AI in Claims Processing: Moving Beyond Simple Automation

1Why automation alone isn't enough

The U.S. healthcare system wastes an estimated $250B annually on administrative complexity. Claims processing software is a $47B+ market. But most automation focuses on speed — routing claims faster — without questioning whether the claim is correct.

Auto-adjudication rates above 90% sound impressive until you realize errors are being auto-paid too. The industry needs AI that doesn't just move faster but thinks critically about every claim line.

2Governance-first AI

The shift is from "black-box ML" to explainable, auditable rules. Governance-first means: every flag has a traceable reason, every override is logged, and every threshold is adjustable by the employer.

• Rule transparency — every flag cites specific contract terms or coding logic
• Audit trails — full history of decisions and overrides
• Adjustable thresholds — employers control sensitivity by error type
• Human-in-the-loop — complex cases route to reviewers with pre-built context

3What explainable claims intelligence looks like

Instead of a model saying "this claim is suspicious," the system says: "This claim was billed as DRG 470 but documentation supports DRG 469, representing a $4,200 overpayment based on your contract's MS-DRG fee schedule."

Every flag includes: the rule that triggered it, the financial impact, the contract reference, and a suggested action. Reviewers spend time deciding, not investigating.

4How CodaHx approaches it

We score every claim line against contract terms, coding rules, and pricing benchmarks. Flags include specific citations — not confidence scores. Employers see exactly why a claim was flagged and can tune thresholds without touching code.