CoMoVi: a Framework for Data Transformation in Credit Behavioral Scoring Applications Using Model Driven Architecture

The stage of transforming data in knowledge discovery projects is costly, in general, it takes between 50 and 80% of total project time. This step is a complex task that demands from database designers a strong interaction with experts that have a broad knowledge of the application domain, making the task prone to error. The activities of that border region require a conjugation of database, statistics and system analysis competences. These competences are not ordinarily found in the same project team, whether in academia or in professional environment. The frameworks that aim to systemize this stage have significant limitations when applied to Credit Behavioral Scoring solutions. This paper proposes CoMoVi, a framework inspired in the Model Driven Architecture to systemize this stage in Credit Behavioral Scoring solutions. CoMoVi is composed by a meta-model which maps the domain concepts and a set of transformation rules. In order to validate the proposed framework, a comparative study of performance between frameworks found in literature and the proposed framework applied to a database of a known benchmark was performed. Student’s one-tailed paired ttest showed that CoMoVi gives better performance to a Multilayer Perceptron Neural Network with a confidence level of 95%. Keywords—Meta-Modeling; Model Driven Architecture; Credit Behavioural Scoring; Knowledge Discovery