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Spatial financeWhere capital meets hazard, exposure and transition

Reliability and resilience engineering for payments and market infrastructure

Resilience engineering studies how pergalan anticipate, absorb, recover from and adapt to disruption. On this platform it frames impact tolerances, recovery design, failover and the testing that shows whether infrastructure can keep critical services running.

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01Qeyd

Reliability and resilience engineering for payments and market infrastructure on the record.

Method recordSTEM method
  1. RêbazEndezyariya pêbawerî û berxwedanê
  3. Applied inDayîn û binesaziya bazarê
  6. Canonical pageSTEM Commons
02Serlêdan

What reliability and resilience engineering does here.

Reliability and resilience engineering studies how pergalan fail, how failures can be prevented and how systems absorb, adapt and recover from disruption. It addresses both technical components and the organisations that operate them. In financial stability, cyber and digital resilience and digital public infrastructure, resilience engineering identifies critical components and recovery bottlenecks across connected systems. It informs redundancy, spares and restoration priorities. The canonical description is on the STEM Commons.

Impact tolerance setting

Important services are mapped to their dependencies and tolerances are set from the harm that disruption would cause users and markets.

Recovery design

Recovery options, from hot standby to rebuilding from clean backups, are compared for time and integrity under destructive scenarios.

Learning from incidents

Post-incident reviews of outages are analysed for causes and the controls that would have reduced impact, across operators.

03Stress 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 Testa zextê. Teaching model with stated illustrative values, not a prediction.

Kontrol

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

Xwendin

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
Jimar, rêbaz û çavkanî
RoundSystem equity lost (%)Banks in default
018.000
128.800
235.280
339.170
441.500
542.900
643.740
744.240
844.550
944.730
1044.840
1144.900
1244.940
1344.960
1444.980
1544.990
1644.990
1745.000
1845.000
1945.000
2045.000
2145.000
2245.000
2345.000
2445.000
2545.000
2645.000
2745.000
2845.000
2945.000
3045.000

Rêbaz

  1. DebtRank Battiston and others (2012), Scientific Reports 2:541
  2. Differential DebtRank Bardoscia and others (2015), PLOS ONE 10
  3. Contagion in financial networks Gai and Kapadia (2010), Proc. R. Soc. A 466
  4. Clearing in financial networks Eisenberg and Noe (2001), Management Science 47
  5. Pankeya nediyarîyê (Monte Carlo ya parametreyan) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
  6. Hesasiyeta yek-yek (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley

Dane

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.

How a shock to assets becomes a system loss. Illustrative teaching model: it runs on the values set by the controls, not on measured data. 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).

Li seranserê şeş stûnan, bi pêşengiya Nexus Consortium

04Dane

What the method consumes.

Datasets and records used by reliability and resilience engineering on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.

DaneRolNîşan
Incident reportsFailure causesREPORTED
Test resultsRecovery performanceÇAVDÊRÎKIRÎ
Service mapsDependenciesDECLARED
05Exposure, 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 reliability and resilience engineering for payments and market infrastructure reaches credit, market, underwriting, liquidity and operational risk, and feeds back.

Channel structure after the Basel Committee’s work on climate-related risk drivers and transmission channels; links shown are typical paths, not estimated exposures.
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
06Erêkirin

How results are checked.

Reliability predictions are compared with field failure data. Exercises and post-event reviews check whether recovery performed as planned.

Kontrol bikinHow it is done
Severe scenariosTests include destructive and multi-site scenarios
Integrity on recoveryRecovery is checked for data correctness, not only availability
Remediation closureFindings are tracked to verified closure

Common-cause failures and rare events are hard to estimate from limited data. Human and organisational factors resist quantification.

07Connections

Topics that rely on this method.

08Stewardship

Who maintains the method.

Binesaziya girîng stewards reliability and resilience engineering for the Consortium and reviews how it is applied across platforms.

Canonical page: Reliability and resilience engineering

09Girêdayî

Ev rûpel li ser Konsorsiyûmê te ber bi kû ve dibe.

10Pirs

Pirs û bersiv.

What are the limits of reliability and resilience engineering in this field?
Common-cause failures and rare events are hard to estimate from limited data. Human and organisational factors resist quantification.
How are results checked?
Reliability predictions are compared with field failure data. Exercises and post-event reviews check whether recovery performed as planned.
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?
Çavdêrxaneya canonical method page lists every platform that applies it.
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rêzeya Pergalên Stratejîk is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.

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