Modelos analíticos para identificar patrones de delitos financieros: Una revisión sistemática de la literatura

Maritza Murillo Ortíz, Lillyana María Giraldo Marín, Herman Horacio Jaramillo Villegas, Carlos César Piedrahita Escobar

Producción científica: Contribución a una revistaArtículorevisión exhaustiva


One of the greatest challenges facing financial institutions today is the risk of financial crimes that are increasingly sophisticated and global in nature, considering the increasing trends of some types of modalities. For this reason, a systematic literature review on the subject was developed to find out which analytical models are the most used and we managed to detect anomalous situations. From this review, it was identified that, thanks to technology and supervised analytical models such as Support Vector Machine (SVM), Neural Networks, among others, many of the threats that exist in the market today can be considerably mitigated and in this way, it is important to prevent million-dollar losses, however, according to the literature, it is important to take into account that one of the main difficulties in detecting fraud or any other financial crime is unbalanced data, since this implies that the results generated probably show a bias towards the majority class and, in extreme cases, may completely ignore the minority class.

Título traducido de la contribuciónAnalytical models to identify financial crime patterns: a systematic literature review
Idioma originalEspañol
Páginas (desde-hasta)586-598
Número de páginas13
PublicaciónRISTI - Revista Iberica de Sistemas e Tecnologias de Informacao
EstadoPublicada - abr. 2022

Palabras clave

  • analytical models
  • Financial crimes
  • Machine Learning


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