# COMPARATIVE ANALYSIS OF MACHINE LEARNING METHODS IN CREDIT RISK ASSESSMENT

**Author(s):**
Tshub V. S.

**Rubric:**
Information technology

**DOI: **
10.21777/2500-2112-2023-3-81-92

**Release:**
2023-3 (44)

**Pages:**
81-92

**Keywords:**
artificial intelligence, machine learning, dropout, short-term memory, correlation matrix, credit risks

**Annotation:**
The article describes the comparative effectiveness of automated credit risk assessment by modern machine learning
methods (Naive Bayes classifier, k-nearest neighbors, logistic regression, “random forest” and deep neural
network). A database was modeled from available open sources, samples were made, and data was preprocessed.
Machine learning models have been trained and tested, and compared by quality metrics. Based on the test
results, the parameters of the machine learning model were optimized by a complete search of combinations of
statistical models (rule-based method, k-nearest neighbor method, logistic regression, discriminant analysis, naive
Bayes classifier, neural networks and decision trees). Each of the traditional classifiers (naive Bayes classifier,
k-nearest neighbor method, logistic regression, “random forest”, deep neural network) was improved with the
use of standardization mechanisms and dimensionality reduction by the principal component method. Based on
the results of a comparative analysis of machine learning models, the best result was demonstrated by a neural
network model with optimized parameters, which provided the best indicators for all evaluation metrics.

**Bibliography:**
Tshub V..S.. COMPARATIVE ANALYSIS OF MACHINE LEARNING METHODS IN CREDIT RISK ASSESSMENT // Education Resources and Technologies. – 2023. – № 3 (44). – С. 81-92. doi: 10.21777/2500-2112-2023-3-81-92