' important services at the same time.
GRA · Financial Cyber and Operational Resilience
Spatial financeWhere capital meets hazard, exposure and transition
Network science and contagion for financial cyber and operational resilience
Network science maps firms, providers and services as connected nodes. On this platform it shows how a failure at a shared provider or infrastructure spreads across firms, and which dependencies concentrate risk in the financial sector.
01Register
Network science and contagion for financial cyber and operational resilience on the record.
Method recordSTEM method
- MethodNetwork science and contagion
- Applied inFinancial Cyber and Operational Resilience
- Data3 inputs
- Checks3 validation steps
- Canonical pageSTEM Commons
02Application
What network science and contagion does here.
Network science and contagion study how the structure of connections among entities shapes the spread of failures, information and disease. It provides tools to analyse interdependent infrastructure, supply chains and financial systems. In cyber and digital resilience, financial stability and critical infrastructure, network science identifies critical nodes and pathways through which shocks propagate across sectors. It informs targeted protection and diversification to limit cascades. The canonical description is on the STEM Commons.
Outage propagation
Simulations trace how an outage at a provider or infrastructure would affect services across dependent firms and their customers.
Software component networks
Dependency graphs of software components show where a single library flaw would reach many critical applications.
03Spatial finance
Settlement dependencies, and what fails with a node.
How a failure at one financial centre can spread through settlement links. Design links, not real dependencies.
04Data
What the method consumes.
Datasets and records used by network science and contagion on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Third-party registers | Provider links | REPORTED |
| Software bills of materials | Component links | DECLARED |
| Incident reports | Observed propagation | REPORTED |
05Monitor
Network analysis.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Network analysis
Operational resilience · Network science and contagion · Run R-3490
Single point of failureGRA · Financial Cyber and Operational Resilience
Dependency graph
nodes: assets · edges: dependencies
Most central nodes
| N3 | 0.24 | 7 | Critical |
| N4 | 0.33 | 11 | Normal |
| N5 | 0.31 | 6 | Normal |
| N6 | 0.21 | 4 | Normal |
Cascade size under removal
06Validation
How results are checked.
Models are checked by reproducing observed cascades and outbreaks. Robustness of results to missing or uncertain links is tested.
| Check | How it is done |
|---|---|
| Coverage stated | The share of the sector represented in the data is stated |
| Validation against events | Simulations are compared with observed outages |
| Stability | Key nodes are checked across periods |
Network data are often incomplete or confidential. Static network models may miss adaptive behaviour during cascades.
07Stress test
How a shock to assets becomes a system loss.
The equity the system loses after interbank contagion, against the initial hit, as leverage, interconnection and recovery rates change, the core of a macroprudential stress test. Teaching model with stated illustrative values, not a prediction. The map on this page shows The Low Countries; the model itself is not tied to a place.
Controls
- Fall in external assets
- 2.0%
- Leverage (assets over equity)
- 10 times
- Interbank share of assets
- 10%
- Recovery on interbank claims
- 40%
- Banks in the system
- 20
- Core banks
- 20% of banks
Readouts
- Equity lost to the initial shock
- 18.0%
- Equity lost after contagion
- 45.0%
- Added by contagion (DebtRank)
- 27.0 points
- Banks in default
- 0
Numbers, methods and sources
| Round | System equity lost (%) | Banks in default |
|---|---|---|
| 0 | 18.00 | 0 |
| 1 | 28.80 | 0 |
| 2 | 35.28 | 0 |
| 3 | 39.17 | 0 |
| 4 | 41.50 | 0 |
| 5 | 42.90 | 0 |
| 6 | 43.74 | 0 |
| 7 | 44.24 | 0 |
| 8 | 44.55 | 0 |
| 9 | 44.73 | 0 |
| 10 | 44.84 | 0 |
| 11 | 44.90 | 0 |
| 12 | 44.94 | 0 |
| 13 | 44.96 | 0 |
| 14 | 44.98 | 0 |
| 15 | 44.99 | 0 |
| 16 | 44.99 | 0 |
| 17 | 45.00 | 0 |
| 18 | 45.00 | 0 |
| 19 | 45.00 | 0 |
| 20 | 45.00 | 0 |
| 21 | 45.00 | 0 |
| 22 | 45.00 | 0 |
| 23 | 45.00 | 0 |
| 24 | 45.00 | 0 |
| 25 | 45.00 | 0 |
| 26 | 45.00 | 0 |
| 27 | 45.00 | 0 |
| 28 | 45.00 | 0 |
| 29 | 45.00 | 0 |
| 30 | 45.00 | 0 |
Methods
- DebtRank Battiston and others (2012), Scientific Reports 2:541
- Differential DebtRank Bardoscia and others (2015), PLOS ONE 10
- Contagion in financial networks Gai and Kapadia (2010), Proc. R. Soc. A 466
- Clearing in financial networks Eisenberg and Noe (2001), Management Science 47
- 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
No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme's own evidence.
