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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Azevedo, Caio da Silva [1] ; Goncalves, Rodrigo Franco [2] ; Gava, Vagner Luiz [3] ; Spinola, Mauro de Mesquita [1]
Total Authors: 4
[1] Univ Sao Paulo, Sao Paulo - Brazil
[2] Univ Paulista, Sao Paulo - Brazil
[3] Inst Pesquisas Tecnol, Sao Paulo - Brazil
Total Affiliations: 3
Document type: Journal article
Web of Science Citations: 0

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. (C) 2020 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 19/14011-0 - Machine learning for river flow modeling and flood prediction
Grantee:Caio da Silva Azevedo
Support type: Scholarships in Brazil - Master
FAPESP's process: 17/50343-2 - Institutional development plan in the area of digital transformation: advanced manufacturing and smart and sustainable cities (PDIp)
Grantee:Zehbour Panossian
Support type: Research Grants - State Research Institutes Modernization Program