The CO REACT Platform

On demand or dynamic fraud REACTment 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 car insurance fraud detection. CO React 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

Dedicated CO’s proprietary mobile app available

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CO REACT Minimizes Cost & Expenses

CO REACT Platform assesses all claims, and flags possible fraudulent 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 REACT Optimizes Claims Management & Claims Processing

CO REACT 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 REACT 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

Key Features

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All your data connected in one place

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Embedded Visualization Tools

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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 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.

Fraud Detection based on image similarity algorithms

Using powerful deep learning algorithms, the image analysis component can detect and assess cars that have been involved in accidents in the past. In addition, the image analysis component performs antiphotoshop tests to identify any possible photoshopped images.

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.

Fraud Detection based on mobile meta-data analysis

Metadata analyzer component is an optional functionality which requires, the driver to use, a dedicated mobile application that collects and analyzes data in real time. Its purpose is to present the user's profile and provide information on several driving characteristics and possibly suspicious fraudulent actions.

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 Financial Data Analysis

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