Trading Companies & Distributors · European Central Bank
Traffic Settled in the TARGET Services — ECB Monthly Statistics
Datadory delivers trading companies & distributors data covering ecb payment statistics target services monthly settlement traffic data: monthly counts and settled values for every payment the Eurosystem settles across T2, T2S and TIPS, observed as year x month x service-segment cells from 1999 through 2026 - 138,507,792 payments in January 2026 alone, worth EUR 73,846.1 billion.
API, files, or your warehouse. Daily, weekly, or hourly.
- Where it covers
- Eurosystem - euro area national TARGET components, with connected non-euro settlement (Danish krone sections observed) on the same tables
- How far back
- Aggregate volumes and values 1999-2026; T2 accounts 2009-2026; T2S Dedicated Cash Accounts 2015-2026; legacy domestic and cross-border breakdowns 1999-2008
- How fine
- One observation per year x month x service segment x currency - monthly totals plus daily averages, values on a mirror grid in EUR billions
What is the ECB Payment Statistics – TARGET Services Monthly Settlement Traffic dataset?
Every euro that moves between banks in earnest crosses a rail the Eurosystem runs, and the European Central Bank publishes the meter readings. TARGET Services - T2, T2S and TIPS, owned and operated by the Eurosystem - settle the continent's large-value traffic, and the monthly statistics report what each service cleared: volumes as payment counts and settled values in EUR billions, laid out as year-by-month grids from 1999 through 2026. January 2026 alone records 138,507,792 payments worth 73,846.1 billion euros. The sheets split traffic five ways - all TARGET payments, Central Liquidity Management related payments, real-time gross settlement related payments, and transactions on T2S and TIPS Dedicated Cash Accounts - so the mix of old plumbing versus instant rails is visible rather than averaged away. Within Datadory's catalog of 1,744 datasets across 159 viable industries this record scores 8/10, and the reasoning is unpacked below.
What do sample rows look like?
One observation cell, flattened for reading - the layout is exact, the values are real January and July 2026 figures:
# one observation cell - year x month x service segment (layout exact)
year : 2026
month : Jan
section : All TARGET payments
total_volume : 138507792 # payments settled in the month
daily_average : 4860146 # payments per settlement day
# more January 2026 cells, identical shape
section : CLM related payments total_volume : 151972 daily_average : 7237
section : RTGS related payments total_volume : 8509478 daily_average : 405213
section : TIPS Dedicated Cash Accounts total_volume : 112978592 daily_average : 3644471
# July 2026 - the busiest month on the current sheet
section : All TARGET payments total_volume : 182757876 daily_average : 6208972
# companion values table, same grid
settled_value_eur_bn : 73846.1 # January 2026, all TARGET payments
# keys that hold across the whole corpus
KEYS : year, month, service_segment, settlement_currency
SEGMENTS : all_target_payments | clm_related | rtgs_related | t2s_dca | tips_dca
CURRENCY : EUR sections throughout; DKK sections observed alongsideThree properties carry the analytical weight. First, every cell is counted twice: a raw monthly total and a daily average ride together, so February never looks weak merely because it is short. Second, service_segment keeps the flows honest - instant settlement growth cannot hide inside a gross-settlement total. Third, the values table mirrors the volumes grid segment for segment, in EUR billions, which makes velocity-style ratios possible without re-shaping anything.
What fields does the dataset include?
Seven dimensions define a fully expanded observation: when it happened (year, month), where it settled (service segment, settlement currency), and what it measured (total volume, daily average, and settled value in EUR billions on the companion table). The dictionary reads like a query grammar because that is how the corpus is assembled - pick a table family, a year, a segment section, a currency block, and every remaining cell is determined. Definitions stay conservative where the source is: counts are counts, values carry their unit in the column name, and nothing pretends to be more granular than a calendar month.
Which fields arrive only on request?
Everything beyond the headline aggregate gets mapped to your use case at sampling:
- National TARGET component breakdowns - T2 account traffic per country component, volumes and values, from 2009.
- T2S Dedicated Cash Account detail per national component, from 2015.
- Legacy cross-border versus domestic splits covering 1999 through 2008, for long-run continuity work.
- Year-total rows alongside the monthly grid in every table family.
- Aligned values cells matched to whichever volume cut you take.
Specified and populated when you ask for them; mapped during the sample stage rather than dumped raw.
What geography, time range, and granularity does coverage span?
Geography - the Eurosystem: euro area national TARGET components, with settlement in connected non-euro currencies appearing as its own blocks; Danish krone sections are observed on the current sheets.
