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Fraud Analytics

Predictive Fraud Analytics for Insurance

Detect, prevent, and manage fraud for the insurance industry through statistical, machine learning, and artificial intelligence techniques.
In the insurance industry, analysis of claims and payments is a paramount issue for the investigation unit in a company. Rawcubes has pre-built frameworks to accelerate fraud model implementation. With our Standardized Data Model (SDM) for insurance, worker compensation fraud implementation will speed up and take action on fraudulent cases without impacting customer experience.

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Medical Code Analysis

We have used both statistical (Outlier) and latest machine learning techniques (Gradient Boosting Classifier, Support Vector Machine etc.) to identify outlier behaviors by medical providers based on the procedures they are billing.

Bills & Pharmacy data Analysis

This type of analysis raises flags for providers or pharmacies who are billing a disproportionate amount of money to an insurance provider in a given code, drug, or drug class.

Bills & Pharmacy data Analysis

This type of analysis raises flags for providers or pharmacies who are billing a disproportionate amount of money to an insurance provider in a given code, drug, or drug class.

Network Analysis

This Analysis looks across all provider’s connections to see if there are unusually high rates of fraudulent activities.

Nearest Neighbor Analysis

This analysis finds the latest providers by the features and the fraudulent activities.

Nearest Neighbor Analysis

This analysis finds providers those who are similar to the ones that have been flagged in other analysis to improve lead generation and give some indication of correlated attributes.

Data Sources

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