The financial data services market was valued at more than $28 billion in 2025 and is expected to reach $59 billion by the end of 2035. Financial data drives everything from day-to-day choices to long-range strategy, but pulling that information together quickly and accurately can be challenging without assistance. Financial data vendors help meet the growing demand for real-time market data, company data, and transactional data. They make it possible to work with precise, up-to-date financial information at scale.

Below, you’ll learn what financial data providers are, what kind of information they provide, and how to evaluate which providers are best suited to help your business.

What’s in this article?

• What are financial data providers?
  
• What types of financial data do providers usually offer?
  
• How do financial data providers collect and deliver market, company, and transactional data?
  
• How do organizations evaluate data accuracy, coverage, and latency?
  
• What challenges typically arise when working with financial data providers?
  
• How can businesses compare providers based on cost, licensing models, and technical requirements?
  
• How Stripe Financial Connections can help
  

What are financial data providers?

Financial data vendors are companies that gather, organize, and deliver financial information to businesses. They track market shifts in real time so that investors, finance teams, and operators have accurate numbers when they need them for reporting or forecasting revenue. Whether it’s a price feed that informs a trade or a refreshed account balance a customer expects to see immediately, financial data providers minimize latency and ensure updates appear when they should.

Building and maintaining financial data infrastructure internally can be expensive and never-ending. Providers can maintain the integrations, compliance controls, global coverage, and extract, transform, and load (ETL) pipelines that make raw data usable so companies don’t have to build them from scratch.

Good financial data providers also understand the quirks and edge cases behind the data. They normalize formats, account for corporate actions, resolve mismatched identifiers, and show corrected data when needed. Many companies don’t have the time, infrastructure, or head count to replicate that level of precision internally, which is why they rely on providers.

What types of financial data do providers usually offer?

Financial data providers cover a wide spectrum of information because businesses use financial signals in so many different ways. Here are some of the data types and why each is important.

Market data

This might include real-time and historical prices, volumes, order book activity, benchmarks, and index levels across equities, fixed income, commodities, currencies, and derivatives. These datasets power trading systems, portfolio models, and workflows that require a precise read on market movements.

Company fundamentals

These often include revenue, margins, earnings, balance sheets, cash flows, filings, corporate actions, and analyst estimates. Providers extract and normalize these, most of which are disclosures from regulators and company releases, so teams can compare performance across regions and accounting standards.

Economic indicators

Some financial data providers include macrolevel signals—such as gross domestic product (GDP), inflation, interest rates, labor market data, and customer sentiment—which can help businesses understand the broader environment that affects demand, pricing, capital costs, and planning.

Reference data

Identifiers, classifications, security metadata, corporate hierarchies, and event histories that keep all other datasets consistent are typical. These allow analysts and systems to reliably join data across sources.

Alternative data

Aggregated transaction data, web traffic, shipping and supply chain indicators, satellite observations, and text-derived sentiment are nontraditional signals. They provide teams with early readouts of trends not yet visible in public filings or earnings.

News and sentiment feeds

Machine-readable news, alerts, and sentiment scores derived from articles, transcripts, and social signals help reveal risks and opportunities faster than manual monitoring.

Transactional and account-level data

With user permission, providers often aggregate bank transactions, account balances, credit information, and spending patterns. These datasets underpin credit models, personal finance tools, and many modern fintech experiences.

How do financial data providers collect and deliver market, company, and transactional data?

Financial data providers aggregate data from a variety of sources, and they’re expected to deliver it in formats that teams and systems can use. Here’s how this works.

Data collection

How providers gather data depends on which type they’re collecting:

• Market data: Providers connect directly to exchanges and trading venues to capture every price tick, trade, and update as it happens. They often colocate servers near exchanges and use high-speed networks to reduce latency and ensure the feed reflects the market with minimal delay.

• Company data: Filings, earnings releases, corporate actions, and disclosures come from regulators, company websites, and exchange portals (apps or websites that compile financial information). Providers parse and standardize these documents, often with automation or machine learning, so that metrics align cleanly across countries, sectors, and reporting formats.

• Transactional data: With user permission, providers connect to banks and financial institutions through secure application programming interfaces (APIs) or open banking systems to retrieve balances and transactions. Others license anonymized, aggregated card spending or retail flow data to identify broader customer trends.

Normalization and quality control

Once it’s collected, data gets scrubbed, mapped, and reconciled so that identifiers, time stamps, currencies, and definitions match across sources. This step resolves the messy realities of raw inputs, such as sticker changes, corporate restructurings, and inconsistent field names.

Data delivery

After gathering, organizing, and cleaning up your financial data, providers offer access to it in a few different ways:

• Real-time feeds: For time-sensitive use cases, providers let you stream data over dedicated feeds or WebSockets (a type of network protocol that enables continuous, bidirectional communication between servers and clients with low latency) that push updates as soon as they’re available. This keeps trading systems, dashboards, and automated models tied to the latest market conditions.

