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Synopsis
The scope of this study is to investigate the capability of AI methods to accurately detect and predict credit risks based on retail borrowers' features. 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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About the Author
MA Bohdan Popovych is a data scientist and a researcher in quantitative finance. The main scientific focus of the author is application of advanced analytics and artificial intelligence in finance and economics.
From the Back Cover
The scope of this study is to investigate the capability of AI methods to accurately detect and predict credit risks based on retail borrowers' features. 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.
About the author
MA Bohdan Popovych is a data scientist and a researcher in quantitative finance. The main scientific focus of the author is application of advanced analytics and artificial intelligence in finance and economics.
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Application of AI in Credit Scoring Modeling (eng)
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Application of AI in Credit Scoring Modeling
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