AI Agents

Context

Built for banking within the Corporate Account Opening stack and used by Corporate Onboarding teams, this agent checks that the story told by a group’s documents actually hangs together. It runs once key group documents are collected—registries, articles, resolutions, ownership declarations, UBO forms—and the bank needs to know whether names, roles, dates, and structures are coherent across entities before sign-off.

What it does

The agent looks across all documents linked to a corporate group and performs coherence checks: legal names and IDs, group structures, key roles (directors, signatories, UBOs), and timeline elements such as incorporation dates, board changes, and mandate validity. It builds a cross-document view of the group, compares how each entity and person is described from source to source, and flags inconsistencies or gaps—for example, a director present in one registry but missing from board minutes, a changed legal name not reflected in mandates, or mismatched incorporation dates. The output is a simple set of consistency findings written back into the onboarding case, so teams see clearly what aligns and what needs clarification before approval.

Core AI functions

The core capability is cross-document graphing: the agent turns entities, people, roles, and dates from multiple documents into a connected graph, then runs consistency checks over that graph. Instead of looking at each document in isolation, it compares references across the whole pack and highlights where the graph “breaks” (name variants, missing links, timeline contradictions) so humans can quickly focus on the small set of issues that matter.

Problem solved

Timeline and naming inconsistencies are a classic source of last-minute surprises in corporate onboarding—picked up late by a careful reviewer, a downstream product team, or a regulator asking why documents don’t match. Without a structured cross-check, each team piece-meals its own review, some discrepancies are missed, and others lead to rework and back-and-forth with the client just before go-live. This agent moves that check earlier and makes it systematic, so inconsistencies are surfaced and resolved before the case is considered “complete.”

Business impact

The impact is fewer last-mile surprises. Onboarding teams catch conflicting names, roles, and dates while there is still time to fix them calmly, rather than at the point of account opening, product setup, or audit. That reduces documentation defects, shortens back-and-forth with clients on “just one more clarification,” and strengthens the bank’s ability to show regulators that the group information it relies on is both complete and internally consistent. For Tier 1–2 banks, this lowers the risk of embarrassing corrections and remedial KYC work on supposedly “finished” relationships.

Integration and adjacent use cases.

Integration complexity is medium: the agent needs access to the onboarding/KYC case, document sets for all relevant group entities, and the customer/group master data, and it writes its consistency findings back into the same case.

Common combinations in this stack:

• Multi-Entity Legal Identity Extractor to extract and normalise legal names, IDs, and registrations across group entities before consistency checks;

• Ownership Structure & UBO Mapping to build the ownership and control graph that the consistency checks can validate against;

• Board Authority & Multi-POA Validator to ensure mandates and POAs line up with the names, roles, and timelines found across documents;

• Compliance Risk Scanner & Jurisdiction Classifier to apply PEP/sanctions and country-risk checks once the party and jurisdiction set is consistently defined; and

• Corporate Onboarding Completeness Agent to act as the final gate that only releases the relationship when documents, ownership, authority, compliance, and consistency checks are all green.