Intelligent Credit Limit Management in Consumer Loans Based on Causal Inference

TL;DR

This paper introduces a data-driven, causal inference-based method for managing credit limits in consumer loans, improving upon traditional heuristic strategies by modeling heterogeneous treatment effects with advanced transformations.

Contribution

It presents a novel approach combining conditional independence testing, response modeling, and feature transformations to optimize credit limit adjustments for individual customers.

Findings

Effective in capturing heterogeneous treatment effects

Improves credit limit management accuracy

Demonstrates superior performance over baseline methods

Abstract

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.