Fraud Detection & Transaction Risk
S

Spade

Spade turns the indecipherable strings that banks and fintechs receive from card, ACH and wire transactions into verified merchant records, matching raw data against a proprietary ground truth database so an institution knows exactly where and with whom each transaction occurred. AI agents continuously scan the web and external sources to fill metadata gaps and remove duplicates, producing precise geolocation and verified merchant categories independent of the legacy category codes the industry has relied on. The company publishes 99.9 percent coverage of United States and Canadian merchants at over 99 percent accuracy, with tail latency under 40 milliseconds. Customers use the enriched data for authorization decisioning, fraud prevention, rewards attribution, analytics, behavioral segmentation and loan targeting.

Last VerifiedAugust 15, 2026
Compare Spade with other vendors
Founded
2021
Headquarters
New York, New York, United States
Website
spade.com
Categories
fraud-and-transaction-risk, customer-banking-agents, lending-and-banking-operations
Assessment

Capability Axes

Capability grades

15 of 15 axes rated · 5 graded A or B

AI Capability
AI Centrality
AA on AI CentralityThe artificial intelligence is the product. Remove the models and there is nothing left to sell.
Vendor Published

Take the AI away and what remains is merchant category codes, precisely the legacy standard the product exists to replace. The company markets against those codes by delivering verified categories independent of them. A proprietary matching engine is driven by agents that continuously scan the web and external sources to fill metadata gaps and eliminate duplicate records. The company describes the result as self reinforcing, growing more capable with every transaction processed. Resolving an arbitrary payment string to a specific business with precise geolocation is inference, not lookup.

Autonomy and Oversight Model
CC on Autonomy and Oversight ModelAutonomy is claimed and oversight is asserted without a mechanism, or full automation is presented as the entire disclosure. Human in the loop appears as a phrase rather than a described control.
Vendor Published

The data feeds automated decisions by design. Authorisation decisioning is the first named workflow and the sub 40 millisecond tail latency exists precisely so enrichment can sit inside a live authorisation path, where no human reviews anything, and the chief executive frames the ambition as banks moving toward fully automated agentic workflows built on this data.

Nothing describes what happens when a match is uncertain: no confidence score is mentioned, no fallback behaviour is defined for an unresolved merchant, and no guidance is published on how a customer should treat a low confidence enrichment inside a decline decision.

Model Risk Management and Transparency
AA on Model Risk Management and TransparencyExplainability and validation are built into the product and mapped to the supervisory instrument they serve: per alert attribution, backtesting or test before deploy, with a stated alignment to a framework like SR 11-7, OCC 2011-12 or NYDFS Part 504.
Vendor Published

Three performance figures are published, and one of them is chosen in a way that reveals seriousness. Coverage is stated at 99.9 percent of United States and Canadian merchants with matching accuracy above 99 percent, both scoped to a defined universe rather than asserted generally. Latency is published as a 99th percentile figure under 40 milliseconds rather than as an average.

That is the honest measure, because it describes the worst case customers actually experience inside an authorization path rather than a flattering mean. For a data layer whose entire value is correctness and speed, publishing both scoped and tail metrics is the right disclosure. No confusion matrix or error breakdown accompanies it.

Operational and Outcome Evidence
AA on Operational and Outcome EvidenceNamed customers with hard performance figures and enough method to test them.
Vendor Published

Five customers are named, and they are substantial. They are a global payments company, a corporate payments group, a business banking provider, a banking as a service platform and a rewards focused card program, described as using the data to authorize more transactions, prevent fraud and build new features. A $40 million Series B closed in March 2026 with a specialist fintech growth investor.

The performance figures are stated precisely, at 99.9 percent coverage of United States and Canadian merchants, over 99 percent matching accuracy, and tail latency under 40 milliseconds. Founded in 2021, the company describes customers arriving for authorization decisioning, fraud prevention, rewards attribution and analytics. It notes these workflows were historically considered too critical to entrust to something resembling data cleaning.

