BIS total credit statistics, delivered as one leverage panel

Three series from the total credit panel, exactly as they land:

FREQ BORROWERS_CTY  TC_BORROWERS TC_LENDERS           VALUATION UNIT_TYPE       TC_ADJUST  2023-Q4   2025-Q4
Q    LU             N            A (All sectors)      M         770 (% of GDP)  A          402.8     358.8
Q    NZ             P            B (Banks, domestic)  M         770 (% of GDP)  A          132.3     132.8
Q    SE             P            A (All sectors)      M         USD (Billions)  A          1463.297  1657.085

Read left to right and each row is a fully specified promise: quarterly frequency, a borrower country, a borrowing sector, a lending sector, a valuation basis, a unit and a break-adjustment flag - then one value per quarter, running 344 columns deep. Luxembourg's non-financial-corporation credit fell 44 points of GDP in eight quarters, 402.8 down to 358.8. New Zealand's bank lending to its private non-financial sector barely moved, 132.3 to 132.8. Sweden's private-sector credit in dollar terms climbed about 13 percent, $1,463.3 billion to $1,657.1 billion - a rise mixing borrowing growth with currency translation, which is precisely why the unit axis exists as a selector.

Identity lives in the dimensions, not in column names. Nothing about a series key is hard-coded, so adding an economy or a sector cut extends the grid instead of reshaping it - and narrowing a pull to your country panel is a filter on existing columns, not a re-parse of a new file.

The raw packaging is where most builds lose their week. Four frictions show up reliably:

The wide layout runs one column per quarter - 344 of them - so naive parsers meet their widest frame here, and reshaping wide to long precedes any regression.

Unit columns mix percent-of-GDP with US-dollar billions in the same file, which quietly poisons sums and ratios until the unit triple (measure, multiplier, descriptor) is respected. Japan's private-sector credit reads seven digits in yen and two digits as a share of GDP; both are correct, neither is comparable to the other.

Valuation and consolidation choices diverge at exactly the interesting moments. Market-value and nominal-face-value government debt separate when the price of credit moves; consolidated and unconsolidated figures differ by design between government and private sectors.

Break adjustment decides whether a long run is real. Adjusted series splice statistical breaks into continuous history; unadjusted ones preserve raw prints. A pipeline that grabs whichever loads faster ends up charting both without knowing it.

Datadory resolves all four upstream of delivery: wide and long layouts both available, units typed beside values, valuations kept as explicit dimensions, break flags carried on their own axis. The table below maps the field dictionary that makes that possible.

Ten dimension fields pin down what a number is before the number appears, verified during our August 2026 research pass against the released files. Frequency comes first in the key - Q, always Q, because this dataflow has no monthly cut. The unit travels as a three-part code. And the break-adjustment flag is the difference between a clean long-run read and a false step.

The derivatives, debt-securities and exchange-rate families inside the same record carry their own dimension sets, shipped as additional fields alongside your sample once the requested cut is known. Everything shares the economy-by-quarter spine, so a joined pull keeps one key set end to end.

BIS Total Credit & Debt Securities Statistics carries the same credit grid filed on our internet-services shelf, adding resident-issued international debt securities beside it - useful when one integration needs leverage plus issuance supply in a single contract. Scored 9/10 like its financial-exchanges sibling.

BIS Debt Securities and Capital Markets Statistics follows instruments rather than borrowers: debt securities outstanding and issuance across more than 50 economies by issuing sector, maturity band and currency. Joining it on economy and quarter puts the demand side (who owes) beside the supply side (what was issued).

BIS Locational Banking Statistics supplies the creditor flow: roughly 609,000 quarterly series of cross-border bank claims and liabilities running unbroken since 1977-Q4 - the lending counterpart this panel only summarizes.

Federal Reserve Economic Data Portal - Board Releases adds margin context: H.15 selected interest rates observed back to 1959, weekly H.8 bank assets since 1973 and quarterly Z.1 flow-of-funds reaching 1945, the rate path beside the leverage stock.

Macro strategists and economists benchmark government and household debt trajectories using the pre-built G20, advanced-economy and emerging-market aggregates, watching gaps widen before headlines form. Their workflows sit at financial exchanges & data for investors and quants.

Quant researchers build quarterly leverage factors from the 45-plus-year private-sector history and feed them to backtests that would otherwise starve for sample length.

Data scientists and ML engineers mirror the dimensional grid as local warehouse features; identity-in-dimensions means restatements never cause schema drift. See data scientists use cases.

Journalists, academics and students cite the institutional source for cross-country debt comparisons - continuity back to 1940-Q2 ages better than any highlights reel.

Market researchers and consultants frame country-risk and financial-depth sections from one internally consistent record rather than a collage of national sites; notes live at market researchers use cases.

All five share one habit worth keeping: logging economy, sector pair, valuation and unit with every pull, so results reproduce after the next revision cycle. Delivery metadata at Datadory carries that automatically.

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

Open the product page itself - BIS Statistics Exchange and Derivatives Data - for sample rows, the full field dictionary and coverage chips, then request a sample cut to your economies and sector pairs. For the whole slice, the financial-exchanges-data data hub lists nineteen datasets from tick vendors to official statisticians, best financial exchanges datasets ranks them side by side, and the Bank for International Settlements source profile shows everything else shipping from the same publisher.

Then go deeper on the threads this page touches: the SDMX grammar behind the keys lives in the SDMX API glossary entry, the positioning families beside credit live in the OTC derivatives glossary entry, and the scored trade-off against American wholesale structure sits at Economic Census NAICS 424 vs BIS Statistics Exchange and Derivatives Data. Read the dictionary, then ask for the sample: your economies, your sectors, your quarters.