Evaluation of empirical attributes for credit risk forecasting from numerical data

  • Published March 31, 2017
  • Author(s)
    Augustinos I. Dimitras , Stelios Papadakis ,
    Alexandros Garefalakis
  • DOI
  • Article Info
    Volume 14 2017, Issue #1, pp. 9-18
  • Cited by
    5 articles

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This work is licensed under a Creative Commons Attribution 4.0 International License

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.

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    • Fig. 1. Tenfold cross-validation
    • Table 1. Key steps in a bank’s loan decision
    • Table 2. Areas of business analysis
    • Table 3. The selected attributes presented in descending order according to their |r| value