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In finance, “value network analysis” can mean mapping how participants exchange tangible and intangible value; financial network analysis usually maps institutions or sectors connected by exposures, holdings, payments, collateral, or operational dependencies. The methods can both use node-and-link diagrams, but they answer different questions. There is no single standardized procedure formally called “value network analysis in finance” established by the sources cited here.
For U.S. financial-system questions, a network map is useful when it makes clear who is connected, by what relationship, over what period, and how complete the underlying data are. It can help examine concentration, exposure, and possible disruption paths; it does not, by itself, show that a disruption will happen.
What a financial network map represents
A network model reduces a defined system to nodes and edges. Nodes might be financial sectors, banks, payment utilities, counterparties, or service providers. Edges represent a specified relationship: one sector holding another sector’s instruments, a funding transaction, collateral moving between participants, or a bank relying on a payment service.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchEdges may be directed and weighted. Direction indicates which way a relationship or flow runs; weight records a quantity such as an exposure balance or estimated payment volume. The unit matters: an outstanding balance is not the same as a transaction flow or a transaction count. A visual line alone does not explain which one the model represents.
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The Federal Reserve distinguishes the Financial Accounts of the United States, which report sector assets and liabilities by instrument, from “from-whom-to-whom” (FWTW) data that add direct sector-to-sector relationships, such as which sectors hold instruments issued by other sectors. The FWTW data follow the Accounts’ sector and instrument definitions, but they are not a complete record of every bilateral relationship. Corporate equities are excluded because of data limitations, and known relationships for many instruments are partial, requiring assumptions. Federal Reserve, March 24, 2023.
How to build an analysis that answers a real question
The following workflow synthesizes examples from Federal Reserve and Office of Financial Research (OFR) work. It is a practical way to structure an analysis, not an official standard.
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- Define the decision or risk question. Specify whether the goal is to understand value creation, identify exposures, trace possible contagion, or consider operational resilience. A map designed for one purpose may not support another.
- Set the boundary and time window. State which entities, markets, services, instruments, and dates are included. A sector-level map over several years and a bank-level daily payment network describe different systems.
- Choose nodes and define each edge. Explain what counts as a participant and what a connection means. Specify whether the relationship is directed, what the weight measures, and the unit used.
- Collect and reconcile the data. Identify the source, reporting period, and coverage. Keep observed links separate from estimated or inferred ones; do not treat a missing edge as proof that no relationship exists.
- Visualize the relevant layers. Separate distinct relationships—such as funding, collateral, assets, and payment-service links—so that one kind of connection is not mistaken for another.
- Choose measures that match the question. Measures such as concentration or node centrality can help identify prominent connections, but their meaning depends on the network’s definitions and data.
- Interpret results with coverage limits and scenarios in view. If testing an outage or failure, state the hypothetical assumptions and distinguish the scenario’s possible consequences from the likelihood of that event.
How financial connections show up in practice
Sector holdings and exposures
FWTW data connect sectors through holdings of instruments issued by other sectors. This adds a bilateral view to sector-level assets and liabilities, while the exclusions and assumptions described above constrain how precisely those links can be read. Such a map can help organize exposure questions, but it should not be presented as a complete ledger of every claim between institutions.
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Secured funding and collateral
OFR’s collateral map treats collateral as a network exchanged among bilateral counterparties, triparty banks, and central counterparties. In secured funding, the funding flow and collateral movement run in opposite directions. Mapping these layers can clarify how collateral is used in secured funding and derivatives activity. OFR, May 26, 2016.
A separate OFR multilayer map combines short-term funding, collateral, and assets to illustrate possible transmission paths through interconnected participants. The layers help analysts examine how a shock might travel across relationships; a modeled path is not evidence that the shock will occur. OFR, July 14, 2016.
Payments and operational dependencies
A Federal Reserve operational-resilience example maps large banks to payment financial market utilities, using reported key links and estimated weights. Its public-filing basis means it shows only relationships reported in those filings, and the model does not include bank-to-bank links. In a separate 2025 note, the Federal Reserve constructs a bank–payment service provider network and uses concepts such as node centrality to examine hypothetical operational outages. These are model-based scenario analyses, not evidence that a particular provider is more likely to fail.
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In the 2022 note’s discussion of large-value domestic and international U.S.-dollar payments, CHIPS together with Fedwire is described as the primary U.S. network, with CHIPS reported at approximately 96% market share. That figure belongs to the scope of that cited discussion; it should not be read as a current, all-payments market share. Federal Reserve, July 1, 2022.
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For the sample of Y-15 reporting banks examined in the 2025 note, yearly and daily aggregate payment volume correlated at roughly 90%. This is a sample-specific benchmark result, not a general rule for all banks or payment providers. Federal Reserve, January 3, 2025.
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What contagion analysis can—and cannot—tell you
Once exposures or dependencies are represented as a network, analysts can trace paths through which a hypothetical default, funding shock, collateral constraint, or service outage might affect connected participants. The result depends on the edges included, their weights, the time period, and the scenario assumptions. Network analysis can identify a plausible route or estimate potential amplification within a model; it cannot establish from connectivity alone that a disruption is likely.
A historical example shows why the period and method must accompany a number. A Federal Reserve Bank of New York staff report, published in November 2017 and revised in October 2019, studied U.S. financial institutions over 2002–16. It found expected spillovers negligible in 2002–07 and 2013–16, while default spillovers could amplify expected losses by up to 25% in 2008–12. That is a study-specific historical estimate, not a current forecast or a timeless measure of systemic risk. Federal Reserve Bank of New York, Staff Report 826.
How to judge whether two network analyses are comparable
Two maps may look alike while representing different questions or evidence. Before comparing their conclusions, check the underlying definitions and coverage:
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- Boundary: included entities, instruments, services, geography, and observation period.
- Node and edge definitions: who or what each node represents and what relationship creates a link.
- Direction and weight: whether links are directional and whether weights represent balances, flows, counts, or estimates.
- Data and completeness: source, reporting basis, exclusions, and whether links are observed, estimated, or inferred.
- Scenario assumptions: what event is being modeled and which responses or dependencies are included.
Without alignment on these points, differences in apparent centrality or exposure may reflect model construction rather than a change in the financial system.
Where transaction-level analysis fits
For institutional users, the Federal Reserve’s FedTransaction Analyzer supports after-the-fact Fedwire transaction analysis, exception review, and risk and compliance workflows. The service page says it provides access to up to seven years of historical Fedwire data. This is a transaction-analysis service, not a substitute for a complete map of all financial relationships. Federal Reserve Financial Services, accessed October 7, 2026.
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