GRA · Banking Nexus
Spatial financeWhere capital meets hazard, exposure and transition
Credit and counterparty risk
Credit risk is the chance that a borrower or counterparty fails to pay in full and on time, and it remains the main source of loss for most banks. This page sets out how credit quality deteriorates, how losses concentrate in sectors, regions and single names, and how counterparty exposures in derivatives and securities financing pass stress from one institution to another. It names the measures risk committees and supervisors use and the limits of each.
Credit and counterparty risk in seven parts.
- 01The questionCredit and counterparty risk: how the risk develops
- 02IndicatorsWhat is measured, and how well
- 03SourcesWhere the evidence comes from
- 04DecisionsWho decides, and what the evidence serves
- 05Core questionsShared questions the industry asks
- 06MechanismsHow the risk arises and transmits
- 07Methods and limitsHow evidence is gathered, and where it stops
Credit and counterparty risk: how the risk develops.
Credit quality, concentration and contagion. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Quality erodes before default
Arrears, covenant breaches, rating downgrades and forbearance usually precede default by several quarters. Under IFRS 9 a significant increase in credit risk moves a loan from a twelve-month to a lifetime loss allowance, so provisions rise before losses are realised.
Losses concentrate
Exposures cluster in sectors, regions, collateral types and large single names. A shock to one segment, such as commercial property or an export industry, can drive losses well beyond what a diversified portfolio model implies.
Counterparty exposure moves with markets
In derivatives and securities financing the amount owed changes with prices. Wrong-way risk arises when exposure grows just as the counterparty weakens, and margin calls in volatile markets can drain liquidity at the same moment.
Defaults propagate
A large counterparty failure imposes losses on creditors, freezes collateral and triggers close-out across many contracts. Uncertainty about who holds the exposure can lead lenders to withdraw from sound institutions as well.
A loan book under scenario families.
How physical and transition risk could reach a loan book under different scenarios. Illustrative design values.
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 |
|---|---|---|---|---|
| Non-performing loan ratio | percent of gross loans | quarterly | Supervisory returns, Pillar 3 disclosures | Published statistics |
| Stage 2 and stage 3 loan share | percent of gross loans | quarterly | IFRS 9 financial statements | REPORTED |
| Coverage ratio of non-performing loans | percent of non-performing loans provisioned | quarterly | Supervisory returns | Published statistics |
| Probability of default by rating grade | percent over one year | annual | Internal ratings-based models | MODELLED |
| Loss given default | percent of exposure at default | annual | Workout and recovery data | ESTIMATED |
| Large exposures to single counterparties | percent of Tier 1 capital | quarterly | Large exposures reporting | Published statistics |
| Sectoral concentration index | Herfindahl index of exposures by sector | quarterly | Credit registers, supervisory returns | ESTIMATED |
| Potential future exposure on derivatives | currency units at a stated confidence level | daily | Counterparty credit risk models | MODELLED |
Capital and liquidity console.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Capital and liquidity console
Credit and counterparty risk · Bank B · Reporting date Q3
Capital stack
% of RWA vs requirement and buffers
Liquidity
% vs minimum
CET1 ratio walk, stress scenario
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 |
|---|---|---|
| IMF Financial Soundness Indicators | Non-performing loans, provisioning and sectoral loan distribution reported by national authorities | Free, IMF terms of use |
| EBA risk dashboard and EU-wide transparency exercise | Bank-level asset quality, forbearance and IFRS 9 stage data for EU banks | Free, attribution |
| ECB Supervisory Banking Statistics | Asset quality and credit risk aggregates for significant institutions in the euro area | Free, attribution |
| FFIEC Call Reports and Federal Reserve FR Y-9C | Loan categories, past-due and charge-off data for US banks and holding companies | Public domain |
| BIS consolidated banking statistics | Banks' foreign claims by counterparty country and sector on an ultimate-risk basis | Free, BIS terms of use |
| Rating agency default and recovery studies | Historical default and recovery rates by rating grade and sector | Licensed, subscription |
| National central credit registers | Borrower-level exposures for concentration and interconnection analysis | Restricted, supervisory access |
Trace a shock in credit and counterparty risk to the balance sheet.
