  Nowadays consumer loan plays an important role in promoting the economic
growth, and credit cards are the most popular consumer loan. One of the most
essential parts in credit cards is the credit limit management. Traditionally,
credit limits are adjusted based on limited heuristic strategies, which are
developed by experienced professionals. In this paper, we present a data-driven
approach to manage the credit limit intelligently. Firstly, a conditional
independence testing is conducted to acquire the data for building models.
Based on these testing data, a response model is then built to measure the
heterogeneous treatment effect of increasing credit limits (i.e. treatments)
for different customers, who are depicted by several control variables (i.e.
features). In order to incorporate the diminishing marginal effect, a carefully
selected log transformation is introduced to the treatment variable. Moreover,
the model's capability can be further enhanced by applying a non-linear
transformation on features via GBDT encoding. Finally, a well-designed metric
is proposed to properly measure the performances of compared methods. The
experimental results demonstrate the effectiveness of the proposed approach.
