Benford's Law, data mining, and financial fraud: a case study in New York State Medicaid data

被引:1
作者
Little, B. [1 ]
Rejesus, R. [2 ]
Schucking, M. [3 ]
Harris, R. [4 ]
机构
[1] Tarleton State Univ, Texas Data Min Res Inst, Dept Math Phys & Engn, Stephenville, TX USA
[2] North Carolina State Univ, Dept Agr & Resource Econ, Raleigh, NC 27695 USA
[3] Qinetiq North Amer Planning Syst Inc, Data Min & Informat Sci Div, Stephenville, TX USA
[4] New York Comptrollers Off, Albany, NY USA
来源
DATA MINING IX: DATA MINING, PROTECTION, DETECTION AND OTHER SECURITY TECHNOLOGIES | 2008年 / 40卷
关键词
Benford's Law; cluster analysis; ensemble multivariate technique;
D O I
10.2495/DATA080191
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Benford's Law was first described by an astronomer in 188 1, but physicist Frank Benford lent his name to the property in a mathematical treatise published in 1938. Behaviour of numbers described by the Law defies intuition, demonstrating that one is the most frequent (30.1%), and nine is the least frequent (4.6%). The property holds for a wide variety of numbers, including but not limited to: stock indices, river lengths, road numbers, etc. Departures from the classic Benford distribution are linked to anomalies, specifically in financial data where the property has been successfully employed in financial audits. The limitation of Benford's Law is that it identifies a relatively large pool of "candidate" anomalies that must be manually evaluated. In the present analysis of Medicaid data, multivariate cluster analysis in multiple tandem analyses is used to winnow the number of anomalies to a pool of high probability anomalies for evaluation. This approach makes the application of Benford's Law more practical.
引用
收藏
页码:195 / +
页数:3
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