Efficient Commercial Bank Customer Credit Risk Assessment Based on
  LightGBM and Feature Engineering
Yanjie Sun Zhike Gong Quan Shi Lin Chen
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.