Analysis of methods for generating classification rules applicable to cbkp_redit risk
Abstract:
Cbkp_redit risk is defined as the probability of loss due to non-compliance by the borrower with the required payments in relation to any type of debt. When financial institutions select their customers correctly, they can reduce their cbkp_redit risk. To achieve this, they use various classification methodologies to sort customers based on their risk, analyzing a set of variables such as reputation, leverage, income and so forth. The extensive analysis and processing of these variables is quite time-consuming, partly because the data to be analyzed are not homogeneous. In this paper, we present an alternative method that operates on nominal and numeric attributes, which allows obtaining a pbkp_redictive model that uses a reduced set of classification rules aimed at reducing cbkp_redit risk. When the number of rules used decreases, cbkp_redit analysts need less time to make their decisions, which will also result in better customer service. The methodology proposed here was applied to two databases of the UCI repository and two real databases of Ecuadorian banks that grant various types of cbkp_redit. The results obtained have been satisfactory. Finally, our conclusions are discussed and future research lines are suggested.
Año de publicación:
2017
Keywords:
Fuente:

Tipo de documento:
Other
Estado:
Acceso abierto
Áreas de conocimiento:
- Minería de datos
- Ciencias de la computación
Áreas temáticas:
- Funcionamiento de bibliotecas y archivos