Trading Companies & Distributors Data Provider · Head-to-head
BIS CPMI Red Book Statistics – Retail Payments, Currency and FMIs vs Federal Reserve Payments Study – U.S. Noncash Payments Volumes and Trends
Which trading companies & distributors data provider data fits your job: BIS CPMI Red Book Statistics – Retail Payments, Currency and FMIs, or Federal Reserve Payments Study – U.S. Noncash Payments Volumes and Trends. API, files, or your warehouse. Daily, weekly, or hourly.
BIS CPMI Red Book Statistics – Retail Payments, Currency and FMIs
Federal Reserve Payments Study – U.S. Noncash Payments Volumes and Trends
Where the fields line up
No shared field names. These two answer different questions.
| Field | BIS CPMI Red Book Statistics – Retail Payments, Currency and FMIs | Federal Reserve Payments Study – U.S. Noncash Payments Volumes and Trends |
|---|---|---|
FREQ | Observation frequency of the series. | not in this set |
REP_CTY | Reporting country or jurisdiction behind every observation — the axis that turns the collection into a cross-country panel. | not in this set |
MEASURE | Whether the observation counts transactions or values them. | not in this set |
INSTRUMENT_TYPE | Payment instrument category: credit transfers, direct debits, cheques, card and e-money payments, cash withdrawal and deposit rows. | not in this set |
CARD_FCT | Card function split — credit, debit or delayed debit — wherever the reporting jurisdiction supplies it. | not in this set |
CARD_ISS_LOC | Where the card was issued relative to the reporting country, separating domestic spending from cross-border card use. | not in this set |
TIME_PERIOD | Reference year of the observation. | not in this set |
OBS_VALUE | The observed statistic for the series cell — transaction counts, currency values, ratios. | not in this set |
UNIT_MULT | Unit multiplier applied to the observed value, so a raw figure reads correctly at scale. | not in this set |
UNIT_MEASURE | Unit of measure: number of payments, domestic currency, US dollar, per cent change, per-capita ratio. | not in this set |
TITLE | Human-readable series title combining country, measure and instrument — the label a chart or report quotes directly. | not in this set |
TABLE | Red Book comparative table number the series belongs to, mapping every row back to the published table family. | not in this set |
Coverage, side by side
| BIS CPMI Red Book Statistics – Retail Payments, Currency and FMIs | Federal Reserve Payments Study – U.S. Noncash Payments Volumes and Trends | |
|---|---|---|
| Geographic | 27 CPMI member jurisdictions including the US, euro area countries, UK, Japan, India, Brazil, Mexico, Saudi Arabia and Singapore | United States - national aggregates plus Top 100 depository institutions |
| Temporal | 2012-2024 verified in downloaded files; earlier Red Book editions extend further back | National top-line 2015-2024 with trends from 2000; Top 100 institution detail 2015-2022 |
| Granularity | Annual, by country x indicator x instrument x measure x unit | Triennial national estimates by payment instrument; annual card estimates between benchmarks |
What each contains
They tie on 1 attribute. Pick by fit, not by loyalty.
| BIS CPMI Red Book Statistics – Retail Payments, Currency and FMIs | Federal Reserve Payments Study – U.S. Noncash Payments Volumes and Trends | |
|---|---|---|
| Publisher | Bank for International Settlements (CPMI) | Federal Reserve Board |
| Subject lens | Retail payments, currency and payment devices, plus financial market infrastructure participation and activity | Aggregate US noncash payment volumes and values by instrument, with trends |
| Geographic coverage | 27 CPMI member jurisdictions including the US, euro area countries, UK, Japan, India, Brazil, Mexico, Saudi Arabia and Singapore | United States - national aggregates plus Top 100 depository institutions |
| Temporal coverage | 2012-2024 verified in downloaded files; earlier Red Book editions extend further back | National top-line 2015-2024 with trends from 2000; Top 100 institution detail 2015-2022 |
| Granularity | Annual, by country x indicator x instrument x measure x unit | Triennial national estimates by payment instrument; annual card estimates between benchmarks |
| Scale | 68,197 rows in comparative table 1; 19,514 rows in the cashless file; eight CPMI dataflows | One table plus two figure series covering roughly 20 instrument rows x 4 metrics |
| Documented fields | 13 | 8 |
| Signature stat | US 2023 card and e-money payments valued at about $10.79 trillion | 142.42 billion noncash items worth $83.99 trillion in 2015 |
| Best for | Cross-country benchmarking of payment instruments and FMI activity | US market sizing by instrument with average ticket and growth rates |
Or take both in one feed
They stack cleanly because they occupy different layers of the same system. A defensible workflow: build the US baseline on the study's instrument-level counts, values, averages and CAGRs, then set it in context with the Red Book's 27-jurisdiction grid - per-capita ratios, dollar-converted values, device and cash series, FMI activity.
Align before joining. The Red Book prints observations with explicit unit codes and multipliers - the verified US 2023 card-and-e-money value reads 10,786,745.849 under a millions-of-US-dollars multiplier, about $10.79 trillion - while the study fixes units in its column headers, billions of items and trillions of dollars. Agree on calendar year and unit scale first and the two totals meet without friction. Datadory ships either record alone or both merged onto one delivery calendar, delivered daily, weekly, or hourly - your call. Or take both in one feed.
API, files, or your warehouse. Daily, weekly, or hourly.
Fair questions
Is BIS CPMI Red Book Statistics better than the Federal Reserve Payments Study?
Better depends on the question. The Red Book wins whenever the frame crosses a border: one set of instrument definitions applied across 27 jurisdictions, with per-capita ratios, dollar-converted values and an FMI layer on top. The study wins whenever the question is American: instrument-level US counts, values, average tickets and CAGRs, plus Top 100 institution detail. Both score 9/10.
Do the two datasets overlap?
On the United States. Both publish American card and noncash payment totals, so the two views can be read side by side - the Red Book's US cells sit inside a 27-country grid, while the study decomposes the same economy into prepaid and non-prepaid debit, ACH credit and debit transfers, checks and ATM withdrawals. Elsewhere they complement rather than compete.
Which dataset covers more geography?
The Red Book, decisively: 27 CPMI member jurisdictions including the US, euro area countries, UK, Japan, India, Brazil, Mexico, Saudi Arabia and Singapore. The study is deliberately single-country - national US aggregates plus a Top 100 depository institution cut - trading breadth for depth no international compilation reproduces.
Which payments dataset reaches further back?
It depends which window you trust. The study publishes trend figures by number and value from 2000 to 2024, with triennial benchmarks for 2015, 2018, 2021 and 2024.
Which one should a market-sizing analyst sample first?
Sample both, then let the model decide. For US market sizing - instrument shares, average ticket, growth by CAGR window - start with the Federal Reserve Payments Study, whose Table 1 already carries the arithmetic. For a global total addressable market or a country-by-country ranking, start with the Red Book's 27-jurisdiction panel.
Can Datadory deliver both datasets together?
Yes. Either record arrives alone or both arrive merged onto one delivery calendar, aligned on calendar year and unit scale so the join is done before it reaches you - delivered daily, weekly, or hourly, your call. Name the jurisdictions, instruments and windows when you request the sample and it lands pre-cut.