iCARE: A framework for big data-based banking customer analytics

被引:34
|
作者
Sun, N. [1 ]
Morris, J. G. [2 ]
Xu, J. [1 ]
Zhu, X. [1 ]
Xie, M. [1 ]
机构
[1] IBM Res China, Dept Business Analyt & Optimizat, Beijing 100193, Peoples R China
[2] IBM Global Business Serv, New York, NY 10016 USA
关键词
D O I
10.1147/JRD.2014.2337118
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
The amount of data stored by banks is rapidly increasing and provides the opportunity for banks to conduct predictive analytics and enhance its businesses. However, data scientists are facing large challenges, handling the massive amount of data efficiently and generating insights with real business value. In this paper, the Intelligent Customer Analytics for Recognition and Exploration (iCARE) framework is presented to analyze banking customer behaviors from banking big data, through analytical modeling methodologies and techniques designed for a key business scenario. Combining IBM software platforms and big data processing power with customized data analytical models, the iCARE solution provides deeper customer insights to satisfy a bank's specific business need and data environment. The advantages of the iCARE framework have been confirmed in a real case study of a bank in southeast China. In this case, iCARE helps generate insights for active customers based on their transaction behavior, using close to 20 terabytes of data.
引用
收藏
页数:9
相关论文
共 50 条
  • [1] Customer experience management in the age of big data analytics: A strategic framework
    Holmlund, Maria
    Van Vaerenbergh, Yves
    Ciuchita, Robert
    Ravald, Annika
    Sarantopoulos, Panagiotis
    Ordenes, Francisco Villarroel
    Zaki, Mohamed
    [J]. JOURNAL OF BUSINESS RESEARCH, 2020, 116 : 356 - 365
  • [2] Data-based drivers of big data analytics utilization: moderating role of IT proactive climate
    Seifian, Atiyeh
    Bahrami, Mohamad
    Shokouhyar, Sajjad
    Shokoohyar, Sina
    [J]. BENCHMARKING-AN INTERNATIONAL JOURNAL, 2023, 30 (10) : 4461 - 4486
  • [3] Customer data analytics: privacy settings for 'Big Data' business
    Leonard, Peter
    [J]. INTERNATIONAL DATA PRIVACY LAW, 2014, 4 (01) : 53 - 68
  • [4] Big data analytics for supply chain relationship in banking
    Hung, Jui-Long
    He, Wu
    Shen, Jiancheng
    [J]. INDUSTRIAL MARKETING MANAGEMENT, 2020, 86 : 144 - 153
  • [5] Agile Visual Analytics for Banking Cyber "Big Data"
    Jonker, David
    Langevin, Scott
    Schretlen, Peter
    Canfield, Casey
    [J]. 2012 IEEE CONFERENCE ON VISUAL ANALYTICS SCIENCE AND TECHNOLOGY (VAST), 2012, : 299 - 300
  • [6] Impact of big data analytics on banking: a case study
    He, Wu
    Hung, Jui-Long
    Liu, Lixin
    [J]. JOURNAL OF ENTERPRISE INFORMATION MANAGEMENT, 2023, 36 (02) : 459 - 479
  • [7] A Framework-Based Approach to Utility Big Data Analytics
    Zhu, Jun
    Zhuang, Eric
    Fu, Jian
    Baranowski, John
    Ford, Andrew
    Shen, James
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2016, 31 (03) : 2455 - 2462
  • [8] Banking in Terms of Deposit Prediction Based on Machine Learning and Big Data Analytics
    Alessa, Nourah
    Majdua, Amal
    Alshehri, Sharifah
    Alhawiti, Maryam
    Aljohani, Resan
    Alhakamy, A'aeshah
    [J]. 2023 11TH INTERNATIONAL CONFERENCE ON CONTROL, MECHATRONICS AND AUTOMATION, ICCMA, 2023, : 69 - 74
  • [9] A Review of Big Data Analytics for Customer Relationship Management
    Perera, W. K. R.
    Kulawansa, K. A. Dilini T.
    [J]. 2018 3RD INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY RESEARCH (ICITR), 2018,
  • [10] An Automated Cloud-based Big Data Analytics Platform for Customer Insights
    Han, Liangxiu
    Haleem, Muhammad Salman
    Sobeih, Tam
    Liu, Ying
    Soroka, Anthony
    Han, Lianghao
    [J]. 2017 IEEE INTERNATIONAL CONFERENCE ON INTERNET OF THINGS (ITHINGS) AND IEEE GREEN COMPUTING AND COMMUNICATIONS (GREENCOM) AND IEEE CYBER, PHYSICAL AND SOCIAL COMPUTING (CPSCOM) AND IEEE SMART DATA (SMARTDATA), 2017, : 287 - 292