US IRS to identify tax evaders, reduce fraud through analytics

By eGov Innovation Editors | Dec 14, 2011

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The US Internal Revenue Service (IRS) will be using SAS Analytics to help detect, prevent and resolve criminal and civil noncompliance with tax law.

The $6.25-million contract will give the IRS Return Review Program (RRP) access to many SAS technologies, including SAS Fraud Framework for Government, SAS Social Network Analysis and SAS' industry-leading data integration, data mining and business intelligence technologies.

To analyze massive amounts of data, the IRS will have big data analytics capabilities through SAS In-Database functionality, SAS Scoring Accelerator for Greenplum and SAS Grid Manager. SAS combines an array of technologies to tackle fraud, waste and improper payments in many areas of government – from detecting collusive patterns in entitlement programs such as Medicare and Medicaid to purchase-card fraud, bid-rigging and terrorist financing.

The agency's RRP system is designed to help reduce the $345 billion tax gap – the difference between what taxpayers owe and what they pay voluntarily and on time.

"With SAS, the IRS can reduce the number and amount of fraudulent tax refunds, discover emerging fraud schemes and increase tax collections to shrink the tax gap."

Tax credit fraud ranges from child adoption or earned income tax credits to ghost tax-return preparers and identity theft.

SAS scores tax returns through a hybrid approach of business rules, anomaly detection, predictive modeling and social network analysis. Users can easily set up business rules that detect possible fraud and immediately alert investigators or auditors to suspect returns.

The software also searches data for anomalies that could indicate fraud or error. Predictive modeling uses historical behavioral information to identify suspicious behaviors similar to known fraud patterns. Social network analysis uncovers hidden relationships or linkages that suggest collusion and organized fraud rings.