Automate Fraud, Waste, and Abuse Detection with AI
Billing anomalies and provider misconduct do not wait for audits. AI detects fraud, waste, and abuse patterns before they escalate into regulatory action.
Fraud, Waste, and Abuse Detection
CMS and OIG have intensified their focus on recovering improper payments and penalizing non-compliant billing practices. From upcoding to unnecessary services, many healthcare organizations remain unaware of the financial and reputational risks until they are subject to enforcement.
Karma Health AI eliminates that blind spot. Our platform analyzes claim activity, detects suspicious billing behavior, and flags high-risk providers or CPT codes in real-time. We provide automated surveillance to help you move from reactive compliance to proactive risk management.
Our solution enables healthcare systems, MSOs, and multi-location providers to reduce payment errors, strengthen internal controls, and meet fraud prevention obligations under federal law.
AI-Powered Tools to Ensure Compliance
In a 2023 study published inSN Computer Science, researchers introduced a data-centric approach that improved Medicare fraud classification through enriched datasets and intelligent labeling techniques. Their work shows the clear advantage of AI in identifying fraud patterns across large claims datasets.
Karma Health AI incorporates these advancements to power enterprise-grade fraud detection:
- Trains AI models on enriched CMS datasets and exclusion lists
- Detects outlier behavior at the claim, provider, and location level
- Scores are risk-based on payment velocity, CPT clusters, and historical anomalies
- Flag upcoding, duplicate claims, and overutilization patterns
- Aligns with LEIE exclusions and CMS audit triggers in real-time
Our system uses advanced validation and explainability to ensure alerts are defensible and auditable. Each flag includes supporting context, so your compliance team can act with confidence.
Fraud, Waste, and Abuse Detection for Safer, Compliant Healthcare Operations
Fraud prevention is not just about avoiding enforcement. It is about building a culture of accountability, transparency, and financial stewardship. Karma Health AI helps organizations achieve that by embedding real-time detection into everyday operations.
In one example, a multi-site MSO used our platform to uncover $450,000 in inappropriate Part B billing tied to 12 locations. By detecting the issue early, they avoided a CMS clawback and reinforced internal training protocols.
Whether you oversee a single health system or a network of practices, our tools offer:
- Dynamic tracking of high-risk services and codes
- Customizable alerts tied to risk thresholds
- Location-based heatmaps and claim anomaly visualizations
- Full integration with compliance, billing, and revenue cycle workflows
Stay ahead of scrutiny. Stop losses before they scale. And build a compliance foundation that your stakeholders can trust.
Are You Proactively Detecting
FWA or Waiting for a CMS Audit?
Fraud, waste, and abuse are not always intentional but are expensive. Karma Health AI provides advanced compliance tools that spot risk before payers or regulators flag it. Using AI trained on historical CMS enforcement data and real-time provider behavior, our platform helps your organization catch anomalies early, defend your claims with evidence, and build an accountability infrastructure. Stay ahead of fraud, not behind it.
We will show you exactly how many opportunities you are missing, and how
Karma Health AI can help you capture them.
Frequently Asked Questions
How does AI detect healthcare fraud, waste, and abuse?
Karma Health AI uses supervised learning, CMS datasets, and behavioral pattern analysis to flag anomalies like overbilling, upcoding, and service misuse.
Can your platform track provider-level and claim-level risk?
Yes. Our system scores risk at both the individual claim and provider summary level, enabling granular and macro-level analysis for billing compliance.
What types of claims data can your system ingest?
We support CMS Part B, Part D, and DMEPOS data and commercial insurance claims, enabling a complete picture of billing behavior.
Is your platform suitable for large MSOs or ACOs?
Absolutely. Our tools are designed for enterprise-scale oversight with cross-location comparisons, audit logs, and policy alignment controls.
How do you ensure model accuracy and prevent false positives?
We use adjusted cross-validation techniques, updated exclusion data, and transparency-focused design to ensure precision and minimize review burden.
