Tianyi Yang, Qi Xin, Xiaoan Zhan, Shikai Zhuang, Huixiang Li
{"title":"通过大数据和人工智能驱动的客户洞察和风险分析提升金融服务","authors":"Tianyi Yang, Qi Xin, Xiaoan Zhan, Shikai Zhuang, Huixiang Li","doi":"10.60087/jklst.vol3.n3.p53-62","DOIUrl":null,"url":null,"abstract":"The article discusses the integration of big data and artificial intelligence (AI) technologies in the financial sector, focusing on supervised learning for pricing models to enhance customer identification and targeting. It details the construction of customer feature systems, including attributes like debit and credit card transactions, loan applications, and online behavior. By leveraging AI, financial institutions aim to accurately profile customers, boost consumption, and improve price management, ultimately aiding risk management and loan approval decisions. The article also covers related work in financial risk monitoring and machine learning in credit risk modeling, highlighting advancements and challenges in these areas.","PeriodicalId":509244,"journal":{"name":"Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)","volume":"61 7","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ENHANCING FINANCIAL SERVICES THROUGH BIG DATA AND AI-DRIVEN CUSTOMER INSIGHTS AND RISK ANALYSIS\",\"authors\":\"Tianyi Yang, Qi Xin, Xiaoan Zhan, Shikai Zhuang, Huixiang Li\",\"doi\":\"10.60087/jklst.vol3.n3.p53-62\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The article discusses the integration of big data and artificial intelligence (AI) technologies in the financial sector, focusing on supervised learning for pricing models to enhance customer identification and targeting. It details the construction of customer feature systems, including attributes like debit and credit card transactions, loan applications, and online behavior. By leveraging AI, financial institutions aim to accurately profile customers, boost consumption, and improve price management, ultimately aiding risk management and loan approval decisions. The article also covers related work in financial risk monitoring and machine learning in credit risk modeling, highlighting advancements and challenges in these areas.\",\"PeriodicalId\":509244,\"journal\":{\"name\":\"Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)\",\"volume\":\"61 7\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.60087/jklst.vol3.n3.p53-62\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.60087/jklst.vol3.n3.p53-62","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
ENHANCING FINANCIAL SERVICES THROUGH BIG DATA AND AI-DRIVEN CUSTOMER INSIGHTS AND RISK ANALYSIS
The article discusses the integration of big data and artificial intelligence (AI) technologies in the financial sector, focusing on supervised learning for pricing models to enhance customer identification and targeting. It details the construction of customer feature systems, including attributes like debit and credit card transactions, loan applications, and online behavior. By leveraging AI, financial institutions aim to accurately profile customers, boost consumption, and improve price management, ultimately aiding risk management and loan approval decisions. The article also covers related work in financial risk monitoring and machine learning in credit risk modeling, highlighting advancements and challenges in these areas.