Evaluation of empirical attributes for credit risk forecasting from numerical data

Augustinos Dimitras, Stelios Papadakis, Alexandros Garefalakis

    Research output: Contribution to journalArticlepeer-review


    In this research, the authors proposed a new method to evaluate borrowers' credit risk and quality of financial statements information provided. They use qualitative and quantitative criteria to measure the quality and the reliability of its credit customers. Under this statement, the authors evaluate 35 features that are empirically utilized for forecasting the borrowers' credit behavior of a Greek Bank. These features are initially selected according to universally accepted criteria. A set of historical data was collected and an extensive data analysis is performed by using non parametric models. Our analysis revealed that building simplified model by using only three out of the thirty five initially selected features one can achieve the same or slightly better forecasting accuracy when compared to the one achieved by the model uses all the initial features. Also, experimentally verified claim that universally accepted criteria can't be globally used to achieve optimal results is discussed.

    Original languageEnglish
    Pages (from-to)9-18
    Number of pages10
    JournalInvestment Management and Financial Innovations
    Issue number1
    Publication statusPublished - 2017


    • Computational intelligence
    • Credit risk
    • Management commentary
    • Management commentary index
    • Quantitative & qualitative criteria


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