Fraud Detection

Detect and eradicate Fraud with Data Analytics and AI

Goal

Fraud Detection → Strategic Advantage → Cost Reduction  → Increased Revenue and ROI

Challenges

According to the FBI, the total cost of insurance fraud (non-health insurance) is estimated to be more than $40 billion per year, which costs average U.S. family between $400 and $700 per year in increased premiums. Insurance claims processing is dull, potentially leading to errors. Some fraud types are well known and can be located by using rules-based systems. However, new or more ‘’subtle’’ fraud is missed by rules-based systems until that fraud becomes evidence based.

Opportunities

AI ideally adapts to fraud detection for insurance claims. Machine learning models can be deployed to automate claims assessment and routing based on existing fraud patterns. This process flags potentially fraudulent claims in order to be further reviewed but also has the added benefit of automatically identifying good transactions and streamlining their approval and payment. More sophisticated detection systems can be deployed to find new patterns and to flag those for review, which leads to prompt investigation of new fraud types. AI systems can also provide concrete codes for investigators, so they can quickly see the key factors that led the AI to indicate fraud which streamlines their investigation. With AI based fraud detection, fraudulent claims can be assessed and flagged before they are paid, which reduces costs for insurance providers and helps reduce costs for consumers.

Application Specific Services

AI.R Solutions.

From paper to insights solution.
Covariance Team supports you to “read paper”, create automatically structured data bases and acquire knowledge using Machine Learning Technology.
Covariance Team supports you to identify fraud even from paper documents.

Application Specific Services

Signature identification.

Covariance Team provides you Neural Network based technology to automatically identify fraudulent signatures.

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