Using data mining for bank direct marketing: an application of the CRISP-DM methodology

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Adaptive Sensitive Reweighting to Mitigate Bias in Fairness-aware Classification (2018, background): “For example, if financial status is a sensitive attribute for term deposit predictions [37], disparate impact elimination would ensure that the portion of positive predictions is the same between lowincome and high-income clients.”
An Interpretable Model with Globally Consistent Explanations for Credit Risk (2018, background): “decision trees and support vector machines for bank direct marketing [10, 9]), which cannot accommodate FICO’s monotonicity constraints.”
An optimization approach to learning falling rule lists (2017, methodology): “We used the public bank-full dataset (Moro et al., 2011), which contains 45211 observations, with 12 predictor variables that were discretized.”
Using sensitivity analysis and visualization techniques to open black box data mining models (2013, background): “The bank data consists of 4521 samples related to a direct marketing campaign of a Portuguese bank [24].”