LightGBM Model for Credit Card Fraud Discovery

Abstract

The exponential growth of e-commerce and online-based payment options has created an empirical universe of financial fraud, with credit card fraud being the most prevalent. For several years, many researchers have developed a variety of data mining-based methods to address this issue. To detect credit card fraud, there has recently been a lot of interest in using machine learning algorithms instead of data mining techniques. In the digital space of financial transactions, on-going work is being conducted to put in a conceptual difference between fraud identification and predicting likely fraudulent opportunities. This paper extends the fraud detection technique and proposes a LightGBM-based detection algorithm. The dataset is a credit card dataset for credit card transactions in Europe. Our approach outperformed other traditional approaches such as random forest, AdaBoost, and XGBoost in this experiment. Furthermore, it demonstrates the value of feature engineering in terms of feature selection and performance tuning.