Commercial payers
Pre-payment scoring to hold suspect claims for review before dollars go out.
Payer Integrity · AI Agent
Medical Billing Fraud Detection
Suspicious claims hide among millions of clean lines. Agnotic Technologies provides fraud detection services for payers, building software that scores claims for fraud, waste and abuse and hands the suspicious few to a Special Investigations analyst.
Human-in-the-loopHIPAA-ReadyAudit LoggedExplainable ML
Trusted by global innovators
What is fraud detection for healthcare payers?
Fraud detection for healthcare payers is software that scores claims and providers for fraud, waste and abuse, using anomaly models and graph analysis of provider, member and referral relationships. Agnotic Technologies builds it for health plans, TPAs and special investigations units, with an explanation on every flag and an investigator deciding each case.
Fraudulent, wasteful or abusive claims (upcoding, unbundling, phantom billing, collusive rings) are a small fraction hidden in the flood. Rules-only edits catch obvious patterns and miss adaptive ones, and manual review can only sample.
Anomaly models flag claims and providers that deviate from peer and historical norms, and graph ML surfaces suspicious relationships. Each flag is scored, explained and routed to an analyst; the software never denies a claim or accuses a provider on its own.
Anomaly + graph ML on the claim stream
Score, explain, and route the suspicious minority to investigators, with a human deciding every outcome.
What does the fraud detection software do?
Detection built for how healthcare fraud behaves: anomalies and relationships, not just static rules.
Anomaly detection
Scores each provider and claim against peer-group and historical baselines to catch upcoding, unbundling, and impossible-day billing rules miss.
Graph ML on relationships
Builds a graph of providers, members, and referrals to expose collusive rings, kickbacks, and identity misuse no single claim shows.
Claims-native scoring
Ingests X12 837 claims and 835 remittances with CPT/HCPCS, ICD-10, and modifier context so scores reflect real coding.
Explainable risk scores
Every flag carries the features that drove it (codes, peers, relationships) so investigators act on evidence, not a black box.
Investigator review gate
Flags route to Special Investigations for a human decision; the agent prioritizes and explains, never denies or sanctions automatically.
Who runs it
Who uses payer fraud detection?
The same agent, aimed at where payer losses concentrate.
Medicaid & Medicare plans
Program-integrity screening tuned to public-program billing patterns and rules.
Special Investigations Units
A ranked, evidenced queue that replaces sampling with prioritized casework.
TPAs & self-funded plans
FWA detection for administrators managing claims on behalf of employers.
Provider-network integrity
Graph analysis to surface collusive referral and billing rings across a network.
Post-payment recovery
Retrospective scoring of paid claims to target audit and recoupment efforts.
What changes for investigators?
Directional targets measured on your own claims, never fabricated.
Ranked
Suspicious claims prioritized by explained risk
Fewer
False positives on honest, high-volume providers
1 gate
Investigator decision on every flagged case
Compliance-First Healthcare App Development Services Backed by Global Standards
01
HIPAA
Health Insurance Portability and Accountability Act
Protect PHI with privacy-first architecture, encrypted storage and transmission, strict access controls, and traceable audit logs.
02
GDPR
General Data Protection Regulation
Implement lawful consent flows, data minimization, retention controls, and secure processing for sensitive health data.
03
FHIR
Fast Healthcare Interoperability Resources
Enable standardized health data exchange across apps, care teams, and systems through standards-based FHIR APIs.
04
HL7
Health Level Seven International
Support enterprise-grade interoperability with HL7-based integrations for records, events, and clinical messaging workflows.
05
HITRUST
Health Information Trust Alliance
Align security programs to healthcare-specific control and risk management practices trusted by providers and ecosystem partners.
06
HITECH
Health Information Technology for Economic and Clinical Health Act
Design with breach notification readiness, digital record safeguards, and operational controls that support regulated care programs.
07
SaMD
FDA Software as a Medical Device
Plan software quality, traceability, and documentation pathways for products that may require SaMD review and submission.
