The questions due diligence actually asks

Yes. EyesClear deploys inside the bank’s own environment — on-premise or private cloud — and the AI runs on that same infrastructure behind a private LLM gateway. No customer data and no model inference goes to an external cloud or a third-party vendor. Data sovereignty is guaranteed by architecture, not by contract.

An analyst. The AI drafts from the alert and the full evidence set; the analyst reviews, edits and signs. AI is the maker and the analyst is the checker — nothing closes and nothing files without that signature.

One engine serves real-time and back-dated processing as two modes. You update the typology, re-run it across the entire transaction history the same night, and review the new alerts the next morning. No vendor statement of work, no multi-month remediation project.

No. EyesClear does not screen payments in real time. It takes the sanctions and name-matching alerts your screening platform raises and resolves them through the same maker-checker workflow. Its own analysis can also flag sanctions concerns for referral to the screening team.

Days, rather than a multi-month integration programme. Detection, case management and reporting arrive as one platform on a single data fabric that connects to the warehouses you already run, so there is no new data-lake programme to complete first.

Configurable report types, field catalogues and transformation rules generate Türkiye’s MASAK ŞİB/STR submissions on the current schema (Communiqué No. 30), the CRS and FATCA regimes, and other complex outputs — with submission tracking, retry handling and scheduling. A new report type is configuration, not development.

By architecture, not by policy. The LLM runs inside EyesClear only, with no access to bank systems, databases or applications. It sees the case data the platform hands it, and nothing else. Everything the agents can do is defined by tools written in-house, each a known, audited capability with defined inputs and outputs.

Universal connectors take SWIFT and ISO 20022 (MT103, pacs.008, camt and related messages are handled natively), real-time queues such as MQ and Kafka, and database and file sources covering CRM, transactions, risk and KYC — all mapped into the single data fabric.

No. Data enters through rules-based collection that your analysts and technology teams control — every source, field and refresh is explicitly configured. Some tools in the market crawl internal data instead; in our view that pattern is not secure enough for a bank. Nothing is read that was not explicitly granted.

A full-platform deployment targets a 30% efficiency gain; running alongside your existing systems targets 10–15%. The two numbers to watch in a pilot are average handling time per case, and analyst hours returned to higher-value work.

No. EyesClear is not multi-tenant SaaS. It runs inside the bank’s own environment as containerised microservices — that is the point of it. It is the investigation and decision platform inside the bank.

EyesClear can arrive with right-sized private AI hardware, so the LLM layer comes inside the perimeter as equipment rather than as a cloud dependency. Ask us where that option stands for your environment.

Yes — that is the design. Compliance officers edit rules and typologies directly, and your team designs what a case captures through dynamic forms that go live without a vendor change request. Every material change still passes through a maker-checker (four-eyes) workflow with a full record of who requested it and who approved it.

The AI never decides on its own. Every output traces back to the source data and is reviewed, edited and approved by an analyst before it is acted on, so there is no black-box score to defend. All evidence, case history and decisions are held centrally with full audit trails, and activity logging records who opened which screen and what they did.