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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Um escore de risco para classificação de transações suspeitas de lavagem de dinheiro via regressão ordinal

Borba, Maria Clara Vieira 05 December 2017 (has links)
Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Estatística, 2017. / Submitted by Raquel Viana (raquelviana@bce.unb.br) on 2018-07-24T18:00:26Z No. of bitstreams: 1 2017_MariaClaraVieiraBorba.pdf: 413555 bytes, checksum: 153a9545cd4a36324babc40155eeb41e (MD5) / Approved for entry into archive by Raquel Viana (raquelviana@bce.unb.br) on 2018-07-24T19:34:37Z (GMT) No. of bitstreams: 1 2017_MariaClaraVieiraBorba.pdf: 413555 bytes, checksum: 153a9545cd4a36324babc40155eeb41e (MD5) / Made available in DSpace on 2018-07-24T19:34:37Z (GMT). No. of bitstreams: 1 2017_MariaClaraVieiraBorba.pdf: 413555 bytes, checksum: 153a9545cd4a36324babc40155eeb41e (MD5) Previous issue date: 2018-07-24 / O objetivo deste trabalho foi apresentar um escore de risco para classificação de transação financeiras suspeitas de lavagem de dinheiro. Esse escore é obtido a partir dos resultados de um modelo de regressão logística ordinal cumulativo. O escore proposto foi ilustrado em dados reais visando classificar os associados quanto o seu risco de ter realizado movimentações com indícios do crime de lavagem de dinheiro, sendo que o mesmo se mostrou uma ferramenta útil para ranquear e classificar esses associados. / The purpose of this paper is to present a risk score to classify suspected money laundering transactions. This score is obtained from the results of a cumulative ordinal logistic regression model. The proposed score was illustrated using real data in order to classify the associates as to their risk of having carried out movements with indications of the crime of money laundering, and this proved to be an useful tool to rank and classify these associates.

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