Need financial data, but not sure where to start?

We get it.

“Financial data” can mean very different things, from real-time market feeds and company financials to estimates, filings, private-company coverage, and alternative data signals.

And with so many providers claiming depth, accuracy, and speed, it’s easy to lose time comparing options before you even know what matters for your use case.

In this guide, we break down seven financial data providers you need to know about.

For each one, you will get a clear snapshot of what it is best for, key features, pros and cons, and what to expect in terms of pricing. 

Let’s get started.

Veridion

Veridion is a modern data-as-a-service (DaaS) provider that uses AI and ML to enrich and verify company data, including financial and company-size signals.

It is built for teams that need clarity at the entity and location level, not just a high-level company record.

Traditional financial data providers are usually strongest on markets, filings, and standardised statements.

Veridion complements them by adding granular context you can use in underwriting, supplier risk, market intelligence, and investment analysis.

Alongside firmographics and operational footprint, Veridion includes attributes such as employee count and revenues (including extracted or modelled values), plus revenue type.

If you are, say, validating a supplier, you can enrich the record with size/financial indicators, then cross-check them against the entity structure and location footprint.

Source: Veridion

This helps you catch mismatches early, like a “small” supplier that operates across many sites, or a group structure that changes the real exposure.

Here’s a quick summary of everything you need to know about Veridion.

Best for	Enrichment and verification for underwriting, supplier risk, and market intelligence workflows
Financial signals	Company size and financial attributes such as employee count, revenue values, and revenue type
Differentiator	Entity- and location-level context that complements market-data-first providers
Pricing	Quote-based (depends on coverage, attributes, and delivery method)

Overall, use Veridion when you need verified company-level context that makes financial signals more decision-ready.

Next, we will look at providers that focus primarily on market and securities data.

Bloomberg

Bloomberg is a financial data platform built for teams that rely on real-time markets, company financials, news, analytics, and economic indicators in one workflow.

It is best known for the Bloomberg Terminal.

For enterprise teams that want the data inside their own systems, Bloomberg also offers data products and licensing through its Enterprise Data business.

Bloomberg often positions its scale in two headline numbers: 8,000+ enterprise datasets and 100 billion+ data points published daily via Data License (consistent with Terminal data).

Source: Bloomberg

If your team needs to track market moves and explain them fast, Bloomberg lets you pull live pricing, layer in news, and run analytics in the same environment. 

Here’s what else you need to know:

Best for	Real-time market workflows and research driven by live pricing + news + analytics
Data scale	8,000+ enterprise datasets; 100 billion+ data points published daily via Data License
Key products	Bloomberg Terminal; Enterprise Data / Data License
Pricing	Not publicly listed. Industry sources often report Terminal pricing at around $32,000 per year (varies by contract and setup).

This is a different job than what Veridion does, which is focused on verifying and enriching company-level records across entities and locations.

Next, we will look at another provider that is widely used for research and analytics, but with a different product setup and coverage focus.

FactSet

FactSet is a financial data and analytics platform used for equity research, portfolio analysis, and due diligence workflows.

It brings together market and company datasets with analytics so teams can research securities, monitor portfolios, and run investment analysis in one environment.

Source: FactSet

Compared with Bloomberg, which is often Terminal-first and news-led, FactSet is commonly positioned around research and portfolio workflows, including exposure, risk, benchmarking, and performance analysis.

FactSet’s coverage includes building blocks that research teams use frequently, such as fundamentals, like financial statement data, and ownership datasets, like institutional and mutual fund ownership.

If you are doing investment screening or due diligence, you can pull company fundamentals, check ownership context, and then evaluate portfolio exposure and performance metrics.

Source: FactSet

All of this can be done in the same workflow using FactSet’s analytics capabilities.

Here’s a breakdown of all key facts about FactSet:

Best for	Equity research, portfolio analysis, and due diligence workflows
Key features	Fundamentals data; ownership data; portfolio analytics
Pros	Strong for research + portfolio workflows where analytics and attribution matter
Cons	Not positioned as a “news-first, real-time terminal” experience in the same way Bloomberg Terminal is
Pricing	Tailored pricing; available on request

Next, we will look at another provider with broad financial datasets, but with different strengths in credit risk, ratings, and market intelligence.

S&P Global

S&P Global is a data and analytics provider known for credit ratings, market intelligence, company financials, and risk analytics.

This platform is frequently chosen for underwriting and credit workflows because it provides both the necessary data and the analysis required for confident risk assessment and decision-making.

In its Data & Analytics offering, S&P Global highlights “Essential Intelligence”, which combines sector and market data with news and analytics. 

It also offers productised platforms like S&P Capital IQ Pro for deeper market and company intelligence, and a Marketplace for fundamental and alternative datasets delivered via cloud, data feeds, and APIs.

Here is the dashboard of S&P Capital IQ Pro.

Source: S&P Global

If you are assessing credit risk or portfolio exposure, S&P Global can support the workflow end-to-end, from research and company intelligence to broader datasets that help explain risk drivers across markets and sectors. 

This sits closer to risk and ratings use cases than Veridion’s entity-and-location enrichment focus, and is less terminal-first than Bloomberg.

