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    政大機構典藏 > 資訊學院 > 資訊科學系 > 期刊論文 >  Item 140.119/73379


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    題名: Data Mining Based Tax Audit Selection: A Case Study of a Pilot Project at the Minnesota Department of Revenue
    作者: 徐國偉
    Hsu, Kuo-Wei;Pathak, Nishith;Srivastava, Jaideep;Tschida, Greg;Bjorklund, Eric
    貢獻者: 資科系
    日期: 2015
    上傳時間: 2015-02-09 14:22:40 (UTC+8)
    摘要: 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.
    關聯: Real World Data Mining Applications, Springer International Publishing,17, 221-245
    資料類型: article
    DOI 連結: http://dx.doi.org/10.1007/978-3-319-07812-0_12
    DOI: 10.1007/978-3-319-07812-0_12
    顯示於類別:[資訊科學系] 期刊論文

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