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

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

APPLIED SOFT COMPUTING, cilt.60, ss.808-819, 2017 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 60
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1016/j.asoc.2017.07.047
  • Dergi Adı: APPLIED SOFT COMPUTING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.808-819
  • Anahtar Kelimeler: 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
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.