The CO ASSESS Platform

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.

Covariance has designed an advanced AI - based solution for health insurance fraud and waste detection. CO ASSESS is a great solution for screening any claim in order to identify possible fraudulent behaviors.

Dynamically linked to company’s database

User Friendly environment

Customizable to company's requirements

Reporting and KPIs

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CO ASSESS Minimizes Cost & Expenses

CO ASSESS Platform assesses all claims, and flags possible fraudulent or wasteful ones, prior to reimbursement.

Administrative Cost Reduction
Operational Cost Reduction
Inspection Cost Minimization
Expenditure Control
Claims Prediction
Strategic Advantage in Pricing
Strategic Advantage in Underwriting
Ultra-High ROI

CO ASSESS Optimizes Claims Management & Claims Processing

CO ASSESS Platform combines multiple technologies, multiple data sources, and significant expertise of the leading team to maximize results accuracy, and to efficiently identify fraudulent claims. CO ASSESS Platform optimizes claims management and claims processing, trough introducing a paperless process.

Making claims settlement faster using an easy-to-use one-stop-shop
Safeguarding data safety since all processes are internal
Predicting future claims
Offering real time claims and fraud analytics
Optimizes claims management
Optimizes claims processing
Introduces a paperless process

Key Features

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All your data connected in one place

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Unified Visualizations

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No code needed, easy application in just a few clicks

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Scalable for vast amount of data

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Dynamic Dashboards

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Machine and Deep Learning Algorithms

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Risk analysis reporting capabilities

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Claims predictive analytics capabilities

AI-based Components

Fraud Detection based on Supervised ML data models

Fraudulent claims can be assessed and flagged, prior to reimbursement, using an advanced machine learning component that analyses available structured claim data."

Waste Assessment based on a Proprietary Data Base

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.

Fraud Detection based on Network Analysis

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.

Fraud Detection based on Empirical Rules

This component applies standard empirical rules to the data in order to flag possible fraudulent cases.

Fraud Detection based on ML Unsupervised Models

Fraudulent claims can be assessed massively and flagged, prior to reimbursement, using an advanced machine learning component that analyses available structured claim data.

Fraud Detection based on Financial Data Analysis

This component applies dynamic comparative analysis in order to identify fraud cases based on the claim size data.