Titlebook: Application of AI in Credit Scoring Modeling; Bohdan Popovych Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive l

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Conclusion,. The research question was to determine which method more accurately predicts individual defaults based on the comparison of AI and logit models. In order to answer the research question, theoretical concepts regarding credit scoring were introduced.

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Book 2022es. The comparison of logistic regression, decision tree, and random forest showed that machine learning methods are able to predict credit defaults of individuals more accurately than the logit model. Furthermore, it was demonstrated how random forest and decision tree models were more sensitive in detecting default borrowers.

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Introduction,development of credit risk management can improve the competitiveness of European banks and financial institutions. The necessity of more accurate credit risk modeling motivates researchers to discover new methods in credit assessment.

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Theoretical Concepts of Credit Scoring, discriminant analysis for the classification of groups based on the independent variables. Fisher’s research defined the discriminant function that was based on a linear combination of predictors that discriminates the predicted variable.

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Credit Scoring Methodologies,ch, where a linear combination of independent features is used for the representation of a dependent variable. In credit scoring, independent parameters are risk factors, and a dependent variable is PD or creditworthiness level. Discriminant analysis is the first linear approach that was applied for

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Conclusion,. The research question was to determine which method more accurately predicts individual defaults based on the comparison of AI and logit models. In order to answer the research question, theoretical concepts regarding credit scoring were introduced.

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