Skip to content

GRA · Pembayaran dan Infrastruktur Pasar

Keuangan spasialTempat modal bertemu bahaya, keterpaparan, dan transisi

Reliability and resilience engineering for payments and market infrastructure

Resilience engineering studies how Sistem 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.

01Daftar

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

Catatan metodeMetode STEM
  1. MetodeRekayasa keandalan dan ketahanan
  2. Diterapkan diPembayaran dan Infrastruktur Pasar
  3. Halaman kanonisSTEM Commons
02Permohonan

What reliability and resilience engineering does here.

Reliability and resilience engineering studies how Sistem 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 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.

03Uji tekanan

Bagaimana guncangan pada aset menjadi kerugian sistem.

Ekuitas yang hilang dari sistem setelah penularan antarbank, dibandingkan dengan pukulan awal, seiring berubahnya leverage, keterhubungan, dan tingkat pemulihan, yang merupakan inti dari Uji tekananmakroprudensial. Model pengajaran dengan nilai ilustratif yang dinyatakan, bukan prediksi.

Kendali

Penurunan aset eksternal
2.0%
Leverage (aset terhadap ekuitas)
10 kali
Porsi aset antarbank
10%
Pemulihan atas tagihan antarbank
40%
Bank dalam sistem
20
Bank inti
20% bank

Pembacaan

Ekuitas yang hilang akibat guncangan awal
18.0%
Ekuitas yang hilang setelah penularan
45.0%
Ditambahkan oleh penularan (DebtRank)
27.0 poin
Bank yang gagal bayar
0
Angka, metode, dan sumber
PutaranEkuitas sistem yang hilang (%)Bank yang gagal bayar
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

Metode

  1. DebtRank Battiston and others (2012), Scientific Reports 2:541
  2. DebtRank Diferensial Bardoscia and others (2015), PLOS ONE 10
  3. Penularan dalam jaringan keuangan Gai and Kapadia (2010), Proc. R. Soc. A 466
  4. Kliring dalam jaringan keuangan Eisenberg and Noe (2001), Management Science 47
  5. Kipas ketidakpastian (Monte Carlo parametrik) Spiegelhalter, Pearson dan Short (2011), Memvisualisasikan ketidakpastian tentang masa depan, Science 333
  6. Sensitivitas satu per satu (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.

Bagaimana guncangan pada aset menjadi kerugian sistem. Model pengajaran ilustratif: model ini berjalan pada nilai yang ditetapkan oleh kontrol, bukan pada data terukur. Metode: DebtRank (Battiston dkk. (2012), Scientific Reports 2:541); Differential DebtRank (Bardoscia dkk. (2015), PLOS ONE 10); penularan dalam jaringan keuangan (Gai dan Kapadia (2010), Proc. R. Soc. A 466).
04Data

Apa yang digunakan metode ini.

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 register sumber.

DataPeranLabel
Incident reportsFailure causesDILAPORKAN
Test resultsRecovery performanceTERAMATI
Service mapsDependenciesDINYATAKAN
05Eksposur, kerugian, dan modal

What it means for exposure, loss and capital.

Tampilan eksposur, kerugian, skenario, dan modal yang akan diminta oleh pejabat risiko, otoritas pengawas, atau kementerian keuangan, beserta kerangka pelaporan yang mereka gunakan. Strukturnya mengikuti praktik pengawasan dan aktuaria. Nilai bersifat ilustratif: tidak pernah merupakan harga, peringkat, atau saran.

Saluran transmisi

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.
Skenario iklim NGFS · Network for Greening the Financial SystemIFRS S2 Pengungkapan terkait Iklim · ISSBRekomendasi TCFD · Financial Stability Board TCFDPrinsip-prinsip pengelolaan efektif risiko keuangan terkait iklim · Basel Committee on Banking Supervision
06Validasi

Cara hasil diperiksa.

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

PemeriksaanCara pelaksanaannya
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.

08Pengelolaan

Siapa yang memelihara metode ini.

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

10Pertanyaan

Tanya jawab.

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.
Bagaimana hasil diperiksa?
Reliability predictions are compared with field failure data. Exercises and post-event reviews check whether recovery performed as planned.
Apakah nilai di halaman ini nyata?
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.
Di mana lagi metode ini diterapkan?
Observatory Halaman metode kanonis mencantumkan setiap platform yang menerapkannya.
LaporanBerkontribusi

Kontribusikan bukti dari halaman ini.

Laporan 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.

Global Risks AlliancePembayaran dan Infrastruktur PasarSistem StrategisSkenario dan Uji Tekanan
Pembayaran dan Infrastruktur Pasar

Ajukan pertanyaan eksposur atau modal kepada Aliansi.

Informasi dan pertanyaan sudah terbuka hari ini. Sebuah pertanyaan menerima nomor referensi dan menyebutkan langkah berikutnya; lamaran, pembayaran, alokasi komputasi, dan pendaftaran hanya dibuka ketika tim, ketentuan, dan kapasitas tinjauannya tersedia.