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In US finance, “value network analysis” can mean mapping how people, teams, and organizations exchange tangible or intangible value. Financial network analysis is narrower: it models banks, sectors, market utilities, or service providers as nodes connected by exposures, holdings, payments, collateral, or operational dependencies. The approaches can both use network diagrams, but they answer different questions—and the reviewed sources do not establish one standardized method formally called “value network analysis in finance.”
For a financial network map to be useful, its author must define what is being mapped, how links are measured, and which relationships are observed versus estimated. Those choices determine whether the map can help assess concentration, exposure, contagion paths, or operational resilience.
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What a financial network map represents
A network consists of nodes and links. In a financial analysis, nodes might be sectors, banks, market utilities, or payment service providers. A link represents a defined relationship between them, such as one sector holding another sector’s liabilities, counterparties exchanging collateral, or a bank using a payment utility.
Links may be directed and weighted. Direction shows which way a relationship runs—for example, from an issuer to a holder, or from a bank to a payment provider. Weight gives the relationship a measure, such as a balance, estimated payment volume, or another specified quantity. These units are not interchangeable: a map of outstanding balances is not a map of transaction counts or estimated vulnerability.
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Network analysis differs from an organizational value-network map, which can focus on how participants create or exchange value. A financial map is generally built to examine relationships such as exposures, funding, payments, or dependencies. A diagram alone does not make these two analyses equivalent.
How to build an analysis that answers a financial question
A defensible workflow can be synthesized from Federal Reserve and Office of Financial Research (OFR) examples. It is a practical sequence, not a formally prescribed standard.
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- Define the decision or risk question. Decide whether the goal is to examine holdings, funding, collateral, payment connections, concentration, or a hypothetical disruption.
- Set the boundary and period. Specify which participants, instruments, services, and dates are included. A sector-level map, for example, has a different boundary from a bank-to-provider map.
- Choose the nodes and define the links. State what each node represents and what qualifies as a relationship. Make the link’s direction and unit explicit, such as a holding, collateral flow, or estimated payment volume.
- Gather and reconcile data. Align definitions across sources and periods. Record which relationships are directly reported and which must be inferred or estimated.
- Represent the layers. Separate different relationship types—such as short-term funding, collateral, and assets—when combining them would obscure what each link means.
- Calculate measures suited to the question. Measures such as concentration or node centrality can help identify prominent connections, but their meaning depends on the chosen nodes, links, and weights.
- Interpret findings with coverage limits and scenarios in view. Explain omissions, assumptions, and the conditions of any hypothetical disruption. A modeled path shows how a disruption could propagate under stated assumptions, not that it will happen or is likely.
Examples of financial networks in US research
Sector holdings in the Financial Accounts
The Federal Reserve’s Financial Accounts describe major US sectors’ financial assets and liabilities by instrument. Its From-Whom-to-Whom (FWTW) data add direct sector-to-sector relationships, such as which sectors hold instruments issued by other sectors. The Board says FWTW data are consistent with the Accounts’ sector and instrument definitions. Corporate equities are currently excluded because of data limitations, and known relationships for many instruments provide only partial information, so assumptions are needed to estimate some links. Federal Reserve, “From-Whom-to-Whom Relationships in the Financial Accounts of the United States,” March 24, 2023.
Collateral and secured funding
OFR’s collateral map models collateral exchanged among bilateral counterparties, triparty banks, and central counterparties. Secured funding moves in the opposite direction to the collateral that supports it, so keeping those directions clear matters when interpreting the map. OFR also describes a multilayer map combining short-term funding, collateral, and assets to illustrate possible transmission paths through interconnected participants. These are distinct relationship layers, not one undifferentiated measure. OFR, “A Map of Collateral Uses and Flows,” May 26, 2016; OFR, “Looking Deeper, Seeing More: A Multilayer Map of the Financial System,” July 14, 2016.
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Payment utilities and operational dependencies
A Federal Reserve example connects large banks to payment financial market utilities using reported key links and estimated weights. The authors note that the relationships shown are limited to those reported in public filings and that the model does not include bank-to-bank links. A 2025 Federal Reserve note constructs a bank–payment service provider network and uses concepts including node centrality to examine hypothetical operational outages. These models can help analyze dependencies and scenarios; they do not establish that a particular provider is more likely to fail. Federal Reserve, “An Approach to Quantifying Operational Resilience Concepts,” July 1, 2022; Federal Reserve, “Using Service Provider Connections to Model Operational Payment Networks,” January 3, 2025.
What contagion analysis can—and cannot—show
Network models can help trace how distress might pass through exposures or dependencies. They can also test how a hypothetical failure or outage would affect connected participants under a chosen set of assumptions. This is useful for identifying possible transmission channels, but a modeled pathway is not evidence that a disruption will occur or that its probability is high.
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A Federal Reserve Bank of New York study illustrates why results need their original scope attached. In its 2019 revision of Staff Report 826, covering the 2002–16 research period, expected spillovers were negligible in 2002–07 and 2013–16, while default spillovers could amplify expected losses by up to 25% in 2008–12. That is a historical, study-specific estimate, not a current forecast for US institutions. Federal Reserve Bank of New York, “Empirical Network Contagion for U.S. Financial Institutions,” revised October 2019.
How to read reported statistics without overgeneralizing
Specific network studies can produce useful figures, but each belongs to its stated sample and time frame.
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- About 96%: A 2022 Federal Reserve note reports this as CHIPS’s market share in its discussion of CHIPS and Fedwire forming the primary US network for large-value domestic and international US-dollar payments. It is a figure from that cited discussion, not a timeless market-share guarantee. Federal Reserve, July 1, 2022.
- Roughly 90%: A 2025 Federal Reserve note reports that yearly and daily aggregate payment volume correlated at roughly this level for the sample of Y-15 reporting banks it examined. The benchmark is sample-specific. Federal Reserve, January 3, 2025.
What to check before comparing two network maps
Two maps should not be treated as comparable just because both use nodes and links. Check the dimensions that determine what each map can support:
- Purpose: Is the analysis about value creation, exposure, contagion, or operational resilience?
- Boundary: Which entities, sectors, instruments, and services are included, and what period does the map cover?
- Node and link definitions: Do nodes represent sectors, institutions, or providers? Do links represent holdings, funding, collateral, payments, or another relationship?
- Direction and weight: Is the relationship directed? What does its weight measure, and in what units?
- Data and observation: Which sources and dates underpin the map? Are links reported, estimated, or inferred?
- Completeness: What exclusions or missing relationships limit coverage?
- Scenario assumptions: If the analysis models a disruption, what event and propagation rules does it assume?
A relationship missing from a map is not necessarily absent from the financial system. It may be outside the boundary, unavailable in the source data, or estimated imperfectly. Labeling observed and inferred links separately helps readers distinguish evidence from modeling choices.
Tools and data access for institutional analysis
For after-the-fact institutional analysis of Fedwire transactions, Federal Reserve Financial Services describes FedTransaction Analyzer as supporting transaction analysis, exception review, and risk and compliance workflows. Its service page says it provides access to up to seven years of historical Fedwire data; that is a stated service capability, not a claim that every network map uses this data or has the same coverage. Federal Reserve Financial Services, “FedTransaction Analyzer,” accessed October 7, 2026.
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