Federal Reserve Payments Study – U.S. Noncash Payments Volumes and Trends

The Board of Governors of the Federal Reserve System publishes the Federal Reserve Payments Study (FRPS) to "estimate aggregate trends in noncash payments in the United States" and give the payments system "a periodic benchmark" - and this record carries that benchmark as typed rows.

The current Initial Data Release, dated July 2026, reports national payment volumes for calendar years 2015, 2018 and 2021 and 2024, with trend figures reaching back to 2000. Its Table 1 - "Noncash payments, 2015, 2018, 2021, and 2024" - breaks the total out by instrument: cards, with debit divided into non-prepaid and prepaid and general-purpose, private-label and electronic-benefits-transfer variants; credit cards; automated clearinghouse (ACH) credit transfers and debit transfers; and interbank checks. Additional estimates cover checks written, checks converted to ACH, and ATM cash withdrawals. Every instrument line arrives four ways - number in billions, value in trillions of dollars, average ticket in dollars, and compound annual growth rates for each triennial window.

A companion release of March 2026 profiles payment volumes at the Top 100 depository institutions for 2015 through 2022 under the Depository Financial Institution Payments Study (DFIPS). Within Datadory's catalog of 1,744 datasets across 159 viable industries this record scores 9/10 on the quality rubric with all eight field definitions verified during research, ranking sixth among the industry's 16 primary records.

Three instrument rows from Table 1 of the verified workbook, flat exactly as the fields arrive:

# Row 1 - the headline print: what America moved noncash in a year
noncash_payment_type : Total
study_year           : 2015
number_billions      : 142.41623675675001
value_trillions      : 83.992252819585204

# Row 2 - cards carry the count: seven of every ten items
noncash_payment_type : Cards
study_year           : 2015
number_billions      : 101.53816766678
value_trillions      : 5.52066510642534

# Row 3 - debit inside cards: volume leader, smaller tickets
noncash_payment_type : Debit cards
study_year           : 2015
number_billions      : 67.8468270390303
value_trillions      : 2.46896311091788

The rows settle the questions buyers ask first. Scale: row one is 142.42 billion items worth $83.99 trillion - the denominator every US payments claim gets measured against, printed to full workbook precision rather than rounded for a slide. Mix: dividing row two by row one reads cards' 71% share of item count straight off two cells, no derivation sheet required. Split logic: row three sits inside row two - debit alone was 67.85 billion items and $2.47 trillion - so the prepaid/non-prepaid divide falls out of the same grid instead of a second source.

A note printed in the workbook matters as much as any cell: "All estimates are on a triennial basis. Card payments were also estimated for 2016, 2017, 2019, 2020, and 2022." The cadence is a feature of the design, announced rather than hidden.

Eight documented fields, definitions verified during the August 2026 research pass by downloading the study's Excel data tables and reading them, grouped into three families.

Classification: Noncash payment type, the instrument row label - debit cards, credit cards, ACH credit transfers, ACH debit transfers, checks - that keys every observation; and Period, the year stamp behind the 2000-24 trend series.

Measurement: Number, the payment count in billions; Value, the dollar amount in trillions; Average, the average ticket in dollars; and the CAGR columns for the 2015-18, 2018-21 and 2021-24 windows.

Figure-series cuts: ACH credit transfers and Non-prepaid debit cards, the isolated columns the study publishes for the splits analysts reach for first.

Read together, those eight already answer how much America pays noncash, on which instruments, at what average size, growing how fast - the skeleton every richer question hangs on. Blank cells or "ND" in the upstream tables mark figures the study did not report, and they travel through as nulls rather than impersonating zeros.

Beyond the verified core, the wider study surface gets mapped to your use case at sampling:

• Top 100 institution detail - the DFIPS companion cut of payment volumes at the largest depository institutions, 2015 through 2022, joined onto the same row shape.
• The complete trend series - noncash payments by number and by value for every year from 2000 through 2024, pulled out of the figure sheets as typed rows.
• Interim-year card estimates - the 2016, 2017, 2019, 2020 and 2022 prints that bridge the triennial benchmarks.
• Average-ticket and growth columns - the dollar averages and three CAGR windows laid out per instrument, ready for model inputs.
• Additional estimates - checks written, checks converted to ACH, and ATM cash withdrawals.

