Catch what individual review misses.
Duplicate billing, upcoding, and diagnosis mismatches pass through unseen when detection depends on the memory of individual assessors. Ajé looks for the patterns across your whole book — and flags them at the point of decision, before the money leaves.
Pattern detection at portfolio scale.
Cross-provider patterns
Behaviour that looks normal on a single claim stands out across a provider's history and against peers. Ajé surfaces the outliers that no single reviewer would connect.
Duplicate and upcoding checks
Duplicate submissions and claims coded to a higher-cost service than delivered are detected systematically, not by chance.
Diagnosis-to-procedure mismatches
Procedures and prescriptions that don't fit the stated diagnosis are flagged for review, tuned to the Nigerian disease burden rather than a foreign dataset.
Routed to investigations
Flags don't stall clean claims. Suspicious patterns move to an investigations queue with the evidence attached, while everything else keeps flowing.
Screen, flag, investigate.
Screen during adjudication
Every claim is screened for FWA signals as it is adjudicated, so detection happens at the point of decision rather than in a monthly report.
Flag the pattern
Claims that match a suspicious pattern are flagged and held, with the contributing signals recorded in the append-only event log.
Investigate with evidence
Your investigations team picks up flagged cases with the full trail attached — provider history, related claims, and the reason for the flag.
Leakage is a pattern problem, not a claim problem.
Most fraud, waste and abuse is invisible one claim at a time. A single duplicate looks like an error; a single upcoded procedure looks like a judgement call. It is only across a provider's history, and against the behaviour of peers, that the pattern becomes obvious. Human reviewers cannot hold that much context in their heads, and a report that arrives after payment cannot undo the spend.
Detecting the pattern at the point of decision changes the outcome. Questionable claims are held before money leaves, providers who consistently bill outside the norm are surfaced, and your investigations team works from evidence instead of hunches — all without slowing down the clean claims that make up the majority of your book.
What individual review misses, portfolio-scale pattern detection catches.
Fraud, waste & abuse, answered.
What is fraud, waste and abuse (FWA) in health insurance?
How does Ajé detect fraud that individual reviewers miss?
Does detection happen before or after payment?
Is the detection tuned to the Nigerian context?
Find the leakage in your own book.
We walk through fraud, waste and abuse detection on the claims patterns your team sees every day.