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

Date

2017-11-01

Author

Batmaz, İnci
Danışoğlu, Seza

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

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

URI

https://hdl.handle.net/11511/36395

Journal

APPLIED SOFT COMPUTING

DOI

https://doi.org/10.1016/j.asoc.2017.07.047

Collections

Department of Statistics, Article

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Citation Formats

İ. Batmaz and S. Danışoğlu, “A data mining application to deposit pricing: Main determinants and prediction models,” APPLIED SOFT COMPUTING, pp. 808–819, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36395.