Temporal - aggregate TARGET volumes and values run 1999 through 2026, starting four days after the euro itself did. The account-level families arrive later: T2 accounts from 2009, T2S Dedicated Cash Accounts from 2015, and the legacy domestic and cross-border breakdowns stop at 2008. Three dates mark the machinery itself - T2S went live June 2015, TIPS launched November 2018, and the consolidated T2 platform replaced TARGET2 on 17 March 2023, with T2 statistics since 2023 revised in March 2025.
Granularity - one observation per year x month x service segment x currency: a monthly total plus a daily average, with values published on a mirror grid. Nothing finer than a calendar month, nothing coarser than the whole system.
How is the data delivered?
API, files, or your warehouse. Daily, weekly, or hourly.
Name the segments, currency blocks and year range you want when you request the sample - instant-rail adoption only, gross settlement only, or the full five-segment grid back to 1999. The sample ships first either way; the ongoing feed lands shaped to the scope you named rather than as twenty-eight annual sheets to reconcile yourself.
Who uses this data, and for what?
A system-level payments ledger earns its keep in six jobs:
- Euro-area activity nowcasting - settlement counts are spending that already cleared; see market-sizing workflows.
- Instant-payments adoption tracking - TIPS volumes on their own line turn adoption into a single ratio; pairs naturally with competitive intelligence.
- Treasury and settlement-capacity benchmarking - daily averages per segment size the flow a working day carries.
- Securities-settlement throughput monitoring - T2S cash-account traffic tracks the securities side of settlement.
- Structural-break detection - a 27-year window containing a platform migration and an instant rail is regime-model fuel; see backtesting patterns.
- Macro denominator - official throughput against which payments-industry claims get scaled, the way citation-grade research expects.
Which personas get the most value?
Investors & Quants get euro-area transaction velocity back to 1999, older than nearly every alt-data series on the market. Data Scientists & ML Engineers get typed monthly panels keyed on year, month, segment and currency that join onto euro-area country-period features without reshaping. Competitive Intelligence & Product Teams get the true mix of settlement traffic - gross, liquidity-management and instant - which is where payments whitespace actually shows. Market Researchers get payment throughput as a demand proxy with enough history to beat seasonality into submission. Journalists & Academics get central-bank-published figures on the plumbing itself. Where each playbook lives: investors & quants, data scientists, competitive intel, market researchers, journalists & academics.
How does it compare to other trading companies & distributors datasets?
This is the strangest fish on the wholesale shelf, and deliberately so. Domestic aggregates - Eurostat short-term business statistics for NACE G46 turnover, the Statistics Canada wholesale sales table, the NAICS 42 workforce profile - count trade inside economies. Shipment archives - Panjiva and ImportGenius - name the firms moving goods. This record measures the money plumbing underneath both: not who traded, but that the payment settled, in what quantity, on which rail. The nearest structural cousin in spirit is the UCI Wholesale Customers Dataset only as a contrast case - spend labeled by channel versus settlement counted by system - and the head-to-head lives on its own comparison page. Weighing the full slate sits on our best trading companies & distributors datasets ranking.
What should I know before requesting a sample?
Four things. First, this is infrastructure telemetry: no company names, no invoices - if you need counterparties, the shipment archives answer and this one does not. Second, mind the splice points: T2 replaced TARGET2 on 17 March 2023 and the affected statistics were revised in March 2025, while the legacy domestic and cross-border breakdowns simply stop at 2008; series that span those seams need a stated treatment. Third, counts and values ride different units - payment counts versus EUR billions - so any ratio needs the pairing made explicit. Fourth, currency sections matter: euro settlement dominates, but connected non-euro blocks such as Danish krone appear on the same sheets, and filtering the currency key is not optional. This record scores 8/10 in our catalog; validate segment definitions and the 2023 seam against your own pipeline before committing.
Field dictionary
Every field below is documented against real records. The full dictionary ships with the sample.
