Due diligence questionnaires exist to help firms assess the risks tied to counterparties, service providers and other third parties. But completing one is only half the battle. The harder task is making sure nothing important gets lost among hundreds of largely routine responses.
That is the problem RegTech firm Zeidler Group is targeting with AI Questionnaire Review, a new feature built directly into its Zeidler Due Diligence (ZDD) platform. Rather than requiring compliance teams to work through DDQs line by line, the tool is designed to point reviewers straight to the responses that actually warrant scrutiny.
At the click of a button, Zeidler’s AI analyses a completed questionnaire and sorts each response into one of three categories: Satisfactory, Flagged or Unanswered. Flagged and unanswered items are then grouped into a dedicated panel, letting reviewers skip past routine answers and go straight to the ones that matter.
A response such as an admission that a counterparty has no cybersecurity policy might be simple to understand once spotted, but easy to miss buried in a lengthy document. Surfacing it automatically removes that risk.
Crucially, Zeidler’s tool does not just flag responses, it explains them. Each assessment comes with a plain-English rationale, noting whether a question was left incomplete, information appears to be missing, or the content suggests a potential compliance, regulatory or operational concern. A Flagged status is not treated as a failed assessment but as a prompt for the reviewer to decide whether clarification, evidence or escalation is needed.
That explanatory layer also feeds into follow-up work. Rather than compiling a separate list of outstanding queries after a full read-through, reviewers can act directly from the flagged panel, whether that means chasing more detail on an outsourcing arrangement or requesting supporting documentation for a stated control.
Because due diligence is rarely a one-off exercise, the tool can also be re-run whenever a counterparty submits updated answers or new evidence, allowing reviewers to reassess flagged areas without starting the process over. Zeidler argues this iterative support matters as much as improving the initial pass, since questionnaires are typically clarified and revised over several rounds.
The feature is also pitched as a way of improving consistency across review teams, where experience levels and familiarity with a counterparty can otherwise shape what gets noticed. By applying the same structured first-pass analysis to every questionnaire, Zeidler says teams get a more uniform baseline, even as human judgement continues to determine significance.
AI Questionnaire Review sits natively within ZDD, meaning questionnaires, AI assessments and the wider review record stay in one system rather than being exported elsewhere for analysis. For Zeidler, that integration is central to the pitch: the goal is not replacing reviewers, but freeing up their time for the judgement calls that automation cannot make.
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