Agio Ratings

The removal test leaves reputation and regulatory status, which the company identifies as exactly what risk managers were relying on before. Calibrated statistical models combine on-chain signals such as reserve balances, wallet flows and transaction patterns with conventional financial indicators to produce a numerical probability of default, refreshed daily across more than 70 counterparties, and a further layer converts a portfolio of those probabilities into a loss distribution. Producing a defensible default forecast for an entity that files no accounts is achievable no other way.

The product is explicitly an input rather than a decision. Ratings are described as quantitative inputs for exposure limits, counterparty selection and insurance underwriting, fitting into the same framework governing traditional credit decisions, and the named customer describes them as an independent signal alongside its own monitoring rather than a replacement for it. That framing is correct for a ratings business, where the user sets policy and the rating informs it. No threshold or automated action mechanism is described, which is consistent with the model.

This is the strongest validation evidence in the index because it is out of sample, real world and tested in both directions. The models flagged elevated default risk at a major exchange four months before its collapse, a correct positive on the most consequential failure the market has seen, and separately assigned a relatively low default probability to an exchange that then withstood a 1.5 billion dollar security breach, a correct negative under extreme stress.

Most vendors publish accuracy on their own benchmark; this one has two public calls that could have gone against it. Methodology is published, with ratings driven by nearly twenty scored variables and the categories named, forecasts described as empirical and calibrated, and specific probabilities published with quarterly change.

A major market maker is named as a customer with its head of risk quoted describing the alerts as an independent signal alongside internal monitoring, and the customer base is stated to include regulators, banks, funds and insurers. The strongest commercial validation is an insurance partnership supporting a crypto exchange default product, which means an underwriter is pricing real risk on these numbers rather than merely consuming them as research. More than 11 million dollars has been raised, with the most recent round including an insurance group's venture arm, and coverage extends across more than 70 exchanges and custodians.

No boundary statement was located. Clients upload counterparty portfolios to obtain loss distributions, which discloses their venue exposures to the vendor, and those clients compete with one another. Nothing states whether aggregate client exposure data informs ratings or research, how portfolio inputs are segregated, or what the company may infer and publish from seeing where the market's capital actually sits.

No data protection agreement, retention schedule or subprocessor list was located. The exposure here is genuinely lower than almost anywhere else in this index, because the subjects of analysis are institutions rather than individuals and the inputs are public chain data and financial indicators rather than personal information. What remains undisclosed is how client portfolio and exposure data is handled, which is commercially sensitive since it reveals where a trading firm holds capital.

No attestation, certification, trust centre or enumerated framework was located. Banks, insurers and regulators are stated customers whose supplier assessments would have covered this ground, and for a provider holding client counterparty exposure data a published control set is the natural disclosure, particularly given that a breach would reveal where major trading firms hold capital.

Three regulatory instruments are named as the drivers creating institutional demand, covering the European digital asset regime, United States stablecoin legislation and an accounting bulletin governing crypto custody, and the company argues correctly that banks and regulated funds entering these markets need documented risk frameworks. Regulators are stated to be among its customers.

The material gap is its own status: this is a ratings business and no recognition or registration as a credit rating agency under any regime is claimed, which matters because regulated firms can only use ratings from registered agencies for certain capital and reporting purposes.

No individual is assessed, and the adapted exposure is the classic hazard of the ratings business rather than demographic bias. A published default probability can help cause the outcome it forecasts, because institutions withdrawing capital from a downgraded venue reduce its liquidity and reserves, which the model then observes as further deterioration.

The company publishes named counterparties with numerical probabilities openly, which makes that reflexivity more immediate than for a subscription only rating. Nothing published addresses the feedback effect, how a rated entity is notified before publication, or how a smaller venue with thinner on-chain history is treated relative to an established one.

No guarantee, indemnity or correction process was located, and the affected party here is the rated institution rather than a consumer. A published default probability directly affects an exchange's ability to attract institutional capital and to obtain insurance, and nothing describes whether a venue is notified before publication, whether it may submit information or challenge inputs, or how a rating derived from misread on-chain reserves would be corrected. Established rating agencies operate formal appeal and comment procedures precisely because the consequences are material.

Input categories are enumerated specifically rather than gestured at, covering on-chain reserves, operating leverage, flow volatility and venue maturity among nearly twenty scored variables, and the company describes combining chain level signals such as wallet flows, reserve balances and transaction patterns with traditional financial indicators. That lets a buyer understand what a rating rests on. Held at B because no chain analytics provider, market data source or financial data vendor is named, and no subprocessor list appears, so dependency concentration cannot be assessed.

Delivery is as ratings, alerts and portfolio modelling rather than through embedded integration, and the value proposition is explicitly that a firm avoids building models and maintaining data pipelines itself. No named risk system, treasury platform, order management system or interface documentation was located, so how ratings reach a risk team's existing exposure monitoring is not described.

No hosting provider, region selection, residency commitment or private deployment option was located. Client portfolio exposures are the sensitive holding rather than personal data, and institutions in Europe and the United States subject to their own operational resilience requirements would ordinarily ask where a risk data provider processes and stores that information.

No pricing, packaging or basis of charge was located. The company does publish specific ratings openly, including named counterparties with their default probabilities and quarterly movement, which is unusual openness about the product itself, and nothing indicates what access to the full daily updating platform costs or whether ratings, alerts and portfolio modelling are sold separately.

The buyer set is unusually complete for a young company, spanning trading firms and market makers, funds, banks, insurers and regulators, which are the parties on every side of a counterparty exposure. Rated coverage extends to more than 70 exchanges and custodians with lenders also in scope.

The constraint is subject matter: this is counterparty credit risk in digital assets specifically, deliberately narrow, and the company positions that focus as the reason established agencies leave the gap open.