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

作者：Batmaz Inci^*; Danisoglu Seza; Yazici Ceyda; Kartal Koc Elcin

来源：Applied Soft Computing, 2017, 60: 808-819.

DOI：10.1016/j.asoc.2017.07.047

• 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

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.

• 出版日期2017-11