Data mining algorithms for risk detection in bank loans

Alvaro Talavera, Luis Cano, David Paredes, Mario Chong

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva


This article proposes a new approach on detection of fraudulent credit operations applying computational intelligence techniques. We use a dataset of historical data of customers from a financial entity and we split it to train a classification and clustering algorithm. We train a radial basis function network to classify clients that commit or not credit fraud. Then, we build a Fuzzy c-means clustering to group data points to create customer profiles. This algorithm has the capacity of grouping the data inside clusters and assigning a degree of membership to the points outside the clusters. Subsequently, the trained classification algorithm is applied to the clusters to provide additional information about customer profiles. We demonstrate good performance for fraudulent credit operations and identification of customer profiles.
Idioma originalInglés
Título de la publicación alojadaInformation Management and Big Data - 5th International Conference, SIMBig 2018, Proceedings
EditoresHugo Alatrista-Salas, Denisse Muñante, Juan Antonio Lossio-Ventura
Número de páginas9
ISBN (versión digital)9783030116798
EstadoPublicada - 1 ene. 2019
EventoCommunications in Computer and Information Science -
Duración: 1 ene. 2019 → …

Serie de la publicación

NombreCommunications in Computer and Information Science
ISSN (versión impresa)1865-0929


ConferenciaCommunications in Computer and Information Science
Período1/01/19 → …

Nota bibliográfica

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

Palabras clave

  • Finance profiles
  • Fuzzy C-means
  • Radial basis function networks
  • Risk detection


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