A Causal Perspective on Loan Pricing: Investigating the Impacts of Selection Bias on Identifying Bid-Response Functions

• URL: http://arxiv.org/abs/2309.03730v1
• Date: Thu, 7 Sep 2023 14:14:30 GMT
• Title: A Causal Perspective on Loan Pricing: Investigating the Impacts of Selection Bias on Identifying Bid-Response Functions
• Authors: Christopher Bockel-Rickermann, Sam Verboven, Tim Verdonck, Wouter Verbeke
• Abstract summary: We take a step towards understanding the effects of selection bias by posing pricing as a problem of causal inference. In our experiments, we simulate varying levels of selection bias on a semi-synthetic dataset on mortgage loan applications in Belgium. We implement state-of-the-art methods from causal machine learning and show their capability to overcome selection bias in pricing data.
• Score: 1.0937531920233807
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In lending, where prices are specific to both customers and products, having a well-functioning personalized pricing policy in place is essential to effective business making. Typically, such a policy must be derived from observational data, which introduces several challenges. While the problem of ``endogeneity'' is prominently studied in the established pricing literature, the problem of selection bias (or, more precisely, bid selection bias) is not. We take a step towards understanding the effects of selection bias by posing pricing as a problem of causal inference. Specifically, we consider the reaction of a customer to price a treatment effect. In our experiments, we simulate varying levels of selection bias on a semi-synthetic dataset on mortgage loan applications in Belgium. We investigate the potential of parametric and nonparametric methods for the identification of individual bid-response functions. Our results illustrate how conventional methods such as logistic regression and neural networks suffer adversely from selection bias. In contrast, we implement state-of-the-art methods from causal machine learning and show their capability to overcome selection bias in pricing data.

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