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Indebted households profiling: a knowledge discovery from database approach
In order to build such risk profiles it is necessary to employ an approach that analyses data systematically in order to detect important relationships, interactions, dependencies and associations amongst the available continuous and categorical variables altogether and accurately generate profiles of most interesting household segments according to their credit risk.
A Data Mining framework to model Consumer Indebtedness with Psychological Factors
Modelling Consumer Indebtedness has proven to be a problem of complex nature.
Augmented Neural Networks for Modelling Consumer Indebtness
Consumer Debt has risen to be an important problem of modern societies, generating a lot of research in order to understand the nature of consumer indebtness, which so far its modelling has been carried out by statistical models.
Using Clustering to extract Personality Information from socio economic data
It has become apparent that models that have been applied widely in economics, including Machine Learning techniques and Data Mining methods, should take into consideration principles that derive from the theories of Personality Psychology in order to discover more comprehensive knowledge regarding complicated economic behaviours.
