English (UK)
English (US)English (UK)English (US)AI Engine structures medical evidence for new business underwriting and post-issue audit, so underwriters spend their time on judgement, not document assembly.
Bring fragmented medical evidence into one clear, structured view. AI Engine groups conditions across case documents, builds condition timelines, and surfaces key treatments, investigations, and clinical indicators, so underwriters understand a case faster, without rebuilding the medical history by hand.
Compare what was disclosed with what the medical evidence shows. AI Engine classifies each condition as correctly disclosed, under-disclosed or not disclosed, with plain-English reasoning and a direct link to the source evidence behind every flag.
ACCURACY: Non-disclosure detection rate¹
TIME SAVED: Average underwriter time savings for clean cases²
TIME SAVED: Reduction in review time for flagged cases³
Boost case review productivityMove from document searching to case understanding. AI Engine does the evidence-gathering work upfront, so underwriters start from a structured picture instead of a document pile.
Verify every finding in secondsEvery insight is backed by evidence. AI Engine links each extracted condition, clinical indicator, and disclosure flag to its exact source passage, enabling underwriters to validate findings without leaving the workflow.
Cut portfolio risk exposureManual review varies by reviewer, case complexity and time pressure. AI Engine gives every underwriter the same evidence summary and a configurable approach to disclosure analysis.
AI Engine supports the full case review workflow, from document ingestion and decision-ready, condition-level summaries to non-disclosure detection and structured reporting. Every finding is explainable and evidence-backed.
Upload medical evidence, application PDFs or supported Underwriting Engine enquiry data, and process the complete case as one connected document set.
See current and significant previous conditions, investigations, repeat medications and other review-critical information in a structured case overview.
Bring related mentions together at condition level, showing diagnosis, development, treatment and latest status.
Surface relevant measures such as blood pressure, BMI and cholesterol, while consolidating repeated tests and prescriptions.
Open the exact source passage behind any extracted condition, clinical indicator, or disclosure flag.
Set relevance and disclosure thresholds to reflect insurer products and underwriting philosophy.
Capture reviewer feedback, notes, rationale and disclosure findings in a consistent record for QA, governance and audit.
AI Engine supports the full case review workflow, from document ingestion and decision-ready, condition-level summaries to non-disclosure detection and structured reporting. Every finding is explainable and evidence-backed.
The Decision Platform Team
Sydney, 24 June 2026
5 min read
The Decision Platform Team
5 min read
The Decision Platform Team
Sydney, 18 December 2025
5 min read
¹ Non-disclosure detection rate calculated as the percentage of cases in which an experienced underwriter agreed with AI Engine’s identification of non-disclosure, based on average beta test results with major UK insurers.
² Average underwriter time savings for clean cases (no non-disclosure detected).
³ Average underwriter time savings for flagged cases (non-disclosure detected), enabled by AI Engine’s automated linking of application answers to supporting medical evidence.