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Financial Cyber and Operational Resilience in Foundry
- Tested at Financial Cyber and Operational Resilience in Universe
- Codified in Digital, Cyber and AI in Standards
- Published in Financial Cyber and Operational Resilience in Reports
- Taught in Faculty of Technology and Digital Systems in Academy
08Connections
Topics that rely on this method.
09Twin and cascade simulator
Trace a shock to the balance sheet.
Assembled from the Nexus Standards area profile Cyber resilience and digital operational resilience: the shocks it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Pick an initiating shock and step through the cascade to see which exposures it reaches and where losses could concentrate. The Map view places the run on an example geography with open data. Every weight is a stated assumption. An exercise, not a forecast, a valuation, a rating or investment advice. To model a place of your own choosing, open the full System Builder.
Hazards
- Dam failure
- River flood (set off downstream)
- Flash flood (set off downstream)
- Mass displacement (set off downstream)
- Widespread power outage (set off downstream)
- Water supply shortfall (set off downstream)
Systems and couplings
Controls
NS-0Constitution and principlesNS-9Contextual UNIs (deployments, instances, programme instances)NS-10Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-12Software, model and AI/ML artefacts (supply chain, model cards, dataset sheets)NRS-X6Cyber and digital security riskNRS-H54Hazard family: Signal and platform disruption (NHS-1 cyber.platform)NRS-P10Third-party, outsourcing and concentration riskNRS-H107Hazard family: Digital infrastructure concentration and monoculture (NHS-1 tech.digital-concentration)NRS-K31Domain pack profile: Cyber national resilience (DOMAIN-031)NRS-S7System profile: NXS-EOP (Earth observation and digital twins)
Observation
- Known Exploited Vulnerabilities catalogue (listed for reference)
- National Vulnerability Database (CVE enrichment API) (listed for reference)
- OSV open source vulnerability database (listed for reference)
- Proportion of population covered by a mobile network, by technology
Example geography
- North America: 243 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Rain rate, last half hour (IMERG), NASA GIBS
- Surface soil moisture (SMAP), NASA GIBS
10Stewardship
Who maintains the method.
Systems Science and Modelling stewards network science and contagion for the Consortium and reviews how it is applied across platforms.
11Exposure, loss and capital
What it 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.
Transmission channels
How a shock in network science and contagion for financial cyber and operational resilience reaches credit, market, underwriting, liquidity and operational risk, and feeds back.
NGFS climate scenarios · Network for Greening the Financial SystemIFRS S2 Climate-related Disclosures · ISSBTCFD recommendations · Financial Stability Board TCFDPrinciples for the effective management of climate-related financial risks · Basel Committee on Banking Supervision
12Connected
Where this page leads across the Consortium.
13Questions
Questions and answers.
What are the limits of network science and contagion in this field?
Network data are often incomplete or confidential. Static network models may miss adaptive behaviour during cascades.
How are results checked?
Models are checked by reproducing observed cascades and outbreaks. Robustness of results to missing or uncertain links is tested.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method's use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
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