The Causal Learning of Retail Delinquency

Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21)

Posted: 1 Mar 2021

See all articles by Yiyan Huang

Yiyan Huang

City University of Hong Kong (CityU) - School of Data Science

Cheuk Hang Leung

City University of Hong Kong (CityU) - School of Data Science

Xing Yan

Renmin University of China; City University of Hong Kong

Qi Wu

City University of Hong Kong, School of Data Science

Nanbo Peng

JD Digits

Dongdong Wang

JD Digits

Zhixiang Huang

JD Digits

Date Written: December 27, 2020

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.

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Suggested Citation

Huang, Yiyan and Leung, Cheuk Hang and Yan, Xing and Wu, Qi and Peng, Nanbo and Wang, Dongdong and Huang, Zhixiang, The Causal Learning of Retail Delinquency (December 27, 2020). Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), Available at SSRN: https://ssrn.com/abstract=3755726

Yiyan Huang

City University of Hong Kong (CityU) - School of Data Science ( email )

Kowloon
Hong Kong

Cheuk Hang Leung

City University of Hong Kong (CityU) - School of Data Science ( email )

Kowloon
Hong Kong

Xing Yan

Renmin University of China

Zhongguancun
Haidian District
Beijing, Beijing 100872
China

City University of Hong Kong ( email )

Hong Kong

Qi Wu (Contact Author)

City University of Hong Kong, School of Data Science ( email )

83 Tat Chee Avenue
Kowloon
Hong Kong

Nanbo Peng

JD Digits

Dongdong Wang

JD Digits ( email )

China

Zhixiang Huang

JD Digits ( email )

China

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