Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework

Mathematics

• Class 4%
• Classifier 15%
• Costs 9%
• Data Modeling 17%
• Demonstrate 8%
• Ensemble 13%
• Evaluation 9%
• Extremes 12%
• Framework 31%
• Fraud Detection 100%
• Large Data Sets 16%
• Machine Learning 30%
• Model 4%
• Outlier Detection 36%
• Performance 8%
• Sampling Methods 15%
• Standards 7%
• Sufficient 10%
• Supervised Learning 19%
• Transactions 17%

Engineering & Materials Science

• Anomaly detection 57%
• Classifiers 19%
• Costs 10%
• Data structures 22%
• Labeled data 32%
• Large dataset 29%
• Machine learning 40%
• Sampling 19%
• Smartphones 25%