@article{
 title = {A data mining application to deposit pricing: Main determinants and prediction models},
 type = {article},
 year = {2017},
 identifiers = {[object Object]},
 pages = {808-819},
 volume = {60},
 websites = {https://www.sciencedirect.com/science/article/pii/S1568494617304672},
 month = {11},
 publisher = {Elsevier},
 day = {1},
 id = {6e9c3e83-8652-3928-b494-5d3ea108c226},
 created = {2018-06-25T13:11:56.611Z},
 accessed = {2018-06-25},
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 profile_id = {35f49df4-e9cf-391e-a76a-8e9e316c42aa},
 group_id = {ee11d323-dd56-3515-8742-9b1e2f0496f7},
 last_modified = {2018-06-30T13:47:44.136Z},
 read = {false},
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 hidden = {false},
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 private_publication = {false},
 abstract = {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.},
 bibtype = {article},
 author = {Batmaz, İnci and Danışoğlu, Seza and Yazıcı, Ceyda and Kartal-Koç, Elçin},
 journal = {Applied Soft Computing}
}

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