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Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/54299
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dc.contributor.authorNaidovich, O.-
dc.contributor.authorNedzved, A.-
dc.contributor.authorShiping Ye-
dc.coverage.spatialМинскen_US
dc.date.accessioned2024-02-21T06:40:36Z-
dc.date.available2024-02-21T06:40:36Z-
dc.date.issued2023-
dc.identifier.citationNaidovich, O. Survival analysis in credit scoring / O. Naidovich, A. Nedzved, Shiping Ye // Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023) : Proceedings of the 16th International Conference, October 17–19, 2023, Minsk, Belarus / United Institute of Informatics Problems of the National Academy of Sciences of Belarus. – Minsk, 2023. – P. 158–161.en_US
dc.identifier.urihttps://libeldoc.bsuir.by/handle/123456789/54299-
dc.description.abstractIn the domain of credit risk assessment, innovative approaches have emerged to address the challenge of predicting loan default probabilities. This article explores Survival Analysis, a statistical method capable of predicting the timing of loan repayments and distinguishing between completed repayments and unpaid loans, treating them as censored events. By integrating Survival Analysis, financial institutions can enhance their ability to forecast repayment timelines, minimize losses from non-performing loans, optimize cash flow management, refine credit collection strategies. The primary goal of this article is to investigate the utility of survival models in estimating Probability of Default (PD) and developing credit scorecards.en_US
dc.language.isoenen_US
dc.publisherBSUen_US
dc.subjectматериалы конференцийen_US
dc.subjectsurvival analysisen_US
dc.subjectcredit risk modelingen_US
dc.subjectprobability of defaulten_US
dc.subjectlogistic regressionen_US
dc.titleSurvival analysis in credit scoringen_US
dc.typeArticleen_US
Appears in Collections:Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023)

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