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Fintech Nexus · Financial-system transmission

Financial-system transmission

A failure in a technology provider, a model or a digital platform can become a financial-system problem within hours, and financial stress can in turn reach households and businesses that depend on digital services. This page shows how real-system and financial vulnerabilities propagate through digital finance: the channels, the amplifiers and the limits of what aggregate research can show. It is shared industry research published by GRA, not advice.

Publisher
The Global Risks Alliance
Field
Capital markets and investment, Cyber and digital resilience, Digital public infrastructure
Status
Established
Service level
R0: information and enquiries

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Latest available

NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth

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Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.

For financial-system transmission, in GRA's spatial-finance reading, the map opens on population density and night lights with built-up and settlement extent from NASA GIBS, framed on World; every other NASA layer, live feed and forecast waits in the layer list.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.

Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · GDACS · CIESIN/Columbia, NASA SEDAC HBASESources and licences

  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). CC BY 4.0
  • VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • NASA Fire Information for Resource Management System (FIRMS), VIIRS 375 m active fire product, NASA LANCE; vector tiles via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
  • © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). ODbL
  • Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
  • Globe: MapLibre GL JS 6.9.1, BSD-3-Clause, © MapLibre contributors.
  • Live: GDACS (EC JRC / UN OCHA) Events are indicative, not official warnings.
  • Context: CIESIN/Columbia, NASA SEDAC HBASE. Center for International Earth Science Information Network (CIESIN), Columbia University, and NASA Socioeconomic Data and Applications Center (SEDAC): Global Human Built-up And Settlement Extent (HBASE) 2010; Seto, Guneralp and Hutyra 2012, Global Grid of Probabilities of Urban Expansion to 2030; Global Urban Heat Island (UHI) Data Set, 2013.
  • Optional layers (35 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.

Layers and dates

  • Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
  • Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
  • Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
  • Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026
  • Active fires and thermal anomalies, last day: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS · 13 Oct 2026

GeographyFor financial-system transmission, in GRA's spatial-finance reading, the map opens on population density and night lights with built-up and settlement extent from NASA GIBS, framed on World; every other NASA layer, live feed and forecast waits in the layer list.

STEM methods
5
Canonical methods this field applies
Topics
9
Subject pages in the field
Counterparts
4
Platforms in shared fields at GCRI, GRF and GRA
Report series
2
Nexus Reports series it contributes to
01In brief

The short answer.

Risk transmission in Fintech Nexus is the path by which physical, transition, technology and systemic shocks reach the sector and travel onward through balance sheets, markets and the real economy. Analysis uses aggregated, sector-level indicators such as payment system availability and time to recover a critical payment service; it assesses no individual firm and is not investment advice.

02Canvas

One field, three institutions.

Canvas · one field, three institutionsDesign, not a record
One field, three institutions: Fintech Nexus and its counterpartsGCRI studies the field as science, GRF governs and convenes it, GRA builds shared financial understanding; the Nexus Universe tests work across them and the competent authorities decide. Design of the arrangement, not a record.

Fig.How GCRI, GRF and GRA share one field while the authorities decide. Design, not a record.

03Console

Field overview, in outline.

The working screen behind this subject: its structure, terms and states. Names are placeholders and values illustrative.

Field overview: the screen's structure, terms and states for Fintech.Placeholder names and illustrative values; a design, not a record or a live system.
04Channels

How technology risk reaches the financial system.

Concentration

Shared providers

Many institutions rely on the same cloud, payment processors, software and data vendors, so one outage or breach affects many at once.

Speed

Instant runs and settlement

Instant transfers and real-time settlement remove buffers, so liquidity stress and fraud move faster.

Correlation

Common models

Similar models and data across firms can produce similar decisions, amplifying credit cycles or market moves.

05Channels

How fintech failures reach the wider system.

  • Through clients: a payment or core-banking provider outage interrupts many banks at once.
  • Through deposits and runs: digital channels let customers move money in minutes, shortening the time to a run.
  • Through stablecoins: reserve asset sales during redemptions can affect short-term funding markets.
  • Through data: a breach at a data aggregator exposes customers of many institutions.
06Prudential link

Where banks meet crypto exposure.

The Basel Committee's prudential standard for cryptoasset exposures sets how banks hold capital against tokenised assets, stablecoins and unbacked cryptoassets. It limits the direct bank channel; indirect channels through payments and funding remain. Digital assets context continues at GRA digital assets.

07Amplifiers

Why small failures can become large.

Interconnection through application interfaces means that a disruption at one firm can cascade to partners whose services depend on it. Social media can accelerate confidence shocks, as rumours about a firm's solvency spread to customers who can withdraw funds immediately. Opaque chains of outsourcing make it hard for any single institution, or supervisor, to see the full set of dependencies.

Transmission runs to the real economy as well. When a payment service fails, merchants cannot sell, workers may not be paid and remittances stop, with the heaviest effects on people and small businesses that depend on a single digital channel. Cyber incidents that disrupt financial services can also undermine trust in digital finance more broadly.

Recovery is part of transmission. How quickly a provider restores service, whether institutions can switch to alternatives, and whether customers keep confidence during the outage together determine whether a technical incident stays contained or becomes a financial event.

08Real economy to finance

Shocks travelling the other way.

Physical events, such as power outages, floods affecting data centres or telecommunications failures, can disable digital financial services. Economic downturns test digital lenders whose models were trained in benign conditions. These links are explored with GCRI at digital service resilience.

Analysis uses aggregated data at sector level. It does not assess individual firms, recommend transactions or constitute investment advice. Any data from firms is aggregated before use, under the safeguards set out in GRA's written competition policy.

10Key terms

Terms used on this page.

Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.

TermDefinition
Transmission channelThe route by which a shock reaches financial institutions' balance sheets.
ContagionTransmission of losses or stress from one institution or market to others.
Second-round effectsFeedback from bank losses to the economy, such as tighter credit deepening a downturn.
Common exposureHoldings of the same or similar assets by several institutions, which makes their losses move together.
11Sources

Where this page's references lead.

The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.

12Questions

Questions and answers.

Is competitor data shared?
No. Any data from firms is aggregated before use, under the safeguards set out in GRA's written competition policy, which counsel reviews before membership opens.
Does this page predict a crisis?
No. It describes channels through which stress could travel. Scenarios, when published, are labelled as such and carry their assumptions.
Who acts on systemic risk?
Central banks, supervisors and resolution authorities, under their own mandates.
Fintech Nexus

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