The Advantage of Case-Tailored Information Metrics for the Development of Predictive Models, Calculated Profit in Credit Scoring

Autor:	Marcin Chlebus, Daniel Chrościcki
Rok vydání:	2022
Předmět:	credit scoring econometrics machine learning performance metrics model development EMP CP General Physics and Astronomy
Zdroj:	Entropy; Volume 24; Issue 9; Pages: 1218
ISSN:	1099-4300
Popis:	This paper compares model development strategies based on different performance metrics. The study was conducted in the area of credit risk modeling with the usage of diverse metrics, including general-purpose Area Under the ROC curve (AUC), problem-dedicated Expected Maximum Profit (EMP) and the novel case-tailored Calculated Profit (CP). The metrics were used to optimize competitive credit risk scoring models based on two predictive algorithms that are widely used in the financial industry: Logistic Regression and extreme gradient boosting machine (XGBoost). A dataset provided by the American Fannie Mae agency was utilized to conduct the study. In addition to the baseline study, the paper also includes a stability analysis. In each case examined the proposed CP metric that allowed us to achieve the most profitable loan portfolio.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e78ef8215427fad19b6532a7e734bc0f https://pubmed.ncbi.nlm.nih.gov/36141104 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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