Real-time AI detection
If fraud detection runs at month-end, you are chasing money already gone.
Real-time AI detection at claim submission. Not batch audits after the check cleared.
Post-pay audits recover pennies on the dollar. Every report from ABI to FinCEN says the same thing: prevention before payment wins, recovery after payment loses. Inspector AI runs 25 detection rules and an AI anomaly layer at the moment the claim is submitted — before authorization, before the money moves, before recovery becomes someone's quarterly project.
The economics of post-pay recovery
Post-pay audit recovers somewhere between three and seven percent of flagged spend after collection costs, legal delays, and the claims that simply become uncollectible. Every dollar the platform identifies after the check cleared is competing with every other recovery project inside your operations team, and losing. Prevention is a different economics entirely. A rejected claim never needs to be recovered. The math is not close: preventing fraud at submission is far more profitable per dollar flagged than catching it at audit.
What real-time detection actually means
Real-time means at the moment the claim is submitted, before authorization, before the dispensation is released. Not the next batch job tonight. Not the weekly reconciliation report. Inspector AI runs 25 rules plus an AI anomaly layer in the decision path of each claim. A flagged claim returns a rejection or a review route in the same round trip as a normal approval. The workflow is the same. The outcome is different.
The 25 rules, and why rule count is the wrong metric
Most vendors count rules. Rule count is a vanity metric. A platform with 200 rules that all fire on the same obvious patterns is worse than a platform with 25 that cover same-molecule re-authorization, cloned prescriptions, cumulative dose, generic substitution, early refill, and clinical mismatch — because the 25 are composed to avoid overlap and maximize coverage. Depth beats breadth. The question is not how many rules you run. It is how much of your WAFL exposure they actually measure.
Where AI helps, and where rules still win
Rules are precise. Rules are auditable. Rules hold up in a regulator meeting because you can point at the clinical guideline they enforce. AI catches what rules miss: fast-mutating schemes, long-tail anomalies, peer-group outliers that do not match any pre-existing pattern. A serious platform runs both. It runs rules first — because rules are defensible — and then runs AI as the second pass on whatever the rules did not catch. Rules without AI miss too much. AI without rules loses the audit. The right architecture runs them in sequence, not in competition.
pharmacy detection rules plus an AI anomaly layer, running at claim submission — not end of month
Stop chasing. Start preventing.
A three-week proof of concept on your real pharmacy data. See what real-time detection flags before the check is written.