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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2625-3577 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.978-3-658-40179-5978-3-658-40180-1Series ISSN 2625-3577 Series E-ISSN 2625-3615

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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

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Credit Scoring Methodologies,search overview, the logistic regression method is considered to be the standard of traditional credit scoring. On another hand, banks and financial companies develop expert systems for credit risk assessment. Expert systems do not use statistical models and are, mainly, based on a set of underwriting rules and procedures.

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Undergraduate Topics in Computer Sciencedevelopment 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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Building an Embedded System (First Pass),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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Application of AI in Credit Scoring Modeling978-3-658-40180-1Series ISSN 2625-3577 Series E-ISSN 2625-3615