Building a Credit Scoring Model Based on the Type of Target Variable

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dc.contributor.authorOrdabayeva, Zhanna
dc.contributor.authorMoldagulova, Aiman N.
dc.contributor.authorRiza, Ibrahim
dc.date.accessioned2026-01-24T12:20:56Z
dc.date.available2026-01-24T12:20:56Z
dc.date.issued2023
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description2023 IEEE International Conference on Smart Information Systems and Technologies, SIST 2023 -- 2023-05-04 through 2023-05-06 -- Astana -- 192082
dc.description.abstractWith the rapid development of big data and internet technology, big data financial platform companies collect and organize massive data through their own platforms, improve credit scoring parameters, and use machine learning methods to conduct comprehensive and scientific credit scoring assessments. Thus, banks in the construction of credit scoring face big problems. Based on the limitations of the existing system and methods of personal credit score, it is necessary to study personal credit score based on machine learning methods, improve the parameters and scoring system of personal credit score, clarify data collection channels, and use dynamic desensitization technology to desensitize data, LOF test method to test outliers' data and a random forest method to complete missing data values. You then use the gradient boosting decision tree method to view the important indicators, process the tested indicators with a scorecard model based on logistic regression, and derive a personal credit score. Finally, the model is tested with a BP neural network and the model is used to predict the level of personal credit. The study shows that machine learning can further improve the accuracy of individuals' credit ratings and provide the scientific basis and reference for credit ratings of commercial banks. © 2023 IEEE.
dc.identifier.doi10.1109/SIST58284.2023.10223505
dc.identifier.endpage36
dc.identifier.isbn9798350335040
dc.identifier.scopus2-s2.0-85171988529
dc.identifier.scopusqualityN/A
dc.identifier.startpage31
dc.identifier.urihttps://doi.org/10.1109/SIST58284.2023.10223505
dc.identifier.urihttps://hdl.handle.net/20.500.12868/4683
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20260121
dc.subjectbig data
dc.subjectBP neural network
dc.subjectcredit scoring
dc.subjectdata desensitization
dc.subjectdecision tree
dc.subjectlogistic regression
dc.subjectmachine learning
dc.titleBuilding a Credit Scoring Model Based on the Type of Target Variable
dc.typeConference Object

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