Abstract: In this study, the clients who applied to a bank branch for loan were analyzed through data mining. The study was composed of the information such as amounts of loans received by personal and SME clients working with the bank branch, installment numbers, number of delays in loan installments, payments available in other banks and number of banks to which they are in debt between 2010 and 2013. The client risk profile was examined through Classification and Regression Tree (CART) analysis, one of the decision tree classification methods. At the end of the study, 5 different types of customers have been determined on the decision tree. The classification of these types of customers has been created with the rating of those posing a risk for the bank branch and the customers have been classified according to the risk ratings.
Abstract: This article analyses the relationship between
sovereign credit risk rating and gross domestic product for Central
and Eastern European Countries for the period 1996 – 2010. In order
to study the metioned relationship, we have used a numerical
transformation of the risk qualification, thus: we marked 0 the lowest
risk; then, we went on ascending, with a pace of 5, up to the score of
355 corresponding to the maximum risk. The used method of analysis
is that of econometric modelling with EViews 7.0. programme. This
software allows the analysis of data into a pannel type system,
involving a mix of periods of time and series of data for different
entities. The main conclusion of the work is the one confirming the
negative relationship between the sovereign credit risk and the gross
domestic product for the Central European and Eastern countries
during the reviewed period.