FDIC Quarterly Banking Profile download, delivered: 160+ quarters of industry aggregates as typed rows

Two views of the same quarter. The first is the headline scorecard the press quotes. The second is the structural map of the time-series release - seven themed grids of aggregate line items, every row carrying a period and an asset-size breakout.

TIME-SERIES RELEASE MAP - seven themed grids 01 Ratios by asset size groups 02 Ratios by community vs non-community banks 03 Balance sheet 04 Loan performance 05 Quarterly income 06 Annual income 07 Small business and farm loans

BREAKOUT DIMENSION - carried on every aggregate row asset_size_group: All Insured Institutions | Assets < $100 Million | Assets $100 Million - $1 Billion | larger bands ```

Five themes organize everything: earnings, asset quality, net interest margin, capital and problem banks. Ten core fields define the aggregate grid - five income-statement aggregates (total interest income, net interest income, provision for loan and lease losses, total noninterest income, total noninterest expense), the bottom-line net income figure, two asset-quality counters (net charge-offs and the count of unprofitable institutions), the number of institutions reporting, and the asset_size_group enum that turns one flat industry number into a curve across size bands. Monetary amounts arrive in USD millions, typed, definitions attached.

Everything else inside a release - full balance-sheet detail, loan-performance splits, small business and farm lending lines, community-bank ratio sets, deposit-insurance-fund trend series - maps into exactly the same shape as additional fields on request.

• Temporal: quarterly observations archived from Q1 1986 through the current quarter - more than 160 quarters, one of the longest continuous runs in US banking statistics - plus annual rollups. The latest verified release covers Q1 2026.
• Granularity: industry aggregates, not bank-level rows: one value per line item per period per breakout, subtotaled by asset size group and by community versus non-community status. That split matters because community banks fund and lend differently than the money-center tier, and the profile quantifies the difference without making you build peer groups yourself.
• Population: every FDIC-insured institution rolled into national aggregates, roughly 4,700 active charters.

Depth and constancy travel together: a margin study built on 1990s quarters uses the same shape as a current-quarter refresh, so the entire run lives in one table instead of a folder of vintages. When the question narrows to a single charter, the aggregate hands off to the FDIC BankFind Suite API, whose institution-quarter financials reach toward the 1930s.

• Is the sector getting better or worse? Net interest margin, provisions, charge-offs and bottom-line profit as one national series since 1986 - direction established before anyone argues about a single bank.
• How does one bank's quarter compare with the tide? Community and non-community subtotals give the peer baseline a management team's “we beat the industry” claim gets measured against.
• Where does credit stress sit by size? Problem-bank counts and loan-performance aggregates by asset-size band show whether stress is a tail phenomenon or a broad wave.
• What does a full cycle look like? A 160-plus-quarter run spans the savings-and-loan aftermath, the dot-com break, the global financial crisis and the 2023 regional episode - enough cycles to calibrate a model instead of narrating a memory.

What aggregates cannot answer is equally sharp: anything about one named institution. There the drill-down records take over - and the two layers reconcile cleanly, because both descend from the same supervisory universe.

• FDIC BankFind Suite API - the bank-level complement, scoring 10 in our catalog: certificates, locations, 4,115 failure records and quarterly financials reaching toward the 1930s. The profile says how the industry is doing; BankFind says who, where and since when.
• SEC EDGAR XBRL Financial Statements API - reported GAAP fundamentals per issuer; a single large-bank record runs about 8 MB across 900-plus concepts. Join regulatory aggregates to published statements and the two reconcile instead of arguing.
• FDIC Bank Data Guide - the index over six FDIC dataset families - registry, financials, peer groups, deposits, history, structure events - for when one aggregate series is not enough.
• BIS Locational Banking Statistics (LBS) & BIS Data Portal - cross-border positions across roughly 609,000 series from 1977-Q4; see the two banking aggregates head-to-head for where each wins.
• ECB Statistics — Consolidated Banking Data (CBD2) - the euro-area mirror image: 66,139 series across 30 reference areas from 2007-Q4.
• World Bank Global Financial Development Database - 108 indicators across 214 economies annually from 1960, for setting the US aggregate against peers; World Bank Indicators — Bank Capital to Assets Ratio is the nearest analogue to the profile's capital section, spanning roughly 265 economies.

1. Investors and quant researchers tracking margin, provisioning and profitability cycles across all insured institutions since Q1 1986 - the base-rate series any single bank's trajectory gets judged against.
2. Market researchers and consultants anchoring reports in official aggregates cut by institution size rather than vendor estimates.
3. Competitive intelligence teams benchmarking a rival's results against community and non-community peers before crediting management's “we beat the industry” line.
4. Data scientists parsing each release into tidy features keyed on period and asset-size band, ready to merge onto bank-level panels.
5. Journalists, academics and students quoting the regulator's own quarterly scorecard with definitions behind every field.
6. Sales and growth teams carrying enough industry context into a bank pitch that they sound like they read earnings season - so their buyers do not have to.

Files, feeds, or straight into your warehouse. Daily, weekly, or hourly - your call.

Every delivery carries the complete field dictionary, the sample rows and the coverage statement - mapped and typed before it reaches you, identical columns and join keys whichever channel you take.

Snapshots pin by release, so a report re-run next month reproduces against the same vintage instead of drifting with aggregate revisions. Nobody on your team re-derives a codebook mid-project, and the wrapper a statistical release ships in upstream stops being your problem the moment the rows are typed.

Name the periods, size bands and line items when you request a sample - the last eight quarters of margin and provision aggregates cut to the bands your model prices, or the full problem-bank series if stress mapping is the job. Real rows come back shaped to that specification, field dictionary and coverage statement attached.

You keep the sample, the dictionary and the coverage statement regardless of what happens next. Production follows the exact shape you evaluated - the thing you tested is the thing that ships.

This page is one workflow inside the diversified-banks stack. The diversified-banks data hub carries field dictionaries and sample rows for every record in the slice, and the best diversified-banks datasets ranking scores all eleven primaries on one rubric - the profile sits fourth among them on fit for aggregate questions.