Data Provenance

ApiFinance AI is designed to give you financial data with a clear source trail.

Overview

For U.S. listed companies, the platform emphasizes filing-backed financial data and structured company records that are suitable for analysis, dashboards, and internal tooling.

Source Of Truth

For U.S. listed companies, our financial statement data is sourced directly from SEC filings. That includes the core filing-based data used to power:

• income-statement
• balance-sheet
• cash-flow-statement
• dividend
• secfiling

This makes the dataset suitable for workflows that depend on filing-backed fundamentals rather than manually maintained summaries.

Supported Exchanges and Markets

Company and market metadata currently includes major U.S. listing venues and common symbol-level exchange attributes.

Typical exchange or market fields include values such as:

• NASDAQ
• NYSE
• NYSE American
• Cboe-listed symbols when available in source records

For cross-listed companies, availability depends on the symbol and filing coverage returned by the selected endpoint.

Historical Depth and Coverage

ApiFinance AI is built for long-range analysis, with up to 30+ years of financial history where filing-backed records exist.

That depth supports:

• valuation history
• trend analysis
• long-horizon screening
• backtesting
• fundamental models

Coverage can vary by issuer, filing cadence, listing venue, and source availability for specific periods.

Data Freshness and Latency Expectations

Different endpoint families update on different schedules. In general:

• quote and market snapshot style fields are near real-time to short-delay, depending on market source timing
• daily price history is updated on market data refresh cycles
• filing-backed financial statements update after source filings are published and processed
• SEC filing metadata updates when new filings are detected and ingested

If your workflow depends on strict freshness guarantees, validate updated_at and filing dates in each response before running downstream decisions.

What Gets Normalized and Cleaned

Raw filing data is structured into API-friendly records so you can work with it consistently across symbols and periods.

In practice, that means:

• period-based statements are exposed in a predictable response shape
• common financial line items are normalized into stable fields
• historical series can be compared across multiple reporting periods
• symbol, CIK, and identity metadata are standardized for easier joins across endpoints
• field naming is normalized to reduce client-side mapping logic

Cleaning and Validation Logic

Before data is exposed through API responses, the pipeline applies consistency checks and normalization rules such as:

• schema validation for required structural fields
• period alignment checks for annual versus quarterly series
• numeric parsing and type normalization for financial values
• source-link retention for filing-backed rows when available
• response-shape validation to keep payload formats stable across releases

Normalization aims to make integration predictable while preserving traceability to filing-backed sources.

What To Expect

• Filing data may vary by issuer, reporting cadence, and available history
• Some fields are only available when they appear in the underlying filing
• Data is intended for analysis and application development, not as a substitute for official filings
• Exchange-level symbol coverage can vary by market and source constraints
• Intraday freshness expectations should be validated per endpoint and plan

Related Endpoints

• search
• profile
• dividend
• income-statement
• balance-sheet
• cash-flow-statement
• secfiling

Why It Matters

A transparent data source makes it easier to trust the output, debug edge cases, and explain your results to users or stakeholders.

If you are building dashboards, screening tools, or internal research workflows, data provenance is often as important as the API itself.

For MCP-based agent workflows, pair this page with the MCP setup documentation so your assistant can surface both tool output and provenance context in one reasoning loop.