Industry overview
Alternative Data for Financial Services
Markets price in obvious information within seconds. The edge lives in signals that are real, measurable, and not yet in the consensus.
Standard input, not exotic
Alternative data is no longer exotic. Sales proxies, hiring signals, app-store rankings, regulatory filings, pricing snapshots, and review-velocity data are now standard inputs in systematic, fundamental, and credit workflows.
Custom universe, every mandate
Most off-the-shelf vendors sell pre-packaged datasets on fixed universes with fixed refresh cycles. Research finds a promising signal and is told the extension would take quarters.
Speed defines the edge
This is how research teams operationalize the open web. Every mandate is scoped to the specific signal, universe, and frequency the strategy needs. The extraction infrastructure is not custom. The research question is.
Built for firms like these
On a recent earnings-preview mandate, custom extraction of a retailer's pricing and promotional activity across 200+ SKUs delivered a measurable deviation from consensus same-store sales estimates two weeks before the print. Positioning established against the signal realized the full spread at reporting. That is what alt-data looks like when it is operational on research timelines.
Use cases
Data extraction use cases
Every function in a financial services company benefits from knowing what competitors are doing. From pricing teams to category managers to operations leads, here are the ways competitive data drives decisions.
Earnings-preview and fundamentals signals
Pull what a public company's products cost, how much is in stock, and what is discounted across all stores and listings. Estimate quarterly sales before the company reports. Same-store, velocity, and mix proxies that track ahead of reported fundamentals.
Thematic and sector research
Deliver structured data across a full group of companies in a theme. 800 consumer-tech tickers, the entire investable universe, not just five bellwethers. The full universe, not a hand-picked sample. Foundation for sector-momentum factors and screens.
Credit early-warning signals
Track public signs of a company in trouble. Hiring slowdowns, shrinking product range, vendor-complaint velocity, fewer job postings. Stress detected at the listing and hiring level, months before it shows up in financial filings.
Private-market and diligence signals
When a PE or VC firm is evaluating a deal, pull live market data to verify the target's claims. Is the company actually growing, are customers actually happy, is pricing actually holding. Management decks meet the live market.
Hiring, headcount and org signals
Job postings, team sizes, executive movements from company sites and professional networks. Which team is growing fast, who just left, where a company is opening offices. Tagged by role, geography, and time.
Launch, expansion and competitive-move detection
Watch competitor websites, app stores, and marketplaces for new products, geographies, and categories. Alert research teams before the launch hits the news. Detection ahead of the press wire.
Pricing power and promotional intensity
Track whether a company's price increases actually stuck at the shelf, how deep its competitors' discounts run, and how pricing moves across a whole category over time. Pricing power as a measured signal, not a management claim.
Supply-chain and input-cost signals
Wholesaler prices, shipping rates, and B2B availability data from B2B wholesale platforms. The inputs that drive company costs but rarely appear in financial statements. Cost-side signal that gross-margin models miss.
Review velocity, sentiment and demand proxies
How fast products are getting reviewed, whether ratings are trending up or down, what customers are actually saying. Real-time read on demand and brand health. Structured data, not anecdotal pulse.
Regulatory, litigation and tender signals
Regulatory filings, court cases, tender wins, ESG controversies. The public record of everything that can materially affect a company's risk profile. Public records, structured and at scale.
Competitor landscape and market mapping
Build structured lists of every company in a given market. Who they are, what they sell, how big they are, how they compete. Full maps that match the depth research teams actually need, not handful-of-names samples.
Custom per-mandate pipelines
Every research question is different. Scope a fresh extraction for each mandate. The exact signal, the exact companies, the exact refresh rate. Shut it down when the thesis ends. Pay only for what the mandate used.
These are the most common use cases. Every engagement is scoped to your specific needs. If you have a use case not listed here, we will build it.
Data landscape
The data we extract
Every financial services mandate is custom. Here are the standard alt-data categories we deliver across research teams, each scoped to the specific signal, universe, and frequency your strategy requires.
This is a representative sample of the data we extract. We customize every extraction to your exact requirements. If you need a data point not listed here, we will add it to your pipeline.
Delivery formats
You tell us how you want the data. We handle everything else.
Impact
Why competitive data matters
The difference between having competitive intelligence and operating without it is measurable in revenue, market share, and speed.
With competitive intelligence
What you gain
Without it
What you risk
Challenges
Why financial services data extraction is hard
If extraction were easy, you would do it yourself. Here is why it is not.
Alpha-window timelines
Alt-data edges have compression half-lives measured in weeks. Data vendors that take quarters to scope and deliver are incompatible with how research teams actually generate and test ideas. Supporting financial services workflows requires extraction infrastructure that scopes, builds, and delivers pilots in days.
