Fraud Detection Model in Imbalanced Data Using Dimension Reduction And Machine Learning Algorithms
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Abstract
The objective of this research is to develop a method for fraud detection model in imbalanced data using dimension reduction combined with machine learning algorithms for fraud detection and finding the relationship of irregular transaction groups. The main purpose is to prevent damage from fraudulent transactions in electronic commerce systems. The results of the experiment show that the model using the Extreme Gradient Boosting algorithm gives the highest accuracy of 98.15 % with the shortest processing time. From the experiment, it was found that the developed model resulted in the algorithm has the ability to perform more effectively.
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