Insurance Business Automation with AI
On demand or dynamic fraud and waste assessment by combining multiple technologies and multiple data sources to maximize results accuracy, and to efficiently identify fraudulent claims.
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Contact usFraudulent claims can be assessed and flagged, prior to reimbursement, using an advanced machine learning component that analyses available structured claim data."
Assess the healthcare expenditures invoiced by healthcare providers to insurance companies, in hospitalizations. Evaluates costs of services and frequencies of services invoiced by healthcare providers, compares charges for consumables to market prices, checks actual quantities compared to expected as defined by physicians and healthcare experts. A comparison of the invoiced prices with the market prices is made and an explanatory report is delivered.
The network analysis component combines ultra-modern technologies to enhance the insurance claims evaluation process. Using the claim information data, the network analysis component generates a possible fraud network map, including various parties (individuals and service providers) that may be involved in possible fraud or waste.
This component applies standard empirical rules to the data in order to flag possible fraudulent cases.
Fraudulent claims can be assessed massively and flagged, prior to reimbursement, using an advanced machine learning component that analyses available structured claim data.
This component applies dynamic comparative analysis in order to identify fraud cases based on the claim size data.
Covariance is empowering leading healthcare organizations to deliver Data-Driven and AI solutions that shape the industry, while supports them to maximize their customers’ value and minimize risks & costs by integrating real-time analysis into existing systems and processes.