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Contradiction Checks surface potential inconsistencies between what has been stated and what financial behaviour shows. Instead of manually reviewing transactions or relying on assumptions, teams receive clear behavioural signals that may warrant further review.

These checks help focus attention on relevant areas while maintaining neutrality and objectivity throughout the claims process.

How contradiction checks are generated

Using verified open banking data, the engine compares observed income, spending and cashflow behaviour against key statements or claim-related context.
Machine learning and rule-based logic identify patterns that do not align, flagging them as signals rather than conclusions.

All outputs remain evidence-led and transparent.

Support consistent investigation

Contradiction Checks help teams assess consistency across cases without introducing bias or interpretation. They highlight where behaviour may not align, not why or what it means.

This supports fair, repeatable investigation workflows while leaving assessment and judgement to professionals.

Why contradiction checks matter

Inconsistent information is difficult to identify at scale.
Behaviour-based checks reduce manual effort, improve focus and help ensure claims are reviewed using the same evidence standards every time.

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Surface inconsistencies with confidence.
Contradiction Checks provide clear behavioural signals to support objective claims review and investigation.

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