| Full text | |
| Author(s): |
Azevedo, Caio da Silva
[1]
;
Goncalves, Rodrigo Franco
[2]
;
Gava, Vagner Luiz
[3]
;
Spinola, Mauro de Mesquita
[1]
Total Authors: 4
|
| Affiliation: | [1] Univ Sao Paulo, Sao Paulo - Brazil
[2] Univ Paulista, Barueri - Brazil
[3] Inst Pesquisas Tecnol, Sao Paulo - Brazil
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | METHODSX; v. 8, 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
Benford Law (BL) states that the occurrence of significant digits in many natural and human phenomena data sets are not uniformly scattered, as one could naively expect, but follow a logarithmic-type distribution. Here, we present a method that consists of the use of BL analysis over first and first-two digits, three statistical conformity tests - Z-statistics, Mean Absolute Deviation (MAD) and Chi-square (chi 2) as well as the summation test which looks for excessively large numbers, having fraud detection as one of its application. We developed the method for fraud detection in the case of the Brazilian Bolsa Familia welfare program. In this case, we submitted four periods of Brazilian welfare program payments to the method with a dataset of 13,442,529 records. We provide a practical implementation of the method based on open-source R library released on a public repository. Furthermore, code implementation of the algorithm as well as datasets are freely available. Advantages of the algorithm are listed below: The method was developed based on open source libraries The technique is simple, rapid and ease of use Easily applicable to other social welfare program auditing (C) 2021 The Author(s). Published by Elsevier B.V. (AU) | |
| 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 Opportunities: | Research Grants - State Research Institutes Modernization Program |
| FAPESP's process: | 19/14011-0 - Machine learning for river flow modeling and flood prediction |
| Grantee: | Caio da Silva Azevedo |
| Support Opportunities: | Scholarships in Brazil - Master |