Data mining algorithms for risk detection in bank loans

Alvaro Talavera, Luis Cano, David Paredes, Mario Chong

Resultado de la investigación: Capítulo del libro/informe/acta de congresoCapítulo de libro

Resumen

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 alojadaCommunications in Computer and Information Science
Páginas151-159
Número de páginas9
ISBN (versión digital)9783030116798
DOI
EstadoPublicada - 1 ene. 2019
EventoCommunications in Computer and Information Science -
Duración: 1 ene. 2019 → …

Serie de la publicación

NombreCommunications in Computer and Information Science
Volumen898
ISSN (versión impresa)1865-0929

Conferencia

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

Palabras clave

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

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