<ul data-eligibleForWebStory="true"><li>Research introduces a machine learning framework for detecting credit card transaction anomalies and fraud.</li><li>Framework merges various datasets to create an analytical view, extracting behavioral signals.</li><li>Features like average spending, deviations from patterns, and temporal markers are utilized in fraud detection.</li><li>Data analysis reveals transaction trends across different features.</li><li>Models like Isolation Forest, One Class SVM, and deep autoencoder are trained on transactional data.</li><li>Top 1% reconstruction errors are flagged as outliers by trained models.</li><li>PCA visualizations depict anomaly separation in a two-dimensional latent space.</li><li>K-Means clustering and DBSCAN are applied to segment the transaction landscape for detecting suspicious regions.</li></ul>

Advanced fraud detection using machine learning models: enhancing financial transaction security

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