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Fraud, Waste & Abuse
Fraud in health insurance claims exists.
We propose a solution to solve this problem using AI
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Fraud in health insurance claims exists.
More than 5% of claims are fraudulent:
· Phantom claims
· Diagnosis code falsification
· Identity theft
· Kickbacks
· Drug reselling
· Other schemes
Why is it difficult to detect fraudulent claims?
Clinically justified
Cleverly concealed
Constantly changing
Fraud claims are:
Fraud in claims may be “invisible” for claims handlers relying on clinical experience and thinking on a single patient level.
There are many IT solutions helping health care providers to pass simple checks of insurers.
As soon as Payer detects fraud, provider starts to invent new fraud patterns.
Fraud problem solution
Adaptation
Support
Fast launch
Ability to add, tune or cancel fraud detection algorithms and triggers
Permanent process fraud analysis and fraud model development
FWA Detection AI
Run without training on your data
Unsupervised AI models identifying anomalies treatment patterns trained on large and diverse training database
Fraud Patterns library
Ready to use Library with proven fraud patterns with more than 2.5 million triggers
Negotiation & BI toolkit
Interactive BI reports illustrating either detected fraud cases or suspicious trends
We’ve been given a dataset for 2 months period of medical services reimbursed by one of the big Health Insurers.
Case study
Romania Business case
How do you feel about big data?
Health. Advanced Health Analytics