• APIs and cloud integrations: Since many workflows need on-demand access, providers expose the architectural style Representational State Transfer (REST), the query language Graph Query Language (GraphQL), or streaming APIs for specific queries. They also often deliver data directly into cloud warehouses, which makes it easier for teams to analyze financial data alongside their internal datasets.

• Bulk file delivery: Historical datasets, reference files, and end-of-day reports are often delivered as downloadable files or via managed storage. This supports large-scale analytics and long-horizon modeling without overloading real-time systems.

How do organizations evaluate data accuracy, coverage, and latency?

Organizations compare a provider’s outputs against authoritative sources, such as exchange records, original filings, and audited historical data, to confirm that values match and aren’t missing or mismatched. They also look for signs of strong internal quality controls, such as automated validation, clear correction workflows, and published error rate metrics.

Teams can map their required instruments, companies, geographies, and session histories against the provider’s catalog. They should check for gaps, outdated records, or missing fields, and assess whether the dataset’s breadth and depth are sufficient for their models, reporting needs, or product features.

Organizations can also measure how quickly data arrives relative to a known time stamp, such as an exchange’s official time or the moment a filing reaches a regulator’s system. They evaluate how performance behaves under load, how often updates refresh, and whether the provider offers different latency tiers.

Beyond the headline metrics, teams should consider monitoring reliability during trials. Watch for dropped updates, inconsistent formats, or downtime. Look for providers with transparent status pages, defined service-level agreements (SLAs), and visibility into their operations.

What challenges typically arise when working with financial data providers?

Problems can occur even with the best financial data providers. Be mindful of these potential challenges.

Integration issues

Each provider might structure data differently so teams often spend meaningful time mapping schemata, handling rate limits, and building pipelines to absorb high-volume updates. Switching providers later can be even harder because your internal systems develop around a specific data model.

Data quality surprises

Even reliable providers might occasionally deliver missing fields, duplicate records, or values that need reconciliation after corporate actions. Teams often build their own validation checks, such as alerts on unusual peaks, stale time stamps, or gaps in coverage, to catch problems early.

Reliability issues

Outages or delayed updates can affect trading systems, credit models, and user-facing products. Teams often avoid these by caching, using fallbacks, or relying on secondary data sources, but unexpected downtime still creates risk.

Rising costs

Fees often scale with usage, and sometimes all at once. Real-time market data, large historical datasets, and high API call volumes can push organizations into cost tiers they didn’t anticipate, which makes ongoing monitoring necessary.

Licensing constraints

Many datasets come with rules that control how data can be displayed, shared, or stored. Restrictions regarding redistribution, user type, or internal vs. external use can introduce compliance overhead and limit product design choices.

Versioning and change management

Providers update APIs, deprecate fields, and revise historical data. Without proactive monitoring and periodic revalidation, those changes have the potential to break downstream processes.

How can businesses compare providers based on cost, licensing models, and technical requirements?

Once you know which datasets you need, the real question becomes which provider can deliver them with the right balance of price, flexibility, and technical fit. Here’s what to look for.

Pricing structure

Providers might charge per API call, per user, per dataset, or via fixed subscription tiers. Estimate your likely usage (e.g., calls, volume, user seats, latency requirements) to predict current cost and how pricing will behave as the business scales.

Transparency and predictability

Some vendors publish clear pricing and usage thresholds, while others rely on bespoke quotes. Some businesses might prefer models that allow them to forecast spend accurately and avoid surprise overage charges or midyear fee adjustments.

Licensing terms

Rules regarding internal use, external display, data redistribution, and derivative works can affect both compliance overhead and product design. You should scrutinize these terms to ensure they match current workflows and future plans, especially if data will appear in customer-facing experiences.

Integration model

Providers differ widely in how easy they are to work with. Strong developer documentation, modern APIs, software development kits (SDKs), cloud delivery options, and sandbox environments speed up the integration process and minimize ongoing maintenance.

Performance and reliability

SLAs, uptime history, available latency tiers, and monitoring transparency all factor into the decision. You want a provider with infrastructure that can handle volume peaks and with support teams that respond quickly when issues arise.

Security and compliance

Encryption practices, access controls, audit trails, and relevant certifications matter for any data that touches sensitive financial information. A provider should be well equipped to protect your business.

How Stripe Financial Connections can help

Stripe Financial Connections is a set of APIs that allows you to securely connect to your customers’ bank accounts and retrieve their financial data, enabling you to build innovative financial products and services.

Financial Connections can help you:

• Simplify onboarding: Offer a seamless, instant bank account verification process that does not require manual identity and account verification.

• Access rich financial data: Retrieve comprehensive information about your customers’ bank accounts, including balances, transactions, and account details.

• Automate recurring payments: Enable your customers to securely link their bank accounts for recurring payments, improving payment success rates.

• Enhance risk management: Analyze customers’ financial data to make more informed decisions about credit, lending, and other financial products.

• Comply with regulations: Financial Connections helps you meet Know Your Customer (KYC) and Anti-Money Laundering (AML) requirements.

• Innovate with confidence: Build new financial products and services on top of the secure, reliable Financial Connections infrastructure.

Learn more about Financial Connections, or get started today.