دورية أكاديمية

Validation and Implementation of Customer Classification System Using Machine Learning

التفاصيل البيبلوغرافية
العنوان: Validation and Implementation of Customer Classification System Using Machine Learning
اللغة: English
المؤلفون: Hyemin Yoon (ORCID 0009-0006-2693-759X), HyunJin Kim, Sangjin Kim (ORCID 0000-0003-2824-0850)
المصدر: Measurement: Interdisciplinary Research and Perspectives. 2024 22(2):131-140.
الإتاحة: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 10
تاريخ النشر: 2024
نوع الوثيقة: Journal Articles
Reports - Research
Descriptors: Classification, Artificial Intelligence, Prediction, Decision Making, Computer Software, Validity, Banking, Evaluation Methods, Models, Comparative Analysis, Scores, Financial Services, Algorithms, Foreign Countries
مصطلحات جغرافية: South Korea
DOI: 10.1080/15366367.2023.2246111
تدمد: 1536-6367
1536-6359
مستخلص: We have maintained the customer grade system that is being implemented to customers with excellent performance through customer segmentation for years. Currently, financial institutions that operate the customer grade system provide similar services based on the score calculation criteria, but the score calculation criteria vary from the financial institution to financial institution. In this study, we create a machine learning prediction model using items and added items that are based on the current customer grade of our bank,- and the purpose is an optimal model that considers the adequacy of existing variables and the validity of additional variables through comparison between models. Using Lasso, Elastic net and Multinomial Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine, we propose that the best model be found and gradually applied to customer grade calculation criteria.
Abstractor: As Provided
Entry Date: 2024
رقم الأكسشن: EJ1422111
قاعدة البيانات: ERIC
الوصف
تدمد:1536-6367
1536-6359
DOI:10.1080/15366367.2023.2246111