Classification of the risk in the new financing framework of the Deposit Guarantee Systems in Europe: K-Means Cluster Analysis and Soft Computing

Abstract

The guidelines published by the European Banking Authority in 2015 about the contributions to the Deposit Guarantee Systems, establish two approaches to classify the member entities' risk: the bucket method and the sliding scale method, allowing freedom to every Member State to decide which methodology to use. In this work, using the bucket method with two different clustering techniques, k-means and soft computing, in a sample that represents more than 90% of the deposits covered in the Spanish banking system during the 2008 to 2014 period, the differences in the distribution of the Deposit Guarantee Fund risk and in the entities' contributions is analyzed. The obtained results reveal important differences. Consequently, the technique chosen by each country will determine the contributions regime.

Sentiment Analysis of Shared Tweets on Global Warming on Twitter with Data Mining Methods: A Case Study on Turkish Language

“…In pattern recognition, the K-nearest neighbor algorithm (or K-NN for short) is a nonparametric method used for classification and regression. It is based on the idea that instance must be in a close distance when compared to its closest neighbors [ 24 ].…”

“…Naive Bayes, one of the techniques of supervised machine learning, is subjected to K-NN [ 24 , 36 ] and SVM classification algorithms [ 37 , 38 ]. During the procedure of classification, WEKA machine learning tool is used.…”

Econometric Analysis of Disequilibrium Relations Between Internet Finance and Real Economy in China

“…Guo et al [11] used the internet finance development index of Peking University to conduct the research and found out that all traditional finance departments, infrastructures and local economic development have significant influence on the development of internet finance. Gómez et al [12] analyzed the differences in the distribution of the Deposit Guarantee Fund risk and in the entities' contributions by using the bucket method with two different clustering techniques, k-means and soft computing, in a sample that represents more than 90% of the deposits covered in the Spanish banking system during the 2008 to 2014 period. Based on social network analysis method, Huang et al [13] found that there were overflow effects of internet finance development existing among different provinces in China.…”