Fraud Detection: Healthcare Claims

Improving Healthcare Claim Fraud Detection

Audit ReportIn this study, 9400 records based on fictitious healthcare claims data were subjected to the first two of the following possible tests:
• Duplicate claims
• High volume claims
• Numeric abnormalities
• Multiple claims for same patient by same provider
• Provider submission anomalies
• Multiple claims for same patient by different providers
• Multiple procedures replacing a single one

For the duplicate claim analysis, we used aggregation techniques to count the number of claims by each provider for the same patient on the same date. In the case of the acceleration analysis, we used aggregation to calculate the month-by-month incremental change in total billing and number of claims for each provider.

The tests identified a number of claims as being fraudulent in the test data set.

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