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

BATMAZ İ., DANIŞOĞLU S., Yazici C., Kartal-Koc E.

APPLIED SOFT COMPUTING, vol.60, pp.808-819, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 60
  • Publication Date: 2017
  • Doi Number: 10.1016/j.asoc.2017.07.047
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.808-819
  • 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, CONSUMER SWITCHING COSTS, NEURAL-NETWORK, BANKING, DISTANCE
  • Middle East Technical University Affiliated: Yes


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.