Healthcare fraud detection involves account auditing and detective investigation. Careful account auditing can reveal suspicious providers and policy holders. Fraudulent claims often develop into patterns that can be detected using predictive models The following techniques are effective in detecting fraud. Auditors should ensure they use these, where appropriate. • Calculation of statistical parameters (e.g., averages, standard deviations, high/low values) – to identify outliers that could indicate fraud. • Classification – to find patterns amongst data elements. • Stratification of numbers – to identify unusual (i.e., excessively high or low) entries. • Digital analysis using Benford’s Law – to identify unexpected occurrences of digits in naturally occurring data sets. • Joining different diverse sources – to identify matching values (such as names, addresses, and account numbers) where they shouldn’t exist. • Duplicate testing – to identify duplicate transactions such as payments, claims, or expense report items. • Gap testing – to identify missing values in sequential data where there should be none. • Summing of numeric values – to identify control totals that may have been falsified. • Validating entry dates – to identify suspicious or inappropriate times for postings or data entry By leveraging the power of QlikView Discovery and data analysis technology, organizations can detect fraud sooner and reduce the negative impact of significant losses owing to fraud. Fraud is a serious financial drain on the healthcare systems in many jurisdictions, and it also drags down the effectiveness of providing care to those in need.
