Volume 3, 2020
|Number of page(s)||11|
|Section||Mathematics - Applied Mathematics|
|Published online||21 August 2020|
Risk of fraud classification
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: firstname.lastname@example.org
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.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.