08
MDR (EU)
Medical Device Regulation (European Union)
Prepare EU market-ready processes for risk classification, evidence tracking, and lifecycle governance under MDR expectations.
09
SAMHSA
Substance Abuse and Mental Health Services Administration
Apply confidentiality controls and consent-aware sharing models for behavioral and mental health data experiences.
Standards it speaks
Which systems and standards does it connect to?
X12 837X12 835CPT/HCPCSHIPAANIST
How It Works
How does a claim become a reviewed case?
A four-stage pipeline that turns a flood of claims into a ranked, evidenced queue for investigators.
1.
Ingest claims
The agent ingests X12 837 claims and 835 remittances, plus provider and member reference data, into a governed, PHI-secured pipeline.
837 / 835 intake
2.
Score anomalies & graph
Anomaly and graph ML models score each claim and provider and surface suspicious relationships, flagging uncertainty.
Anomaly + graph ML
3.
Explain & route
Each flag is packaged with its driving features and routed to a Special Investigations analyst, who confirms, clears, or escalates.
Sign-off required
4.
Record & learn
The decision and rationale are written back to case management, and the outcome feeds model retraining.
System write-back
Facts and sources
Which rules shape payer fraud detection?
Short facts we build fraud, waste and abuse detection around, each linked to its primary source.
Medicaid program integrity rules define fraud as an intentional deception or misrepresentation made with the knowledge that it could result in an unauthorized benefit, and abuse as provider practices inconsistent with sound fiscal, business or medical practices that result in unnecessary cost to the Medicaid program.
Source: 42 CFR 455.2 (eCFR)HIPAA adopts ASC X12 standards for electronic transactions, including health care claims (837), payment and remittance advice (835), eligibility inquiries (270/271) and prior authorization requests (278).
Source: 45 CFR Part 162 (eCFR)A HIPAA covered entity may share protected health information with a business associate, such as a software vendor, only under a business associate agreement that commits the vendor to safeguard it.
Source: 45 CFR 164.502(e) and 164.504(e) (eCFR)The NIST AI Risk Management Framework is a voluntary framework that organizes AI risk work into four functions: Govern, Map, Measure and Manage.
Source: NIST AI 100-1
Related proof of compliant AI delivery
Read Case StudyLera Health: compliant women's health platform
A different domain, not a payer-integrity deployment, but it shows how we engineer AI on sensitive data: privacy-first architecture, consented flows, and human-reviewed outputs.
Compliance & Guardrails
Guardrails on every flag
An investigator owns every consequential decision, and every score can be explained and audited.
PHI handling
Claims and member data stay in HIPAA-ready, encrypted infrastructure under a BAA, with least-privilege access scoped to the investigations team.
Human-in-the-loop
Every flag is decided by a Special Investigations analyst; the agent scores and prioritizes, never acts against a provider on its own.
Explainability
Each score exposes the anomalies and relationships behind it, so decisions withstand provider appeals and regulatory scrutiny.
Audit logging
Every score, flag, review, and outcome is timestamped and attributed for a defensible record.
Frequently Asked Questions
Claims and member data stay in HIPAA-ready, encrypted infrastructure under a signed business associate agreement, with least-privilege access and audit logging of every score and decision.
No. A Special Investigations analyst decides every case. The software scores and prioritizes; it never denies a claim or acts against a provider on its own.
Rules catch fixed patterns and a chatbot only talks. This software adds anomaly and graph ML that learn adaptive fraud and collusive relationships, then hands investigators explained flags alongside your existing edits.
It depends on how quickly claims and reference data can be accessed, how many lines of business are in scope, how many known cases exist for backtesting and which case management system receives the flags. We give you a timeline after the scoping call.
Yes. The software reads X12 837 claims and 835 remittances or your claims warehouse, and writes each flag, its explanation and the investigator's decision back to your case management system.
Yes. The first release scores one line of business, such as post-payment professional claims, backtested on your known cases and run in shadow mode before investigators act on it.
AI tools help our engineers draft code and tests, but a person reviews every change, and it passes the same automated tests and security checks as any other code. Which tools may see your code or data is agreed with you at the start.