Hang Miao, Kui Zhao, Zhun Wang, Linbo Jiang, Quanhui Jia, Yanming Fang, Quan Yu
arXiv 10 Jul 2020 · Machine Learning · 1 citations (OpenAlex)
arXiv:2007.05188 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Miguel A Hernan and James M Robins (2010) Causal inference, 2010 | 0.928 | 4 | 4 | 100% |
| 2 | Guido W Imbens and Donald B Rubin (2015) Causal inference in statistics, social, and biomedical sciences | 0.843 | 3 | 3 | 100% |
| 3 | Jonas Peters, Dominik Janzing, and Bernhard Schölkopf (2017) Elements of causal inference: foundations and learning algorithms | 0.843 | 3 | 3 | 100% |
| 4 | David B Gross, Nicholas Souleles, et al (2000) Consumer response to changes in credit supply: Evidence from credit card data | 0.644 | 2 | 2 | 100% |
| 5 | Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yanxin… (2014) Practical lessons from predicting clicks on ads at facebook | 0.644 | 2 | 2 | 100% |
| 6 | Randy Hodson, Rachel E Dwyer, and Lisa A Neilson (2014) Credit card blues: the middle class and the hidden costs of easy credit | 0.644 | 2 | 2 | 100% |
| 7 | Eric Rosenberg and Alan Gleit (1994) Quantitative methods in credit management: a survey | 0.644 | 2 | 2 | 100% |
| 8 | So Young Sohn, Kyong Taek Lim, and Yonghan Ju (2014) Optimization strategy of credit line management for credit card business | 0.644 | 2 | 2 | 100% |
| 9 | Peter Addo, Dominique Guegan, and Bertrand Hassani (2018) Credit risk analysis using machine and deep learning models | 0.405 | 1 | 1 | 100% |
| 10 | Susan Athey and Guido Imbens (2016) Recursive partitioning for heterogeneous causal effects | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 28 scored citations.