LightGBM Model for Detecting Fraud in Online Financial Transactions
DOI:
https://doi.org/10.54097/xw0bng93Keywords:
Fraud detection, one-hot encoding, violin plot, LightGBM, softmax loss function.Abstract
With the rapid development of online finance, combined with its significant potential threats, nowadays it has increasingly drawn attention to the issue of fraud in online payment transactions. Due to the large and diverse nature of online payment data and the binary nature of fraud detection, this study implements the machine learning algorithm LightGBM to detect fraudulent activities. Through data exploration and data preprocessing, the data is refined. Leveraging violin plots to observe the distribution of variables in fraudulent and non-fraudulent activities, and then an insignificant variable is removed. Next, the processed data is fed into the LightGBM model. The result yields a fraud prediction accuracy of 99.5%, shows a very high precision. Additionally, the study verifies the algorithm's robustness and generalization ability. Overall, this research holds significant importance, given its findings and implications for addressing fraud in the rapidly evolving landscape of online financial transactions.
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