Assembled from the Nexus Standards area profile Banking: 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
- Tropical cyclone
- Earthquake
- Ice-jam flood
- Wildfire
- Dam failure
- Sea-ice loss
- Storm surge (set off downstream)
- Intense rainfall (set off downstream)
- River flood (set off downstream)
- Flash flood (set off downstream)
- Dangerous surf (set off downstream)
- Widespread power outage (set off downstream)
- Mass displacement (set off downstream)
- Coastal flood (set off downstream)
- Coastal erosion (set off downstream)
- Aftershock sequence (set off downstream)
- Fault surface rupture (set off downstream)
- Soil liquefaction (set off downstream)
Systems and couplings
- Banking
- Central banks and financial supervisors Coupling
- Insurance and reinsurance Coupling
- Cities and the built environment Coupling
- Digital, telecom and platforms Coupling
Controls
NS-0Constitution and principlesNS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-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-14Conformance and crosswalk to external standardsNRS-U2Universe platform profile: Banking NexusNRS-F2Banking climate, nature and transition riskNRS-H113Hazard family: Financial-system fragility (NHS-1 soc.financial-system)NRS-K20Domain pack profile: Climate adaptation (DOMAIN-020)NRS-U8Universe platform profile: Energy NexusNRS-D1Risk-informed development and project screening
Observation
- BIS statistics (banking, credit, debt securities, property prices) (listed for reference)
- IMF data services (IFS, FSI, WEO, GFS) (listed for reference)
- World Development Indicators (listed for reference)
- GLEIF Golden Copy (LEI Level 1 and Level 2 data) (listed for reference)
- Bank capital to assets ratio (%)
- Bank nonperforming loans to total gross loans (%)
- Domestic credit to private sector (% of GDP)
- (a) Number of commercial bank branches per 100,000 adults and (b) number of automated teller machines (ATMs) per 100,000 adults
Example geography
- Western Europe: 152 hexagonal cells
- Rain rate, last half hour (IMERG), NASA GIBS
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Carbon monoxide, smoke transport (AIRS), NASA GIBS
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 |
|---|---|---|
| Bank board and board risk committee | What credit risk appetite to set, and which sector and single-name limits to impose | Concentration measures, stage migration and stress losses by segment |
| Chief risk officer and credit committee | When to tighten underwriting, raise provisions or reduce exposure to a weakening sector | Early warning indicators, rating migration and the assumptions of loss models |
| Prudential supervisor | Whether asset quality, provisioning and large exposures warrant supervisory action or a Pillar 2 add-on | Peer comparison of non-performing loans, coverage and concentration |
| Macroprudential authority | Whether a sectoral systemic risk buffer or a borrower-based measure is needed | System-wide concentration and interconnection measures |
Outputs this topic produces
- Explainers on IFRS 9 staging, provisioning and concentration measures
- Concentration and contagion maps of sector and counterparty exposures built from public aggregates
- Comparisons of default and loss modelling approaches with their stated assumptions
- Sector briefings on emerging credit stress, with evidence labels
What credit and counterparty risk 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.
Capital, solvency and fiscal indicators
The indicators a risk officer, supervisor or ministry of finance reads first on credit and counterparty risk.
| Indicator | Unit | Who reads it | Framework |
|---|---|---|---|
| CET1 ratio | % of risk-weighted assets | Banks, supervisors | Basel framework |
| Liquidity coverage ratio | % of stressed outflows | Banks, central banks | Basel framework |
| Solvency ratio | % of solvency capital requirement | Insurers, supervisors | IAIS Insurance Core Principles |
| Combined ratio | % of earned premium | Insurers | Actuarial practice |
| Protection gap | uninsured share of economic loss | Insurers, ministries of finance | Published loss statistics |
| Public debt | % of GDP | Ministries of finance, IMF | Debt Sustainability Framework |
| Gross financing needs | % of GDP per year | Ministries of finance, investors | Debt Sustainability Framework |
Shared questions the industry asks.
- 01
How do shocks become defaults?
How income loss, collateral damage and refinancing difficulty translate into missed payments and losses.
- 02
Where do exposures concentrate?
Common exposures to regions, sectors and large borrowers across many lenders.
- 03
How does counterparty failure spread?
How losses on derivatives, repo and interbank exposures pass between institutions.
- 04
How early can deterioration be seen?
Which data signal credit migration before it appears in defaults.
How the risk arises and transmits.
Credit losses depend on three elements: the likelihood of default, the exposure at default and the loss given default after recovering collateral. Shocks can move all three at once: a disaster raises defaults, draws on credit lines increase exposure and damaged collateral lowers recoveries.
Defaults are correlated. Borrowers in the same region or sector are hit by the same shocks, so portfolio losses are lumpier than models assuming independence suggest. Correlations tend to rise in stress, precisely when diversification is most needed.
Concentration amplifies. Large exposures to a single borrower group, or many banks lending to the same exposed sector, mean that one failure can cause large losses across the system.
Counterparty risk links institutions directly. Margining and central clearing reduce it but create liquidity demands in stress, when collateral must be posted quickly.
Credit cycles have their own dynamics. Lending standards loosen in good times and tighten in bad, amplifying economic swings.
How evidence is gathered, and where it stops.
| Method or data | What it shows | Its limit |
|---|---|---|
| Loan-level data | Borrower characteristics and performance | Confidential; access limited and aggregated |
| Credit scoring and rating models | Relative default likelihood | Calibrated on past conditions |
| Portfolio models | Loss distributions and concentration | Sensitive to correlation assumptions |
| Network analysis | Interbank and counterparty linkages | Exposure data often incomplete |
How it is studied, and what the methods cannot say.
- 01Financial and risk engineeringExpected credit loss modelling · Counterparty exposure and valuation adjustmentsApplied here
- 02Probability and statisticsEstimating default and loss parameters · Operational loss modellingApplied here
- 03Network science and contagionInterbank and counterparty contagion · Overlapping portfolios and fire salesApplied here
- 04Financial economics and macro-financeMacro-financial stress scenarios · Credit cycle and buffer calibrationApplied here
- 05Artificial intelligence and machine learningCredit decisioning with explainability · Early warning and transaction monitoringApplied here
Questions and answers.
What is the difference between credit risk and counterparty risk?
Why do provisions rise before defaults?
What is wrong-way risk?
Does Banking Nexus assess individual banks or borrowers?
Does GRA rate borrowers or banks?
Why do correlations matter so much?
Can banks share loan data through GRA?
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