{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T17:01:35Z","timestamp":1781110895827,"version":"3.54.1"},"reference-count":42,"publisher":"IGI Global Scientific Publishing","issue":"1","license":[{"start":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T00:00:00Z","timestamp":1714953600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/creativecommons.org\/licenses\/by\/3.0\/deed.en_US"},{"start":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T00:00:00Z","timestamp":1714953600000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/creativecommons.org\/licenses\/by\/3.0\/deed.en_US"},{"start":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T00:00:00Z","timestamp":1714953600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/creativecommons.org\/licenses\/by\/3.0\/deed.en_US"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,6]]},"abstract":"<p>Consumer credit assessment has always been a crucial concern in the financial industry. It involves evaluating an individual's credit history and their ability to repay loans, playing a pivotal role in the risk management and lending decisions made by credit institutions. In the present landscape, traditional credit assessment methods confront various shortcomings. Firstly, they typically only consider static features and are unable to capture the dynamic changes in an individual's credit profile over time. Secondly, traditional methods struggle with processing complex time series data, failing to fully exploit the importance of time-related information. To address these challenges, we propose an innovative solution \u2013 the XGBoost-LSTM model optimized with the AdaBound algorithm. This hybrid model combines two powerful machine learning techniques, XGBoost and LSTM, to leverage both static and dynamic features effectively.<\/p>","DOI":"10.4018\/joeuc.343256","type":"journal-article","created":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T16:05:51Z","timestamp":1715011551000},"page":"1-24","source":"Crossref","is-referenced-by-count":6,"title":["Application of AdaBound-Optimized XGBoost-LSTM Model for Consumer Credit Assessment in Banking Industries"],"prefix":"10.4018","volume":"36","author":[{"given":"Lijuan","family":"Fan","sequence":"first","affiliation":[{"name":"School of Teacher Education, Weifang University of Science and Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changlin","family":"Wang","sequence":"additional","affiliation":[{"name":"Shandong University of Aeronautics, China & University of British Columbia, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhonghua","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Logistics, Linyi University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"JOEUC.343256-0","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAS53252.2022.9744714"},{"key":"JOEUC.343256-1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06695-z"},{"key":"JOEUC.343256-2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3166891"},{"key":"JOEUC.343256-3","doi-asserted-by":"crossref","unstructured":"ASL., G. S., Shamsi, K., Thulasiram, R. K., Akcora, C., & Leung, C. (2023). Deep learning-based credit score prediction: Hybrid LSTM-GRU model. 2023 IEEE Symposium Series on Computational Intelligence (SSCI).","DOI":"10.1109\/SSCI52147.2023.10371827"},{"key":"JOEUC.343256-4","doi-asserted-by":"publisher","DOI":"10.1038\/s41379-022-01073-z"},{"key":"JOEUC.343256-5","doi-asserted-by":"publisher","DOI":"10.1016\/j.frl.2021.102052"},{"key":"JOEUC.343256-6","doi-asserted-by":"crossref","unstructured":"Chakrabarti, K., & Chopra, N. (2022). Analysis and synthesis of adaptive gradient algorithms in machine learning: The case of AdaBound and MAdamSSM. 2022 IEEE 61st Conference on Decision and Control (CDC).","DOI":"10.1109\/CDC51059.2022.9992512"},{"key":"JOEUC.343256-7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2023.06.036"},{"key":"JOEUC.343256-8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-18907-4_53"},{"key":"JOEUC.343256-9","doi-asserted-by":"publisher","DOI":"10.1109\/ICAT57854.2023.10171259"},{"key":"JOEUC.343256-10","doi-asserted-by":"publisher","DOI":"10.1007\/s44230-023-00035-1"},{"key":"JOEUC.343256-11","first-page":"633","article-title":"A big data deep learning approach for credit card fraud detection. In Computer networks, big data and IoT","volume":"2021","author":"K.Illanko","year":"2022","journal-title":"Proceedings of ICCBI"},{"key":"JOEUC.343256-12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2021.06.023"},{"key":"JOEUC.343256-13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2021.12.024"},{"key":"JOEUC.343256-14","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/6584352"},{"key":"JOEUC.343256-15","doi-asserted-by":"crossref","unstructured":"Li, Q. (2023). 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