GRA · Banking Nexus
Spatial financeWhere capital meets hazard, exposure and transition
Payment dependencies
Every bank depends on payment systems, central counterparties, securities settlement systems, correspondent banks and messaging networks to move money and collateral. These financial market infrastructures concentrate risk by design: they make settlement safe for many participants, but a failure of the infrastructure, or of a large participant inside it, can spread fast. This page sets out how payment dependencies create risk, how they are overseen, and how instant payments and new digital services change the picture.
Payment dependencies on the record.
Payment dependencies: how the risk develops.
Critical market infrastructure 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.
Liquidity is needed intraday
Real-time gross settlement requires banks to fund each payment as it is made. If a large participant delays payments to conserve liquidity, others wait in turn, and gridlock can build within hours.
Central counterparties concentrate risk
Clearing nets exposures and mutualises default losses. In stress, margin calls rise across members at the same time, and a member default draws on a default fund that all members share.
Operational failure halts flows
An outage at a payment system, a messaging provider or a large correspondent stops salaries, securities settlement and trade payments. Most participants have few alternatives available at short notice.
Digital services add new links
Instant payments, fintech intermediaries, stablecoins and cloud-hosted infrastructure create new dependencies and allow faster runs. Some of them sit outside the traditional perimeter of payment oversight.
Climate risk in the book.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Climate risk in the book
Payment dependencies · Mortgage book · Scenario: delayed transition
Collateral and flood hazard
geocoded collateral · illustrative
Exposure by hazard band
| High | 1891 | 155 | 87 |
| Medium | 3705 | 376 | 60 |
| Low | 27,186 | 2266 | 54 |
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 |
|---|---|---|---|---|
| Value settled through large-value payment systems | currency units per day | daily | System operator statistics | Published statistics |
| Payment system availability | percent of operating hours | monthly | Operator disclosures | REPORTED |
| Peak intraday liquidity usage | share of available intraday liquidity | daily | Bank intraday liquidity monitoring | OBSERVED |
| Initial margin and default fund resources at central counterparties | currency units | quarterly | CPMI-IOSCO public quantitative disclosures | REPORTED |
| Settlement fails | value and count per period | monthly | Securities settlement system statistics | Published statistics |
| Active correspondent banking relationships | count of relationships | annual | CPMI analysis of payment messaging data | ESTIMATED |
| Instant payment adoption | instant transactions as share of credit transfers | annual | Central bank payment statistics | Published statistics |
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.
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 Banking Nexus in Foundry
- Tested at Banking Nexus in Universe
- Codified in Finance, Insurance and the Economy in Standards
- Published in Banking 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 |
|---|---|---|
| CPMI Red Book statistics | Payment, clearing and settlement statistics for CPMI jurisdictions | Free, BIS terms of use |
| Public quantitative disclosures of central counterparties under CPMI-IOSCO standards | Margin, default resources, stress testing and availability for each central counterparty | Public, operator terms |
| ECB TARGET Annual Report and payment statistics | Activity, availability and incidents in euro area payment and settlement systems | Free, attribution |
| Federal Reserve Financial Services volume and value statistics | Activity in Fedwire and FedACH services | Public domain |
| Bank of England RTGS and CHAPS statistics | Settlement volumes and values in UK payment systems | Free, Bank of England terms of use |
| FSB and CPMI reports on cross-border payments | Progress, frictions and dependencies in cross-border payment arrangements | Free, attribution |
What payment dependencies 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.
Scenario pathways
How payment dependencies could be stressed under the scenario families central banks and supervisors use.
| Scenario family | Transition risk | Physical risk | What a stress test asks |
|---|---|---|---|
| Orderly | Early, gradual policy | Lower | Can the book absorb a steady repricing? |
| Disorderly | Late, abrupt policy | Lower to medium | What breaks under a sudden repricing and credit migration? |
| Hot house world | Limited policy | High and rising | Where do physical losses concentrate in collateral and insurance? |
Who decides, and what the evidence serves.
Banking 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 |
|---|---|---|
| Central bank as overseer and operator | Which systems are systemically important and what standards and recovery plans they must meet | Availability, incident and participation data; concentration of flows |
| Bank treasury | How much intraday liquidity and collateral to hold, and which backup channels to maintain | Intraday usage profiles, dependency maps and outage scenarios |
| Central counterparty board and risk committee | How to calibrate margin and default resources against extreme but plausible conditions | Stress test coverage and back-testing of margin models |
| Resolution authority | How to preserve a failing bank's access to payment, clearing and settlement services | Continuity of access arrangements and operational dependencies |
Outputs this topic produces
- Maps of payment and settlement dependencies for a typical bank, using public information
- Explainers on central counterparty default waterfalls and margin procyclicality
- Scenarios for infrastructure outages and intraday liquidity stress
- Research notes on instant payments, stablecoins and new service providers
Shared questions the industry asks.
- 01
Which systems are critical?
Which payment and settlement systems, if disrupted, would stop economic activity.
- 02
Who depends on whom?
How banks, payment providers, schemes and technology suppliers connect.
- 03
What happens in an outage?
How quickly disruption spreads to households and firms, and what fallbacks exist.
- 04
How do new forms of payment change risk?
The effect of instant payments, digital wallets and new settlement assets.
How the risk arises and transmits.
Payment systems are networks with central nodes. Large-value systems, clearing houses and card schemes concentrate flows, making them efficient and critical. Their failure would affect all participants at once.
Instant payments reduce settlement risk but increase operational and liquidity demands, because funds move around the clock and must be available immediately. Fraud can also move faster.
Correspondent banking links domestic systems internationally. Withdrawal of correspondent relationships can cut regions off from cross-border payments.
Technology concentration extends dependence beyond the payment operators themselves to the cloud, network and software providers they use.
Cash remains a fallback in many economies, but its declining use, and the closure of branches and cash machines that follows, can reduce resilience when digital systems fail, especially for older, rural and low-income people who rely on it most.
How evidence is gathered, and where it stops.
| Method or data | What it shows | Its limit |
|---|---|---|
| Payment flow data | Volumes and network structure | Confidential; aggregated for research |
| Dependency mapping | Links between participants and providers | Visibility beyond direct links is limited |
| Outage analysis | Causes and effects of past disruptions | Few severe events to learn from |
| Scenario exercises | Fallback effectiveness | Exercises simplify real disruption |
How it is studied, and what the methods cannot say.
- 01Network science and contagionInterbank and counterparty contagion · Overlapping portfolios and fire salesApplied here
- 02Probability and statisticsEstimating default and loss parameters · Operational loss modellingApplied here
- 03Financial and risk engineeringExpected credit loss modelling · Counterparty exposure and valuation adjustmentsApplied here
- 04Artificial intelligence and machine learningCredit decisioning with explainability · Early warning and transaction monitoringApplied here
Questions and answers.
What is a financial market infrastructure?
Why are central counterparties a concern if they reduce risk?
Do instant payments make runs faster?
Does GRA operate or oversee payment systems?
Why does cash matter for resilience?
Are new payment technologies covered?
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