Mercury Fraud Detection Scoring for Claims Documents

Fraud controls break down when every adjuster has to decidefrom scratchwhich documents look suspicious. The fastest organizations build a consistent triage motion that surfaces risk early while preserving good customer experiences.

Mercury includes AI-powered document fraud detection with a 1-100 scoring approach that helps teams prioritize review. The point isnt to replace investigation judgment; its to give claims operations a repeatable way to route the right files to the right level of scrutiny.

Why a document-level score matters

Most claims organizations dont have the time (or specialist capacity) to deep-review every piece of incoming paperwork. A document-level score creates a practical signal that can be used in workflow automation:

• Low scores move through standard processing with fewer interruptions.
• Mid scores can trigger lightweight verification steps (additional documentation, second review, or targeted questions).
• High scores can route to SIU or a specialized fraud queue for deeper analysis.

Even when fraud is rare, the operational cost of inconsistent handling is high: rework, delays, and uneven customer treatment. A standardized scoring signal makes it easier to apply policy consistently.

Where it fits in Mercury workflows

Document fraud scoring is most valuable when its integrated into claims intake and processing steps, not bolted on after the fact. For carriers, MGAs, and TPAs, that typically means:

• Intake: apply scoring as documents arrive via email, portal upload, or imaging pipelines.
• Triage: combine score thresholds with business rules (line of business, claim type, loss severity, jurisdiction).
• Assignment: route to adjusters or SIU based on workload and expertise.
• Documentation: record why a claim was escalated, creating an audit trail for internal reviews.

This is also where the broader document imaging + NLP foundation pays off: when documents are consistently captured and classified, it becomes easier to ensure the score is applied to the right inputs and stored with the correct claim context.

Operational guardrails for adoption

Any scoring model needs guardrails so it improves outcomes instead of creating noise. Practical steps include:

• Define thresholds that match your SIU capacity and desired cycle-time impact.
• Calibrate with feedback so investigators can label outcomes and refine routing rules over time.
• Prevent over-escalation by pairing score signals with human review criteria.
• Measure leakage reduction alongside customer impact metrics like time-to-contact and time-to-settlement.

When deployed thoughtfully, document fraud scoring becomes a consistency tool. It helps organizations apply the same level of rigor across teams, geographies, and peaks in claim volume.

If youre evaluating claims modernization, this is one of the clearest places where AI can support a real, measurable operational processwithout relying on unproven generative AI promises.