Arva AI

Take the models away and only the analyst queue is left. Agents do the screening, investigation and onboarding work across five products, with Arva Intel, an in house model, enriching entity records.

Higher risk customers move to a separate enhanced due diligence product, and analysts stay in the loop by design. Which cases agents close without review, the threshold that sends one to a person and any sampling of automatic closures are all unpublished.

The published figures count closures, not correct ones: 92 percent alert resolution and a 21 percent rise in straight through processing. A claim of up to 50 percent higher accuracy than human teams carries no method or test set, and in sanctions screening a false negative is a missed designated party.

A named customer voice sits beside the volume claim of more than 1 million reviews a month. Marvin Hurlston, a senior director for client onboarding and transaction monitoring, says his due diligence runs on Arva and onboards clients considerably faster, though no measured result is tied to his institution.

Analyst feedback trains the agents through AgentCore, and typologies learned at one institution would be valuable to another. The privacy policy defers to a separate note on training data, and the trust center states no boundary between clients.

Content processed for customers falls outside the published privacy policy, which covers only the website and account users. Personal details on directors and owners, plus ownership and banking documents, are kept and hosted under customer agreements Arva does not publish.

SOC 2 Type II and ISO 27001:2022 are named, with GDPR, California and Canadian privacy compliance listed alongside. Documents come on request, and neither dates nor auditors are given.

Four supervisors are named as the frameworks the platform is built for, including the Financial Conduct Authority and the Office of the Comptroller of the Currency. As a technology supplier Arva needs no license, so checking that fit falls to the buyer.

ISO 42001 certification puts an audited management system behind the AI, with stated alignment to the NIST AI Risk Management Framework and ISO 23894. Adverse media matching on common names and website analysis that favors polished English language sites remain exposed, and no error analysis by population is published.

An institution can reconstruct and contest an agent's decision from its audit trail, and the onboarding product gives a business feedback while it waits. Arva offers no published warranty on agent output, and a person wrongly flagged has no described way to see or challenge the finding.

Suppliers are described by category only. Company registries, screening lists and third party models feed the agents without a provider named, and the privacy policy mentions hosting and cloud providers without identifying them.

Each product can sit beside an existing compliance stack or replace it, and AgentStudio turns a bank's own procedures into production agents. Case management, core banking and screening systems are not named as integration targets, so the depth of each connection is unknown.

Where the service runs is unstated: the trust pages and privacy policy publish no hosting provider, region or private deployment option. That gap matters to a buyer base split between British and American supervision.

Pricing, packaging and the basis of charge are all unpublished. For a product pitched as a replacement for analyst headcount, the unit of charge decides the comparison with the team it would replace.

Banks and fintechs can cover most of a financial crime function here, from screening through onboarding to lending research, taking one module or the full set. Entity data reaches more than 150 countries, but the homepage names no bank or fintech customer.