A data mining application to deposit pricing: Main determinants and prediction models

被引:9
作者
Batmaz, Inci [1 ]
Danisoglu, Seza [2 ]
Yazici, Ceyda [1 ]
Kartal-Koc, Elcin [3 ,4 ]
机构
[1] Middle East Tech Univ, Fac Arts & Sci, Dept Stat, Dumlupinar Bulvari 1, TR-06800 Ankara, Turkey
[2] Middle East Tech Univ, Fac Econ & Adm Sci, Dept Business Adm, Dumlupinar Bulvari 1, TR-06800 Ankara, Turkey
[3] Middle East Tech Univ, Dumlupinar Bulvari 1, TR-06800 Ankara, Turkey
[4] TARU Engn METU Technopk, Dumlupinar Bulvari 1, TR-06800 Ankara, Turkey
关键词
Deposit pricing; Deposit rates; Core deposits; Generalized linear models; Multivariate adaptive regression splines; Support vector regression; Artificial neural networks; Classification and regression trees; Random forest; CONSUMER SWITCHING COSTS; NEURAL-NETWORK; BANKING; DISTANCE;
D O I
10.1016/j.asoc.2017.07.047
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This study provides unique empirical evidence regarding the determinants of deposit pricing by employing data mining methods and making use of proprietary data provided by a commercial bank. Results highlight the importance of taking into account customer- and account-specific characteristics in the determination of deposit rates. Contrary to existing evidence obtained from macro-level bank data, the customer- level data used in this study suggest that depositors with a multi-faceted and long-term relationship with the same bank seem to benefit from higher deposit rates as a reward for being a core depositor. The location of the customer is also shown to have a limited effect on the deposit rates. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:808 / 819
页数:12
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