Universe-specific requirements
Every strategy has a different universe. The consumer discretionary long book, the semiconductor basket, the India small-cap sleeve. Packaged products built on generic universes cannot serve strategies whose edge depends on specific-ticker coverage. Supporting this requires custom-scoped extraction per mandate.
Signal quality for backtests
Alt-data that will feed production models needs to be point-in-time clean, consistently structured across periods, and documented for methodology. Most scraping-vendor output does not meet these standards. Supporting financial workflows requires structured metadata, point-in-time preservation, and methodology documentation.
Anti-bot defenses on target sources
Sources that matter (e-commerce platforms, job boards, wholesaler platforms, review sites) all invest heavily in bot detection. Sustained extraction requires continuous adaptation. Supporting financial timelines means never telling a research team that data is temporarily unavailable because a platform updated its defenses.
Confidentiality and MNPI
Financial services firms operate under strict confidentiality and material-non-public-information rules. Data vendors must operate without disclosing customer identities, strategy details, or even which tickers are being researched. Most public logo-wall marketing is disqualifying.
Latency expectations
Systematic strategies need 15-minute to hourly refresh frequencies. Fundamental research needs daily. Credit analytics can often work weekly. Supporting all three requires infrastructure flexible enough to run multiple frequency tiers against different mandates simultaneously.
Global coverage
A sector strategy may need data across the US, Europe, India, Southeast Asia, and Latin America. Supporting this requires globally distributed extraction infrastructure with language handling and currency normalization, not US-only coverage plus afterthought international.
Why us
Why Clymin for financial services
We are not a tool. We are the team you call when the data matters too much to get wrong.
We solve what others can't
Alt-data for financial services needs speed, customization, signal quality, and coverage breadth that packaged products cannot match. We operate custom extraction for the specific signal, universe, and frequency each strategy requires. When other vendors quote quarter-scale integrations, that is where we start.
You pay only for data delivered
No setup fees, no customization charges, no platform fees. One metric: cost per record. If we do not deliver, you do not pay. Your cost scales with your actual signal consumption, nothing else.
We protect your identity
We do not display customer logos or names anywhere. Financial confidentiality is absolute. Your firm, your strategies, and the signals you commission are never disclosed to anyone. That is a promise, not a policy.
We prove it before you pay
No pitch deck replaces real data. We offer a free pilot: your thesis, your sources, your universe, your frequency. We build the extraction pipeline, run it for 1-3 days, and deliver structured samples. You evaluate signal quality and decide.
100B+
Data points extracted
24/7
Pipeline uptime
Real-time
Data delivery
100K+
Points of interest covered
Proven at enterprise scale. We operate continuous competitive intelligence infrastructure for one of the world's largest quick commerce platforms.
See what alt-data looks like on research timelines
Free pilot. 1-3 day turnaround. Your thesis, your universe, our execution.
FAQ
Financial Services data extraction FAQ
We start with a 30-minute scoping call. You describe the thesis, the universe, the signal, the frequency, and the delivery format. We scope the extraction, confirm feasibility, and begin pilot delivery within 1-3 days. No multi-quarter integrations, no data catalog browsing.
Yes. Alpha-window delivery is why research teams use us. Pilots arrive in 1-3 days. Production feeds run at the frequency the strategy requires. Signals refresh as long as the mandate is active. When a thesis gets retired, the pipeline shuts down. No contracts that outlast the thesis.
Yes. Every mandate operates under NDA. Customer names, strategy details, and signal specifications are never disclosed to anyone, internally or externally. We do not display customer logos anywhere.
We cover every major sector and geography. Consumer discretionary, consumer staples, technology, healthcare, industrials, financials, real estate, energy. Across US, Europe, India, Southeast Asia, Latin America, and the Middle East. If your strategy needs data from a digital source, we likely cover it or can build the pipeline as part of your pilot.
We deliver in CSV, JSON, Parquet, via API, or directly into your warehouse. Formats match how research and production teams actually consume data. Point-in-time preservation is standard.
Yes. Every signal ships with methodology documentation covering source, extraction approach, refresh cadence, deduplication rules, and known limitations. Research teams need methodology they can defend to risk and compliance. We deliver that as standard.
You share the research question, sources, and universe. We build the extraction pipeline, run it for 1-3 days, and deliver structured sample data in your preferred format with point-in-time preservation. You evaluate signal quality and decide whether to scale. No payment, no commitment.
We charge per record delivered. One record is one structured row of data with the columns you define. Zero setup fees. Zero customization charges. Zero platform fees. You pay only for data we successfully deliver, scoped to the mandate. When the mandate closes, billing stops.