AI Safety and Data Stewardship
CC on AI Safety and Data StewardshipGeneral assurances that do not answer the question this axis asks, which is whether one customer’s data trains models serving its competitors. Unbounded cross client learning stated with no boundary grades here too.
Vendor Published

The flywheel is disclosed as the central advantage, and it is exactly the stewardship concern. The system is described as self reinforcing, becoming more intelligent with every transaction processed, which means each customer's volume improves the database delivered to all the others.

Those others include direct and adjacent competitors, since the named customer list spans a global payments company, a business bank, a banking as a service provider and a corporate payments group operating in overlapping markets. The company states this openly as a strength. Nothing describes what is contributed, whether a customer can decline, or what happens to learning derived from a departing customer's traffic.

Regulatory and Compliance
GLBA and Data Privacy Posture
CC on GLBA and Data Privacy PostureA standard privacy policy that covers the website rather than the service, or silence on a product that touches limited consumer data.
Vendor Published

The payload is a complete record of where individuals spend their money, and no data protection agreement, retention schedule, subprocessor list or deletion commitment is published. Enrichment establishes exactly which business a person transacted with and where, at precise geolocation. The company describes the objective as detailed, verified and structured consumer behavior.

Downstream uses named by the company include behavioral segmentation and loan targeting, so purchase histories shape what individuals are offered. Nothing published states what is retained, whether enriched consumer transactions persist beyond the query, or what a person is told.

Security Certifications and Trust Center
CC on Security Certifications and Trust CenterA single footer line, or certifications asserted without being enumerated, which is weaker than naming them because it invites an assumption a buyer cannot check.
Vendor Published

There is no public attestation, certification, trust center or enumerated framework. A global payments company and several banking platforms have completed vendor assessment and placed this service inside their authorization flows, which is diligence at a demanding standard. For a vendor handling consumer transaction data in real time at that position in the stack, a published control set is what every subsequent enterprise buyer will request.

Regulatory Status and Licensure
CC on Regulatory Status and LicensureThe regulatory position is unstated. Most vendors in this index are technology suppliers and being unlicensed is the correct posture, so this grade records silence about the posture, not a missing license.
Vendor Published

No supervisor, statute or instrument is named. Two named use cases sit close to regulated territory: behavioural segmentation and loan targeting derived from a consumer's transaction history engage rules on the use and sharing of financial information, and where such data informs credit offers the consumer reporting framework becomes relevant. Nothing published addresses either, nor any payment network or data handling standard despite the product sitting inside card authorisation flows.

AI Governance and Bias Disclosure
CC on AI Governance and Bias DisclosureResponsible artificial intelligence committed to in policy language with no evaluation behind it, on a product whose bias surface is modest.
Vendor Published

Two exposures matter. The first follows from the stated use cases. Enriched purchase history drives loan targeting and behavioral segmentation, so what a person buys shapes what credit and offers they are shown, through an enrichment layer they never see and cannot correct. The second is a coverage inversion. At 99.9 percent merchant coverage, a residual tenth of a percent goes unresolved, and unresolved merchants will disproportionately be small, new, informal or rural businesses.

The customers of exactly those merchants are the ones whose transactions look unrecognizable to a fraud engine and are likeliest to be wrongly declined. No breakdown of coverage or accuracy by merchant size or type is published.

AI Liability and Recourse
CC on AI Liability and RecourseMechanisms that enable challenge, such as audit trails and source traceability, with nothing standing behind the output and no route for the person affected.
Vendor Published

Beyond the published accuracy figures there is no guarantee, indemnity or falsifiable commitment, and those figures are marketing claims rather than warranted terms. The institutional customer can measure performance against its own dispute volumes, which the company identifies as the problem poor enrichment causes. The consumer has no route at all. A misattributed transaction shows the wrong business on a statement, which is what generates the dispute in the first place. Nothing describes how a wrong merchant match is corrected in the database once identified.

Integration and Deployment
Model Supply Chain Disclosure
CC on Model Supply Chain DisclosureThe architecture is described and no provider is named.
Vendor Published

Sources are described only as the web and external data sources scanned continuously by agents to fill metadata gaps, with the resulting database described as proprietary and ground truth. That leaves the provenance of the merchant data unstated. It matters because the accuracy claim rests entirely on what the underlying references contain and how they are licensed. No model provider is named for the matching or agent components, and no subprocessor list or hosting arrangement is published.

