Efficient Commercial Bank Customer Credit Risk Assessment Based on LightGBM and Feature Engineering
It provides a reference for commercial banks' credit granting and feature processing ideas for similar studies, but is incremental as it applies existing methods to a specific domain.
This paper tackled credit risk assessment for commercial banks by building a classifier using LightGBM and feature engineering on a Kaggle dataset, achieving an accuracy of 0.734 and AUC of 0.772, outperforming other classifiers on the same dataset.
Effective control of credit risk is a key link in the steady operation of commercial banks. This paper is mainly based on the customer information dataset of a foreign commercial bank in Kaggle, and we use LightGBM algorithm to build a classifier to classify customers, to help the bank judge the possibility of customer credit default. This paper mainly deals with characteristic engineering, such as missing value processing, coding, imbalanced samples, etc., which greatly improves the machine learning effect. The main innovation of this paper is to construct new feature attributes on the basis of the original dataset so that the accuracy of the classifier reaches 0.734, and the AUC reaches 0.772, which is more than many classifiers based on the same dataset. The model can provide some reference for commercial banks' credit granting, and also provide some feature processing ideas for other similar studies.