Credit Card Fraud Detection Using Machine Learning: An AI-Driven Approach for Financial Security
AI-driven fraud detection presentationAbstract
Credit card fraud remains a major challenge for financial institutions because fraudulent activity is rare, adaptive, and costly when missed. This presentation covers a machine-learning workflow for identifying fraudulent transactions in an anonymized dataset with more than 200,000 records. It applies SMOTE for class-imbalance handling and compares logistic regression, random forest, XGBoost, and neural models using recall, precision, F1 score, and ROC-AUC.
Date
Jun 1, 2026 1:00 PM — 3:00 PM
Event
Location
Stanford University, 450 Serra Mall, Stanford, CA 94305
450 Serra Mall, Stanford, CA 94305
This presentation explains how supervised machine-learning models can support fraud detection in highly imbalanced financial datasets, with emphasis on recall, explainability, and operational security value.
