A Benford’s Law based methodology for fraud detection in social welfare programs: Bolsa Familia analysis

Caio da Silva Azevedo, Rodrigo Franco Gonçalves, Vagner Luiz Gava and Mauro de Mesquita Spinola

Physica A: Statistical Mechanics and its Applications, 2021, vol. 567, issue C

Abstract: This paper aims to introduce a data science approach for guiding auditors to accurately select regions suspected of frauds in welfare programs benefits distribution. The technique relies on Newcomb–Benford’s Law (NBL) for significant digits. It has been analysed Bolsa Familia data from Federal Government Transparency Portal, a tool that aims to increase fiscal transparency of the Brazilian Government through open budget data. The methodology consists in submit four data samples to null hypothesis statistical methods and thereby evaluate the conformity with the law as well as the summation test which looks for excessively large numbers in the dataset. Research results in this paper are that beneficiaries’ cash transfer per se is not a good test variable. Besides, once payment data are grouped by municipalities, they fit NBL, and finally, when submitted to the summation test, the distribution of the Bolsa Familia payments in several municipalities shows some fraud evidence. In this sense, we conclude the NBL can be an appropriate method to fraud investigation of welfare programs’ benefits distribution having beneficiaries’ payment geographically grouped.

Keywords: Big data analytics; Newcomb–Benford’s Law; Statistical antifraud analysis; Anomaly detection; Bolsa Familia; Social welfare programs (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:567:y:2021:i:c:s0378437120309249

DOI: 10.1016/j.physa.2020.125626

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