Fraud Detection in Ecommerce Transactions: An Ensemble Learning Approach

One of the most critical factors that regulators and consumers consider when it comes to assessing the trustworthiness and security of online transactions is the detection of fraud. This paper presents a framework that combines the power of various machine learning models to perform fraud detection. Due to the increasing sophistication of the techniques used in this field, single-model approaches are typically not able to provide the best possible performance. With the help of ensembles learning, which combines multiple prediction models, an effective solution can be obtained. The study compares the performance of different models in the detection of fraud carried out on e-commerce transactions. The evaluation of these models is carried out using various performance metrics, such as F1-score, precision, and recall. The results of the study revealed that the XGBoost model performed better than the other ensembles when it comes to detecting fraud. Its high F1-score and accuracy can be attributed to its efficient gradient boosting implementation. Compared to traditional GBM, XGBoost's model formulation delivers better performance and control overfitting. The recommendations from this research can help improve the efficiency and effectiveness of e-commerce fraud detection systems, protect the interests of consumers and businesses, and help prevent fraudulent activities.