| Issue |
4open
Volume 3, 2020
|
|
|---|---|---|
| Article Number | 9 | |
| Number of page(s) | 11 | |
| Section | Mathematics - Applied Mathematics | |
| DOI | https://doi.org/10.1051/fopen/2020010 | |
| Published online | 21 August 2020 | |
Research Article
Risk of fraud classification
1
Department of Statistics, University of Campinas, Sergio Buarque de Holanda, 651, CEP: 13083-859, Campinas, SP, Brazil
2
CPFL, Rod. Eng. Miguel Noel Nascentes Burnier, 1755 – Chácara Primavera, CEP: 13088-900, Campinas, SP, Brazil
* Corresponding author: thainass@outlook.com
Received:
17
March
2020
Accepted:
19
July
2020
In this article, we define consumers’ profiles of electricity who commit fraud. We also compare these profiles with users’ profiles not classified as fraudsters in order to determine which of these clients should receive an inspection. We present a statistically consistent method to classify clients/users as fraudsters or not, according to the profiles of previously identified fraudsters. We show that it is possible to use several characteristics to inspect the classification of fraud; those aspects are represented by the coding performed in the observed series of clients/users. In this way, several encodings can be used, and the client risk can be constructed to integrate complementary aspects. We show that the classification method has success rates that exceed 77%, which allows us to infer confidence in the methodology.
Key words: Bayesian Information Criterion / Partition Markov Models / Metric in Markov Processes
© J.E. García et al., Published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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