02/02/2021

The Causal Learning of Retail Delinquency

Yiyan Huang, Cheuk Hang Leung, Xing Yan, Qi Wu, Nanbo Peng, Dongdong Wang, Zhixiang Huang

Keywords:

Abstract Paper Similar Papers

Abstract: This paper focuses on the expected difference in borrower's repayment when there is a change in the lender's credit decisions. Classical estimators overlook the confounding effects and hence the estimation error can be magnificent. As such, we propose another approach to construct the estimators such that the error can be greatly reduced. The proposed estimators are shown to be unbiased, consistent, and robust through a combination of theoretical analysis and numerical testing. Moreover, we compare the power of estimating the causal quantities between the classical estimators and the proposed estimators. The comparison is tested across a wide range of models, including linear regression models, tree-based models, and neural network-based models, under different simulated datasets that exhibit different levels of causality, different degrees of nonlinearity, and different distributional properties. Most importantly, we apply our approaches to a large observational dataset provided by a global technology firm that operates in both the e-commerce and the lending business. We find that the relative reduction of estimation error is strikingly substantial if the causal effects are accounted for correctly.

The video of this talk cannot be embedded. You can watch it here:
https://slideslive.com/38949300
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at AAAI 2021 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 14:40 
 0
 0
 0
 0
 3:33 
 0
 0
 0
 0
 5:13 
 0
 0
 0
 0
 3:14 
 0
 0
 0
 0
 15:15 
 0
 0
 0
 0
 9:06 
 0
 0
 0
 0
 15:02 
 0
 0
 0
 0
 5:11 
 0
 0
 0
 0
 2:58 
 0
 0
 0
 0
 13:37 
 0
 0
 0
 0
 23:22 
 0
 0
 0
 0
 14:48 
 0
 0
 0
 0
 5:37 
 0
 0
 0
 0
 3:21 
 0
 0
 0
 0
 16:09 
 0
 0
 0
 0
 13:42 
 0
 0
 0
 0
 3:00 
 0
 0
 0
 0
 14:59 
 0
 0
 0
 0
 3:02 
 0
 0
 0
 0
 3:21 
 0
 0
 0
 0
 8:58 
 0
 0
 0
 0
 5:11 
 0
 0
 0
 0
 3:12 
 0
 0
 0
 0
 19:59 
 0
 0
 0
 0
 14:45 
 0
 0
 0
 0
 5:14 
 0
 0
 0
 0
 7:32 
 0
 0
 0
 0
 14:56 
 0
 0
 0
 0
 5:09 
 0
 0
 0
 0
 16:05 
 0
 0
 0
 0
 6:10 
 0
 0
 0
 0
 8:03 
 0
 0
 0
 0
 12:25 
 0
 0
 0
 0
 3:21 
 0
 0
 0
 0
 3:22 
 0
 0
 0
 0
 17:20 
 0
 0
 0
 0
 5:10 
 0
 0
 0
 0
 15:29 
 0
 0
 0
 0
 5:25 
 0
 0
 0
 0
 14:10