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 — competitor e-commerce sales proxies, job posting velocity, app-store rank trajectories, pricing moves ahead of earnings, supply chain data scraped from wholesaler platforms.
Hourly competition
Alternative data is no longer exotic. Sales proxies from e-commerce platforms, hiring signals from job boards, app-store rankings, regulatory filings, pricing snapshots from competitor sites, and review-velocity data are now standard inputs in systematic strategies, fundamental research, and credit analytics.
Operational necessity
Most off-the-shelf alt-data vendors sell pre-packaged datasets on fixed universes with fixed refresh cycles. Research teams find a promising signal and are told the extension — a specific geography, a competitor not in the universe, a frequency not in the product — would take quarters to add.
Every platform, every city
This is how quantitative, fundamental, and credit 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.
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 instead of stuck in vendor procurement.
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 signals
Custom extraction of pricing, promotional depth, and availability signals for covered tickers in the weeks leading to earnings. Build structured same-store, velocity, and mix proxies that track ahead of reported fundamentals.
Fundamental research
Deliver structured competitor, pricing, assortment, and launch data for fundamental research teams working on long-horizon theses. Replace expert-network anecdotes with structured data measured across categories and geographies.
Credit analytics
Alternative data for credit analysts covering consumer and corporate credits. Monitor pricing pressure, review-velocity deterioration, assortment contraction, and hiring slowdowns as early warning signals before they appear in quarterly financials.
Private market due diligence
Structured web data for private equity, growth equity, and venture due diligence. Validate management claims on market share, pricing power, and growth trajectory with live market data that internal decks cannot invalidate.
Thematic and sector research
Feed sector strategy work with structured data across thematic baskets — e-commerce, quick commerce, travel, fintech, healthcare, semiconductors. Track sector-level signals across the universe of tickers that matter, not just a handful of bellwethers.
Pricing power monitoring
Track competitive pricing across categories to measure actual pricing power for covered companies. Measure whether list-price increases stick at the shelf, how competitors respond, and which categories preserve or compress margin.
Supply chain and inventory signals
Extract wholesaler pricing, B2B availability, shipping proxies, and inventory signals from platforms like Alibaba, Made-in-China, and IndiaMART. Feed systematic and fundamental models with supply chain inputs that rarely surface in financial statements.
Launch and category expansion tracking
Monitor new product launches, category expansions, and geographic rollouts across the ticker universe. Detect competitive moves before they show up in press releases or earnings commentary.
Review velocity and sentiment
Extract and structure review velocity, rating trajectories, and sentiment shifts across products and competitors. Feed structured sentiment signals into quantitative models and fundamental research frameworks.
Hiring and organizational signals
Extract job postings, hiring velocity, team structure, and organizational changes from company sites and job boards. Feed alt-data models with hiring signals that precede capacity expansion, strategic pivots, and management transitions.
Regulatory and compliance signals
Extract regulatory filings, licensing data, tender activity, and public compliance records for systematic credit monitoring, ESG workflows, and regulatory-overhang tracking.
Custom per-mandate pipelines
Every mandate has a unique thesis. We scope extraction to the specific signal, universe, geography, and frequency the strategy needs. Delivered in 1-3 days. Refreshed at the frequency the strategy requires. Shut down when the mandate ends.
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 — and every major market including 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.