Core Systems and Integration Depth
BB on Core Systems and Integration DepthNamed systems or a documented public API, with the depth or the production evidence left open.
Vendor Published

Delivery is through interface infrastructure engineered for the most demanding environments. The evidence of integration depth is the customer list rather than a connector catalog, since a global payments company and a banking as a service platform have embedded this inside their own transaction paths. Coverage across card, automated clearing house and wire rails means one integration serves all three. No named platform, processor or core system integration is published, and there is no public developer documentation, which interface first products usually provide.

Deployment Model and Data Residency
CC on Deployment Model and Data ResidencyCloud only with nothing stated, which is the category norm.
Vendor Published

Where the platform runs is not published, with no hosting provider, region selection, residency commitment or private deployment option named. The North American footprint simplifies exposure. The platform nonetheless processes consumer transaction records inside live authorization paths for regulated institutions, whose own examiners would expect the processing arrangement to be documented.

Commercial
Commercial Transparency
CC on Commercial TransparencyNo price is published and engagement runs through a demo form, which is the norm in this index.
Vendor Published

No pricing, packaging or basis of charge is published. The company is explicitly moving from an interface priced on enrichment volume toward a platform supporting workflows. A commercial shift like that would ordinarily change how customers are charged, and nothing describes either the old model or the new one. Whether charge falls per transaction enriched, per workflow or as a platform fee is undescribed.

Institution and Segment Coverage
BB on Institution and Segment CoverageNamed segments with dedicated material behind part of the coverage.
Vendor Published

Buyer types span banks, fintechs, payment companies, banking as a service platforms, corporate payments providers and card programmes, which is broad within the payments ecosystem, and rail coverage is complete across card programmes, automated clearing house and wire transfers rather than cards alone. Use cases now extend well beyond enrichment into authorisation decisioning, fraud flagging, rewards attribution, notification triggers, behavioural segmentation and loan targeting. The clear limit is geographic and stated plainly: coverage is United States and Canadian merchants, so this is a North American product.

Tracked Since Listing

Product updates

Material product, regulatory, evidence and commercial changes at Spade, each verified against a live source and tagged to the capability axis it bears on. Funding rounds and awards are not product changes and are not logged.

Oct 7, 2026Integration / interoperabilityPartially verified

Spade's merchant enrichment is now built into Increase's card issuing platform, available to card programs on Increase today. Each card transaction carries a Spade record with merchant name, legal name, logo, website, phone, store location and an industry category up to four levels deep, matched in real time against Spade's Merchant Graph, which links locations to brand and owner, identifies the merchant behind processors such as Square and flags delivery apps and marketplaces. The data is available during the authorization decision and is optional by design, so an authorization never depends on it.

Bears on: Core Systems and Integration DepthSource
Our read on this update →Tracked since Oct 2026

Alternatives to Spade

Vendors in the same categories as Spade whose profiles look most alike, each with a line on what the product does. Similar is not the same: check that each one does the job you need. No vendor pays for placement.

Accertify is an enterprise fraud prevention and payments company based in Itasca, Illinois, and led by president Mark Michelon.

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ClearSale is an ecommerce fraud prevention business founded in Brazil around 2001.

The index publishes no overall score or ranking. See what we assess and the methodology.

Commercial

Pricing

Vendor-published figures are labeled as such. Figures labeled “Estimated” are derived from third-party sources and have not been confirmed by the vendor.

No pricing data has been verified for this vendor. Pricing information will be published here once confirmed through vendor disclosure or third-party estimation.

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AI FinTech Index

The AI FinTech Index is an independent index that tracks changes to AI vendors in financial services. It holds 610 vendors across banking, lending, insurance, wealth, capital markets and financial crime compliance, each graded on the same 15 capability axes from public sources. No vendor pays for inclusion, placement, or rating.

Index Status
Last index update
October 20, 2026
The AI FinTech Index is an editorial reference, not a regulatory body. Vendor data is verified against published sources and public regulatory filings. Figures labeled “Estimated” have not been confirmed by the vendor. See the Methodology page for evaluation standards and limitations.
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