Data Mining Based Tax Audit Selection: A Case Study of a Pilot Project at the Minnesota Department of Revenue

Abstract

We present a case study of a pilot project that was developed to evaluate the use of data mining in audit selection for the Minnesota Department of Revenue (DOR). The Internal Revenue Service (IRS) estimated the gap between revenue owed and revenue collected for 2001 to be approximately $345 billion, of which they were able to recover only $55 billion, and the estimated gap for 2006 was approximately $450 billion, of which the IRS was able to recover only $65 billion. It is critical for the government to reduce the gap and the fundamental process for doing so is audit selection. We present a data mining based approach that was used to improve the audit selection process at the DOR. We describe the manual audit selection process used at the time of the pilot project for Sales and Use taxes, discuss the data from various sources, address issues regarding feature selection, and explain the data mining techniques used. Results from the pilot project revealed that the data mining based approach can increase efficiency in the audit selection process. We also report results from actual field audits performed by auditors at the DOR, and results validated the usefulness of the data mining based approach for audit selection. The impact of the pilot project would be a refinement of the manual audit selection process and tax assessment procedures for other types of taxes.