Verify every
dental claim.
Bright automates the analysis of dental records to surface anomalies across dental claims, with multi-layered verification that prevents revenue leakage, eliminates unnecessary payouts, and scales to your full claims volume.
Manual review cannot scale to the threat.
Dental claims arrive faster than any review team can meaningfully examine them. The result is a structural gap: high-volume, low-value claims pass with minimal scrutiny, and that is precisely where fraud, waste, and abuse concentrate.
Volume outpaces capacity
Adjusters cannot examine every radiograph attached to every claim. Sampling leaves the majority of submissions effectively unreviewed.
Manipulation is now trivial
Consumer-grade editing tools and generative models make altered or fabricated radiographs straightforward to produce and difficult to spot by eye.
Detection is inconsistent
Outcomes depend on which adjuster reviews the file and how much time they have. The same fraudulent claim may pass or fail on chance.
Four layers of verification.
Each layer answers a different question about the claim. Together they close the routes a fraudulent submission can take. There is no single check to circumvent.
Radiograph Authentication · is the image real?
Validates the integrity of the submitted image. Detects digital manipulation, retouching, cropping, AI-generated deepfakes, and radiographs reused from earlier claims, before the claim proceeds any further through the pipeline.
Patient Identity Verification · is it the right person?
One-to-one matching that confirms the submitted radiographs actually belong to the insured individual on file, preventing identity swapping and borrowed benefits, where one person's imaging is used to claim under another's policy.
Clinical-Narrative Consistency · does it match the claim?
Cross-references the clinical reality depicted in the radiograph against the reported treatment and claim code, confirming logical alignment. Surfaces over-treatment and unnecessary procedures, such as a complex procedure billed against a demonstrably healthy tooth.
Statistical & Policy Anomaly Detection · is the claim plausible?
Identifies outliers and flags treatments that statistically deviate from standard care protocols or fall outside the boundaries of the insurance coverage policy in force.
Verification without added workload.
Bright runs the four checks automatically and returns a scored, prioritised output. Bright surfaces evidence-backed anomalies. Your investigators decide what is fraud.
Ingest
Claim record and associated radiographs enter the verification pipeline.
Analyse
All four verification layers run automatically against the submission.
Score
Findings are consolidated into a ranked risk output with supporting evidence.
Act
Low-risk claims are prioritised for fast processing. Flagged claims route to your investigators with the evidence attached.
Every determination is documented. Each flag carries the evidence behind it, the specific finding, the region of the image, and the check that produced it, so your SIU and legal teams can defend any decision that is challenged. Bright is delivered via API and fits into your existing claims workflow.
Measured in money saved.
Bright is built to produce measurable financial return, not just analytical output. The value shows up in three places.
Prevented payouts
Fraudulent and unsupported claims are identified before payment, directly reducing loss.
Reduced review burden
Automated clearing of clean claims frees adjuster time for the cases that genuinely require judgment.
Consistent enforcement
Every claim receives the same scrutiny, removing variance between reviewers and across offices.
Let's talk about your claims operation.
We'll walk you through the verification stack, discuss how it fits your existing claims workflow, and scope what a pilot would look like against your own data.
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