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Please use this identifier to cite or link to this item: https://elib.bsu.by/handle/123456789/306222
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dc.contributor.authorNaidovich, Oleg
dc.contributor.authorNedzved, Alexander
dc.contributor.authorYe, Shiping
dc.date.accessioned2023-12-12T12:42:12Z-
dc.date.available2023-12-12T12:42:12Z-
dc.date.issued2023
dc.identifier.citationPattern Recognition and Information Processing (PRIP’2023). Artificial Universe: New Horisont : Proceedings of the 16 th International Conference, Belarus, Minsk, October 17–19, 2023 / Belarusian State University : eds. A. Nedzved, A. Belotserkovsky. – Minsk : BSU, 2023. – Pp. 158-161.
dc.identifier.isbn978-985-881-522-6
dc.identifier.urihttps://elib.bsu.by/handle/123456789/306222-
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
dc.language.isoen
dc.publisherMinsk : BSU
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Кибернетика
dc.subjectЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Математика
dc.titleSurvival analysis in credit scoring
dc.typeconference paper
Appears in Collections:2023. Pattern Recognition and Information Processing (PRIP’2023). Artificial Intelliverse: Expanding Horizons

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