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.
|Title of host publication||Information Management and Big Data - 5th International Conference, SIMBig 2018, Proceedings|
|Editors||Hugo Alatrista-Salas, Denisse Muñante, Juan Antonio Lossio-Ventura|
|Number of pages||9|
|State||Published - 1 Jan 2019|
|Event||Communications in Computer and Information Science - |
Duration: 1 Jan 2019 → …
|Name||Communications in Computer and Information Science|
|Conference||Communications in Computer and Information Science|
|Period||1/01/19 → …|
Bibliographical notePublisher Copyright:
© 2019, Springer Nature Switzerland AG.
- Finance profiles
- Fuzzy C-means
- Radial basis function networks
- Risk detection