Ask for any of them with your sample and they arrive in the identical schema - nothing here requires a second format.

Geography - the United States at the national level, plus the Top 100 depository institutions in the companion cut. This is the country's central bank counting its own payments system, not a comparative panel; the 27-jurisdiction view lives with the shelf-mate in the rail below.

Temporal - three clocks, deliberately. The benchmark clock strikes every three years: 2015, 2018, 2021 and 2024 are the study years, with the current release dated July 2026. The card clock fills the gaps annually - 2016, 2017, 2019, 2020 and 2022 carry card-only estimates. And the trend clock reaches furthest, tracing number and value back to 2000. Pin the vintage per figure rather than assuming one continuous series.

Granularity - triennial national estimates keyed by payment instrument crossed with metric: number, value, average ticket, growth rate. Roughly twenty instrument rows by four metrics in the core table, which is precisely why every figure carries outsized weight - these are the reference numbers, not a sample of convenience. Set against the wider catalog - a mean quality of 7.81 across 1,744 datasets - this record's 9/10 reflects a verified workbook, verified fields and figures the entire US payments industry cites back to.

API, files, or your warehouse. Daily, weekly, or hourly.

Name the instruments, metrics and benchmark years when you request the sample - the full Table 1 grid, one instrument's series across all three triennial windows, or the 2000-24 trend figures joined beneath the benchmarks. The sample ships first either way; the ongoing feed lands on whatever cadence your planning cycle runs at, with each new Initial Data Release diffing cleanly into the last.

A national payments benchmark earns its keep in six jobs:

• Payments market sizing - counts and dollar values by instrument hand TAM models official denominators; see market-sizing workflows.
• Rail-mix analysis - card versus ACH versus check shares, with the debit splits most sources collapse away.
• Growth assumption modeling - the three CAGR windows become forecast inputs without anyone re-deriving them.
• Concentration studies - the Top 100 cut shows how much volume rides the largest depository institutions.
• Long-horizon trend work - a 2000-to-2024 spine anchors claims about the migration off paper.
• Citable consulting evidence - figures attributed to a named Board program; see citation-grade research.

Market Researchers & Consultants hold the strongest fit: official denominators for any US payments study (market researchers use cases). Investors & Quants read card, ACH and check trajectories when underwriting processors and networks (investors quants use cases). Data Scientists & ML Engineers get typed, labeled rows that load as-is (data scientists use cases). Sales & Growth Teams size segments by instrument mix before spend. Policy & Academia cite a named Board publication with benchmark years printed beside every figure. Start from the trading companies & distributors data hub for the pooled view.

Four things, stated plainly.

First, triennial cadence with annual fill-ins. The benchmarks land every three years - 2015, 2018, 2021, 2024 - while card estimates bridge 2016, 2017, 2019, 2020 and 2022. Nobody should model this as a monthly feed; payment-system telemetry at that frequency lives elsewhere.

Second, not-reported is marked, not interpolated. Blank cells or "ND" in the upstream tables mean the study did not publish that figure, and general-purpose card totals are defined as net, authorized and settled and may not sum due to rounding. Those caveats ride through the feed rather than disappearing.

Third, the underlying tables are free on the Board's site - the study's Excel data tables are published for anyone to take, with the Board asking only to be cited as the source. What Datadory adds is the normalized, sampled, monitored feed and the delivery layer wrapped around it.

Fourth, scope the sample deliberately. Instruments, metrics, benchmark years, whether the Top 100 cut belongs in scope - name them and the sample honors exactly that. Get a sample of this dataset.

This record owns the American deep-dive; the neighbors around it complete the picture:

• BIS CPMI Red Book Statistics – Retail Payments, Currency and FMIs - the 27-jurisdiction panel that sets this benchmark in context; the field-by-field comparison settles when to use which.
• ECB Payment Statistics – TARGET Services Monthly Settlement Traffic - monthly, euro-area, system-level settlement detail beside an annual national lens.
• WTO Stats - the goods flows that generate the payment obligations these rails settle.
• UCI Wholesale Customers Dataset - a single distributor's client-level book, the micro view of settlement behavior the benchmark aggregates over.

The ranking context sits in best trading companies & distributors datasets, the source profile in the Federal Reserve Board record, and the pooled industry view on the trading companies & distributors data hub.