Modeling consumer acceptance probabilities
This paper investigates how to estimate the likelihood of a customer accepting a loan offer as a function of the offer parameters and how to choose the optimal set of parameters for the offer to the applicant in real time. There is no publicly available data set on whether customers accept the offer of a financial product, whose features are changing from offer to offer. Thus, we develop our own data set using a fantasy student current account. In this paper, we suggest three approaches to determine the probability that an applicant with characteristics will accept offer characteristics using the fantasy student current account data. Firstly, a logistic regression model is applied to obtain the acceptance probability. Secondly, linear programming is adapted to obtain the acceptance probability model in the case where there is a dominant offer characteristic, whose attractiveness increases (or decreases) monotonically as the characteristic's value increases. Finally, an accelerated life model is applied to obtain the probability of acceptance in the case where there is a dominant offer characteristic.
acceptance probability logistic regression model linear programming accelerated life model student bank account coarse classifying