AML, KYC & Financial Crime
M

Microblink

Microblink builds computer vision and biometric models for identity document capture, authentication and verification, sold to banks, lenders, fintechs and payment providers as software development kits and as a hosted platform it now calls the Identity Intelligence OS. The platform combines real time intelligence (document verification, deepfake detection, biometrics and device signals), a Command Center with decision and rules engines and fraud analytics, an identity graph, and workflow orchestration. Capture runs on the device through client side machine learning models.

Microblink states that it processes more than 4 billion identities a year across more than 195 countries and territories, and names customers including Banco Azteca, for account opening and loan applications, Latin ID, which reports a 98 percent success rate for automotive lenders, and Instacart. In the Department of Homeland Security Science and Technology Directorate's Remote Identity Validation Rally, announced March 2, 2026, Microblink reports it was the only one of seven vendors to meet every performance threshold, with a 3.15 percent document false reject rate and a 0.88 percent false accept rate.

It holds ISO 27001:2022 with an ISO 27701 privacy extension and a SOC 2 Type II examination. The company was founded in Croatia and is headquartered in New York, with research and development in Zagreb.

Last VerifiedOctober 2, 2026
Compare Microblink with other vendors
Founded
2014
Headquarters
New York, New York, United States
Website
microblink.com
Categories
aml-kyc-financial-crime, fraud-and-transaction-risk, lending-and-banking-operations
Assessment

Capability Axes

Capability grades

15 of 15 axes rated · 11 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

Remove the models and no product remains: document authentication, deepfake detection and liveness are all model driven. A decision engine and fraud analytics sit on top in what is now branded the Identity Intelligence OS.

Autonomy and Oversight Model
BB on Autonomy and Oversight ModelA written commitment that the models work alongside human judgment, with real review surfaces, short of the full control structure: commonly the threshold at which the system stops or what happens after it is wrong.
Vendor Published

Command Center gives the institution a decision engine and rules engine, with the certainty and threshold of each security check tunable by the customer. The applicant's side is missing: no manual review queue, escalation route for a borderline capture or way to reopen an automated rejection is described.

Model Risk Management and Transparency
BB on Model Risk Management and TransparencyReal transparency mechanisms are published, such as per alert explainability, confidence scoring or split testing, without the validation package or supervisory mapping behind them.
Vendor Published

The Department of Homeland Security's Remote Identity Validation Rally, announced March 2, 2026, measured document validation at a 0.00 percent system error rate, a 3.15 percent false reject rate and a 0.88 percent false accept rate against a 10 percent threshold. Model documentation, a validation summary and drift monitoring are not published.

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

Latin ID reports a 98 percent success rate on know your customer checks for automotive lenders, a named customer joined to a figure. More than 4 billion identities are processed a year.

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

Demo app data trains machine learning systems with the user's consent, per the privacy policy, but whether documents captured for one institution improve models serving another is not said. Deepfake, synthetic identity and injection attack detection are strong safety work that leaves that stewardship question open.

Regulatory and Compliance
GLBA and Data Privacy Posture
BB on GLBA and Data Privacy PostureA substantive privacy document that reaches the product itself, short of the subprocessor list or the full data handling detail.
Vendor Published

An ISO 27701:2019 privacy extension has applied to the firm as a processor of personal data since 2023, and the privacy policy sets out the processor role under data processing agreements, standard contractual clauses for transfers and data subject rights. Retention for captured documents and biometric templates, a subprocessor list and state biometric privacy law are not addressed.

Security Certifications and Trust Center
BB on Security Certifications and Trust CenterA recognized certification named in the vendor’s own material without the artifact, or with a scope or renewal question the buyer has to raise.
Vendor Published

ISO 27001:2022 certification covers the computer vision services for scanning and verifying documents across the US and Croatian offices, and a SOC 2 Type II examination against security and availability covers all identity products. Reports sit behind a nondisclosure agreement at trust.microblink.com, and neither the certification body nor the audit period is named.

Regulatory Status and Licensure
BB on Regulatory Status and LicensureThe regulatory position is clearly stated and appropriate to the product, with part of the verification left to the buyer.
Vendor Published

Customer identification, know your customer and anti money laundering duties shape the product material, which combines document, data and watchlist checks to meet them. No authorization or registration is published, which is expected of a technology vendor.

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

Accuracy measured externally is not fairness measured externally: the government rally tested documents from 23 states and Washington DC on several phone platforms and reported no demographic differentials. Its biometrics page claims face match works across global demographics and lighting conditions with no breakdown or bias testing behind it.

AI Liability and Recourse
BB on AI Liability and RecourseA published falsifiable commitment such as an accuracy figure with its method, or a real correction route for the affected person, such as step up verification instead of silent denial.
Vendor Published

Error rates measured by a government test team give a falsifiable accuracy figure with its method, and at the published false reject rate roughly one legitimate applicant in 32 is wrongly turned away. Guarantees, appeal routes and a split of responsibility with the deciding institution are all unpublished.

Integration and Deployment
Model Supply Chain Disclosure
BB on Model Supply Chain DisclosureSubstantial partial disclosure, or a chain that is structurally short: an explicit in house build, on premise deployment, per customer instances, or zero retention at the model layer.
Vendor Published

Owning and building the stack in house, from on device capture to biometric AI and fraud detection, is an explicit homepage claim, and the government evaluation partly corroborates it because the tested system was the firm's own. Hosting providers, licensed components and any model or version identifier a bank could record against a decision are not disclosed.

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

Banco Azteca runs the same component across several mobile applications for account opening and loan applications, and integration comes through interfaces, lightweight development kits and an orchestration layer with a workflow builder. Results return in under three seconds, though no core banking, origination or decisioning platform is named as a connection.

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

Capture and first processing run on the user's device through client side models, keeping raw images local until submission. Authentication, matching and decisioning run server side, and no hosting region or in country residency option is enumerated for that half.

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

A demo request or contact form is the only way in, and no rate, tier or minimum commitment is published. Free trial access to the development kits helps a developer judge fit without letting a buyer size a contract.

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

Bank identity verification and biometric onboarding for regulated finance each get dedicated material, and documents from more than 195 countries and territories are read. Financial services is not split by institution type, and much of the business sits outside finance, from on demand delivery to retail receipt capture.

Tracked Since Listing

Product updates

Material product, regulatory, evidence and commercial changes at Microblink, 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.

Sep 25, 2026Security / compliance

BlinkCard SDK releases published on GitHub on 25 Sep (iOS v3000.1.0, Android v3001.0.0, with Web 3001.0.0 on npm on 24 Sep) make redaction of sensitive card data the default. The card number is now redacted in both the captured image and the returned result with only the first 4 and last 4 digits visible, and the CVV is fully redacted; IBAN and cardholder name are not redacted.

Bears on: GLBA and Data Privacy PostureSource
Our read on this update →Tracked since Sep 2026
Head to Head

Compared With

Most editorial comparisons pair two vendors the index assesses as direct competitors for the same buyer. Some pair vendors that are adjacent rather than rival, where the useful question is where one ends and the other begins. Each carries a verdict, the buyer conditions that favor each vendor, and a graded side by side.

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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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