Optimizing Fintech Marketing: A Comparative Study of Logistic Regression and XGBoost

TL;DR

This study compares logistic regression and XGBoost in predicting customer responses to mail campaigns and default risk, finding XGBoost generally offers superior accuracy especially with complex data preprocessing.

Contribution

It provides a comparative analysis of machine learning models for credit risk prediction, highlighting the effectiveness of XGBoost over logistic regression with specific data preprocessing techniques.

Findings

XGBoost outperforms logistic regression in predictive accuracy.

Categorical binning and custom imputation improve model performance.

XGBoost handles complex data structures more effectively.

Abstract

As several studies have shown, predicting credit risk is still a major concern for the financial services industry and is receiving a lot of scholarly interest. This area of study is crucial because it aids financial organizations in determining the probability that borrowers would default, which has a direct bearing on lending choices and risk management tactics. Despite the progress made in this domain, there is still a substantial knowledge gap concerning consumer actions that take place prior to the filing of credit card applications. The objective of this study is to predict customer responses to mail campaigns and assess the likelihood of default among those who engage. This research employs advanced machine learning techniques, specifically logistic regression and XGBoost, to analyze consumer behavior and predict responses to direct mail campaigns. By integrating different data preprocessing strategies, including imputation and binning, we enhance the robustness and accuracy of our predictive models. The results indicate that XGBoost consistently outperforms logistic regression across various metrics, particularly in scenarios using categorical binning and custom imputation. These findings suggest that XGBoost is particularly effective in handling complex data structures and provides a strong predictive capability in assessing credit risk.