| field | type | definition | example |
|---|---|---|---|
month | date | Calendar month column (Jan-Dec) inside each reported year - the period axis of every cell. | Jan |
year | integer | Reference year of the annual table the cell belongs to. Aggregate volumes and values run 1999 through 2026; the account-level families start later. | 2026 |
service_segment | string | Which slice of the Eurosystem's settlement machinery the row reports: all TARGET payments, Central Liquidity Management related payments, real-time gross settlement related payments, transactions on T2S Dedicated Cash Accounts, or transactions on TIPS Dedicated Cash Accounts. | All TARGET payments |
total_volume | integer | Count of payments settled in the month for the named segment - the throughput column, and the one instant-payments adoption reads come from. | 138507792 |
daily_average | integer | Average payments per settlement day in the month, published next to every monthly total so calendar length never masquerades as momentum. | 4860146 |
settlement_currency | string | Currency section the cell sits under. Euro settlement dominates; connected non-euro currencies appear as their own sections - Danish krone sections are observed on the current sheets. | EUR |
settled_value_eur_billions | number | Value of payments settled in the month, expressed in EUR billions on the companion values table. Never average it against the count column - different units, same grid. | 73846.1 |
TARGET Services segments - what each section of the monthly tables reports
| segment | what settles there | how it reads |
|---|---|---|
| All TARGET payments | Every payment settled across T2, T2S and TIPS combined | The system-wide throughput line |
| CLM related payments | Central Liquidity Management service traffic - liquidity transfers between accounts | The plumbing behind the plumbing; small counts, large values |
| RTGS related payments | Real-Time Gross Settlement service traffic on individual orders | The classic large-value backbone |
| Transactions on T2S Dedicated Cash Accounts | Cash legs of securities settlement on dedicated cash accounts | Where settlement cycles and custody workloads show up |
| Transactions on TIPS Dedicated Cash Accounts | Instant payments settled in central-bank money | The adoption line everyone watches |
What teams do with it
- Euro-area activity nowcasting Settlement counts are spending that already cleared. Month-by-month throughput moves with the real economy faster than most official aggregates get revised.
- Instant-payments adoption tracking TIPS Dedicated Cash Account volumes sit on their own line, so the share of Eurosystem traffic settling instantly is a division, not a research program.
- Treasury liquidity and settlement-capacity benchmarking Daily averages per segment size how much payment flow a working day carries, which is the denominator treasury and bank-operations teams argue about.
- Securities-settlement throughput monitoring T2S Dedicated Cash Account traffic ties cash leg activity to the securities side, useful wherever settlement fails and custody workloads matter.
- Structural-break and regime detection A series spanning 1999 through 2026 contains a currency launch, two platform generations and an instant-payments rail - regime models finally get events to chew on.
- Macro denominator for payments-industry feeds Official system-level throughput scales everything private sources claim; market-share assertions stop floating free of the plumbing they ride on.
Questions buyers ask
How many observations does the dataset contain?
Six table families per year across twenty-eight years, each annual table holding roughly 25 rows by 13 columns - a compact corpus by design, not a bulk dump of everything the central bank publishes. The full aggregate spine is small enough to hold in your head and bounded enough to ship complete; the national-component and legacy breakdowns expand on request.
What does one row measure?
The payments settled for one service segment in one calendar month: a total count, the daily average against that month's settlement days, and - on the mirror values table - the settled amount in EUR billions. One row, one segment, one month, one currency block.
How far back does coverage go?
Aggregate TARGET volumes and values start in 1999 - the first table covers the euro's opening weeks, since TARGET commenced on 4 January 1999 - and run through 2026. T2 account-level traffic starts in 2009, T2S Dedicated Cash Accounts in 2015, and the legacy domestic versus cross-border breakdowns cover only 1999 through 2008.
What are the TARGET Services?
The settlement systems owned and operated by the Eurosystem: T2, which comprises the Central Liquidity Management service and the Real-Time Gross Settlement service; T2S, whose Dedicated Cash Accounts carry the cash leg of securities settlement; and TIPS, which settles instant payments. Each contributes its own section to the monthly statistics, which is why the mix between them is observable rather than blended.
Did anything change mid-series that I should handle?
Yes, three times. T2S went live in June 2015 and TIPS launched in November 2018 - new sections appear from those points rather than backfilled zeros. The consolidated T2 platform replaced TARGET2 on 17 March 2023, and the T2 statistics covering 2023 onward were revised in March 2025. Any model spanning those dates should state its treatment of the seams instead of absorbing them silently.
Can I track instant-payment adoption directly?
That is one of the cleanest reads in the corpus. TIPS Dedicated Cash Account transactions publish as their own section - 112,978,592 payments in January 2026 against 138,507,792 for all TARGET payments that month - so the instant share of Eurosystem settlement is a single ratio computed on the delivered rows, with daily averages available to de-season it.
What should I validate before requesting a sample?
Three things: that the segment set you need - all TARGET, CLM, gross settlement, T2S cash accounts, TIPS - matches the question you are asking, that your currency scope is explicit given the non-euro blocks riding on the same sheets, and how you want the 2023 platform transition treated in any series crossing it. Name them in the sample request and the returned rows settle it.
See the rows before you pay anything.
Name this dataset and we send real records from it — scoped to the fields you asked for.