Credit Card Fraud Detection Using Lightgbm Model
Dingling Ge, Jianyang Gu, Shunyu Chang, JingHui Cai
Abstract
With the rapid growth of online banking and shopping, many companies deploy their online transaction system and thus raising many issues of fraud online credit card transactions. In recent years, several studies have developed some data mining based method to overcome this problem. However, most of the studies used a small amount of data and feature engineering methods. Moreover, the learning models used by them are too weak to fit the large scale of data. This paper expands fraud detection strategy and proposed a detection algorithm using lightgbm. The dataset is IEEE-CIS Fraud Detection dataset provided by Vesta Corporation. The experiments indicated that our method outperformed the other classical methods like Support Vector Machine, Random Forest and Xgboost. Moreover, it also shows the feature importance of our feature engineering, which is valuable for feature selection and performance tuning.