Best for	Underwriting, credit assessment, portfolio risk management
Key products	Data & Analytics, S&P Capital IQ Pro, Marketplace
Key features	Sector + market data with news and analytics; proprietary datasets; tools and feeds for enterprise delivery
Pros	Strong for risk-led workflows that need data plus analysis
Cons	Can be broader and more multi-product than single-purpose datasets
Pricing	Available on request (“Request a Follow-up”)

Next, we will look at a provider that is more specialized in company identity, credit files, and risk signals at the business level.

Dun & Bradstreet

Dun & Bradstreet (D&B) is a business data and insights provider used for company financials, credit risk assessment, firmographics, and third-party risk screening.

It is commonly used in supplier risk assessment and commercial underwriting workflows.

D&B’s core foundation is the Dun & Bradstreet Data Cloud, which it describes as data intelligence on 600M+ organizations across 250+ markets (plus consumer data in many offerings).

A key identifier in D&B ecosystems is the D-U-N-S Number, a 9-digit unique business identifier tied to D&B’s identity resolution and “Live Business Identity”.

This is often used in onboarding and record matching when you need consistent entity identification.

Here is what a D-U-N-S Number certificate looks like in practice.

Source: Dun & Bradstreet

If you are onboarding a new supplier, you can use the D-U-N-S Number to standardise the record, then layer on D&B’s credit/risk indicators to support supplier risk decisions.

This is a different job than Bloomberg or FactSet (market and securities workflows) and a different emphasis than Veridion (entity- and location-level enrichment).

Best for	Supplier risk assessment, commercial underwriting, business identity verification
Key features	D&B Data Cloud; D-U-N-S Number (9-digit identifier); risk and credit decisioning signals
Data scale	600M+ organizations; 250+ markets
Pricing	Available on request (contact/demo flows)

All in all, if your priority is credit and third-party risk decisioning tied to business identity, D&B is worth looking into.

Bright Data

Bright Data provides large-scale web data collection and ready-to-use datasets for teams that want continuously updated public web data for market intelligence, competitor monitoring, and alternative data workflows.

Instead of a terminal-style experience (Bloomberg) or a research-first financial platform (FactSet), Bright Data is positioned as a web data infrastructure.

You can buy pre-built datasets or collect data on demand using its scraping APIs and delivery options.

On the financial dataset page, Bright Data highlights access to public financial data for use cases like portfolio management and predictive analytics.

The page also shows an entry price point of $250 per 100,000 records.

Source: Bright Data

If you need an alternative data feed that updates frequently, Bright Data can deliver structured datasets and enable ongoing collection.

Here’s some additional information about Bright Data:

Best for	Web-sourced alternative data, market intelligence, competitor monitoring
Key features	Dataset marketplace + web data collection APIs; structured delivery options
Pros	Built for scale and continuous updates from public web sources
Cons	Data collection requires careful scoping and QA to match your exact fields and freshness needs
Pricing	The financial dataset page shows $250/100,000 records

Bright Data is worth considering when “fresh public web data” is the core requirement, not traditional licensed market data.

Daloopa

Daloopa is an AI financial copilot designed to extract and standardise fundamental data from filings and disclosures for analyst workflows.

It is built to fit directly into Excel, where most models live.

The focus here is different from Bloomberg or FactSet. 

Those platforms are built around markets, research, and broad financial datasets.

Daloopa, on the other hand, is built for the “document to model” step, where accuracy and traceability matter more than live pricing.

Daloopa highlights auditability as a core feature. It states that every datapoint is hyperlinked back to filings and transcripts, so analysts can verify numbers quickly.

It also publishes an open benchmark on financial retrieval.

In that test, a grounded setup (Claude + Daloopa MCP) achieved 94.2% exact-match accuracy on single-number prompts from official documents.

Source: Daloopa

After earnings, a team can update models faster by pulling newly disclosed metrics from source documents and keeping the links for audit and review.

This is also where Daloopa differs from tools like Veridion.

Veridion is not a filings-extraction layer.

It is a company data enrichment and verification layer, useful when your question is about entity identity, locations, and operating footprint, not only what a filing says.

Best for	Filing-driven fundamental data extraction and model updates
Key features	Excel integration; source hyperlinks; MCP connector benchmarked for grounded retrieval
Pros	Auditability by design through source-linked datapoints
Cons	Not a real-time market terminal or broad “company profiling” dataset
Pricing	Plans are listed (including a Free plan), with details gated behind “Get Started” / demo flows

All in all, Daloopa’s financial data is ideal for finance professionals like equity analysts, portfolio managers, investment bankers, and data scientists in hedge funds, private equity, and more.

It helps you quickly access, process, and analyze thorough, AI-structured historical financial statements, KPIs, and other data for significantly faster modeling and decision-making. 

Conclusion 

Financial data powers so many different decisions, so no single provider will fit absolutely everyone.

Besides, the strongest workflows stack layers: market context for the why, risk signals for the so-what, and clean company-level truth for the final call. 

That last layer is where teams usually lose time, because entities do not match, coverage is patchy, and the data is already stale by the time it hits a spreadsheet. 

So, before making any final decisions, run short pilots with real questions.

When the data stays accurate, current, and consistent across systems, decisions move faster, and they are much easier to defend.