Enhancing Accuracy of Financial Fraud Detection in Mobile Transactions Using Xgboost Algorithm
Yashika Chauhan, Mudita Uppal, Deepali Gupta, Shrikant Ashok Mapari, Shilpa Saini · 2025
With the growth of modern technology, there has been a significant rise in fraud instances. Every year, billion dollars are disappear as an outcome of the growth on a global level. However, using preventive technologies seems to be the most effective approach for fraud detection. The authors focus on an advanced and innovative fraud detection solution using machine learning technique. Machine learning models such as Logistic Regression, Decision Tree, XGBoost Classifier and Random Forest are used in this paper to compare performances. The result indicates that XGBoost Classifier and Random Forest offers the best accuracy, with percentage around 99.9% and 99.14% respectively. These result shows that machine learning could greatly enhance the security for online transactions.