AML, KYC & Financial Crime
K

KYP

KYP, for Know Your Partner, monitors the businesses an institution depends on rather than the customers it onboards, replacing point in time due diligence with continuous surveillance. Volumetric Monitoring analyzes the payment behavior of an acquirer's or payment provider's merchant portfolio to detect emerging transaction risk, and WebCompli monitors merchant websites for compliance, drawing on credit data, anti money laundering signals, adverse media, fraud signals, corporate structure data and cyber risk insight from Mastercard's RiskRecon. Distribution runs largely through payments and embedded finance partners, and named users include Neogames, Pariplay and PayModum.

Last VerifiedOctober 2, 2026
Compare KYP with other vendors
Founded
2021
Headquarters
London, England, United Kingdom
Website
kyp.io
Categories
aml-kyc-financial-crime, fraud-and-transaction-risk, compliance-and-surveillance
Assessment

Capability Axes

Capability grades

15 of 15 axes rated · 5 graded A or B

AI Capability
AI Centrality
BB on AI CentralityThe models are the engine of a core capability, layered on a product that would still function without them as a rules or workflow system.
Vendor Published

Signals from many sources are joined into one risk picture per monitored business, and Volumetric Monitoring reads a merchant portfolio's payment behavior for emerging transaction risk. Aggregation and monitoring underneath would still be useful with threshold alerts alone, so the models sharpen the output rather than make it.

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

Risk alerts go to an analyst to act on, so the platform informs rather than concludes, but that division is implied by the design, not stated. Confidence on a score, an escalation threshold and the expected action when a merchant deteriorates are not described.

Model Risk Management and Transparency
CC on Model Risk Management and TransparencyTransparency is claimed in general terms with no mechanism a model validator could interrogate.
Vendor Published

How often an alert reflects a real change in counterparty risk, and how often a genuine deterioration is missed, are the figures that matter, and neither appears. Precision and false positive rates for scoring and matching are likewise unpublished.

Operational and Outcome Evidence
BB on Operational and Outcome EvidenceVendor aggregate claims with real figures, or audited scale disclosures from a publicly listed company.
Vendor Published

Neogames' group money laundering reporting officer, Fiona Hughes, is quoted on stronger risk monitoring, and Pariplay, Aspire and BtoBet are among the customers named. One figure, manual work cut by up to 60 percent, is tied to no named customer.

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

Many institutions monitor overlapping merchant populations through the same partners, so a profile built for one customer is a natural asset for the next. Sharing of assessments across customers, and protection of the fact that an institution is investigating a given business, are not addressed.

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

Most subjects are businesses, but adverse media and corporate structure data concern named directors, owners and associates. A privacy and cookies policy is linked, while data protection terms and retention for platform data are not published, so nothing says how long an adverse finding persists.

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 certification is named on the homepage, without the certificate or its scope. Selling third party risk assurance makes the firm itself a third party in every customer's supply chain, which raises the bar for that evidence.

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

Open banking provider scoring and merchant monitoring for acquirers are built for named roles, with anti money laundering pressure cited as the driver. Even so, no regulator, statute or card scheme rule is named, and which obligation an acquirer discharges through the platform is not said.

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

Assessing businesses swaps protected class exposure for an accuracy problem of similar weight: a poor score can get a merchant offboarded, and adverse media matching misfires on common names and unfamiliar naming conventions. Match confidence is unpublished, and combining many signals lets one bad match color the whole picture.

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

Full audit reports document what was checked, which supports a defensible decision and lets a wrong one be traced and reversed, though no guarantee, indemnity or accuracy commitment stands behind them. The monitored business is assessed without its knowledge, partly from adverse media it cannot see, and no notification or dispute route is described even where the outcome is losing the ability to take payments.

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

Mastercard's RiskRecon is named as the source of cyber risk insight, and payment behavior, credit data and adverse media are among the data categories listed. Other providers behind those categories, any model provider and a subprocessor list are not 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

Infrastructure partners carry much of the reach: Integrated Finance in embedded finance, Tell.Money in open banking and PayModum in payments, with data connected through APIs. Connection depth is left open, with no acquirer processing platform, case management system or institution side core named.

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

Hosting provider, region and residency commitment are unpublished for a London firm whose partners operate across Europe.

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

Merchants watched, assessment frequency or alert volume could each drive the cost, and the site publishes no price, package or basis of charge to say which. An acquirer with a large portfolio and a firm watching a handful of partners would face very different bills.

Institution and Segment Coverage
CC on Institution and Segment CoverageSegments claimed broadly, banks, fintechs, credit unions, without evidence any of them has its own maintained surface.
Vendor Published

Acquirers and payment service providers are the stated targets, with merchant website compliance monitored through WebCompli. Yet the named customers lean toward gaming and lottery operators and suppliers, and no bank or acquirer is named.

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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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