Title
Client evaluation decision models in the credit scoring tasks
Authors
[ 1 ] Wydział Techniczny, Akademia im. Jakuba z Paradyża | [ P ] employee
Scientific discipline (Law 2.0)
Year of publication
2020
Published in
Article type
scientific article / paper
Publication language
english
Keywords
EN
- decision supportcredit scoringmachine learningclassificationfeature selection
Abstract
EN One of the important decision-making problems of modern financial institutions is credit scoring, which involves assessing credit risk. Decision-making models based on classifiers and feature selection methods that reduce the complexity of a decision problem by limiting the number of conditional attributes find use in such problems. The article examines the effectiveness of various combinations of classifiers and feature selection methods in the problem of credit risk assessment. The results of the conducted research indicate that for the considered set of data on cash loans granted, the Correlation-based Feature Selection method is the best method among the considered ones, and the Random Forest is the most effective classifier.
Date of online publication
2020
Pages (from - to)
3301 - 3309
License type
CC BY-NC-ND (attribution - noncommercial - no derivatives)
Open Access Mode
open journal
Open Access Text Version
final published version
Release date
10.2020 (Date presumed)
Date of Open Access to the publication
at the time of publication
Ministry points / journal
5
Ministry points / journal in years 2017-2021
5
Ministry points / conference (CORE)
70
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