The Causal Learning of Retail Delinquency
This work is significant for lenders and financial institutions as it provides a more accurate method for predicting retail delinquency, potentially leading to better credit decisions and reduced financial risk.
This paper addresses the problem of estimating the causal effect of lender's credit decisions on borrower repayment, which is often confounded in classical estimators. The authors propose new estimators that significantly reduce estimation error by correctly accounting for causal effects, as demonstrated on a large observational dataset from a global technology firm.
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.