AI Agents

What it does

Assesses whether the submitted onboarding documentation is complete, consistent, and sufficient for approval. Flags missing or incomplete documents, detects internal mismatches across files, and recommends additional documentation or clarifications based on context, jurisdiction, and risk level.

Documents used

• All submitted onboarding documents (registry extract, POA, lease, shareholder registry, etc.)

• Jurisdiction-specific and product-specific checklists

• Applicant-declared data from forms or onboarding interface

Extracted data

• List of documents submitted

• Document metadata (type, date, scope, issuer)

• Key entity fields (names, addresses, registration numbers)

• Validity, signatures, completeness status

Checks

1. Are all mandatory documents present, based on company type and jurisdiction?

2. Has the AI detected potential ambiguities that a human would follow up on (e.g., unclear authority in empowerment docs)?

3. Are all documents signed, dated, and complete (e.g., all pages included)?

4. Are there any mismatches across documents (e.g., different addresses, inconsistent names)?

5. Do any documents expire soon (e.g., POA about to lapse)?

6. Are there red flags that require enhanced due diligence (e.g., offshore UBO → request source of funds)?

AI value

The agent performs an end-to-end assessment of document sufficiency, mimicking an experienced analyst who knows what to look for. It catches missing files, partial documents, and logical inconsistencies - then proactively recommends follow-ups, reducing the need for back-and-forth and helping to avoid stalled onboarding processes. Compared to rigid checklists, it understands context and completeness, accelerating throughput and enhancing customer experience with fewer manual handovers.