{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:49:27Z","timestamp":1701478167442},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684444","type":"print"},{"value":"9781643684451","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,30]]},"abstract":"<jats:p>At present, the homogenization of banking products and the vigorous development of internet finance have intensified the competition among banks. Customers are the core assets of banks, whose size and loyalty is crucial to any bank. Loyal customer\u2019s repeat purchases or recommending products to people around creates higher value for banks. Therefore, in order to improve customer loyalty, a method of identifying customer loyalty is urgently needed which prioritizes providing more personalized services for loyal customers. Based on bank\u2019s long-term customer resource data, this paper divides customer groups by means of feature selection and data processing, compares the experimental results of multiple machine learning models such as GBDT, and selects the optimal XGBoost model to predict customer\u2019s long-term loyalty to banks, in order to predict potential customer churn for banks, attempt to retain high-value customers as much as possible, and to increase potential revenue.<\/jats:p>","DOI":"10.3233\/faia230865","type":"book-chapter","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:55:17Z","timestamp":1701446117000},"source":"Crossref","is-referenced-by-count":0,"title":["Analysis and Prediction of Bank Customer Loyalty Based on XGBoost Algorithm"],"prefix":"10.3233","author":[{"given":"Yuyan","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Business, Hohai University, Nanjing, China"}]},{"given":"Ke","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Business, Hohai University, Nanjing, China"}]},{"given":"Ting","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Business, Hohai University, Nanjing, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Advances in Artificial Intelligence, Big Data and Algorithms"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230865","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:55:19Z","timestamp":1701446119000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230865"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,30]]},"ISBN":["9781643684444","9781643684451"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230865","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,30]]}}}