Anomaly-Based Financial Fraud Detection Using Autoencoder A Case Study on the Kaggle Credit Card Dataset - Andri Nata; Dudes Manalu; Jaya Tata Hardinata; Peniel Sam Putra Sitorus (Lembaga Penelitian dan Pengabdian Masyarakat, 2025)
Financial fraud remains a critical challenge for banking systems and digital payment platforms worldwide. With the rapid growth of electronic transactions, effective fraud detection mechanisms are essential to ensure security and user trust. This study explores the application of an unsupervised deep learning model—Autoencoder—for anomaly-based financial fraud detection. Utilizing the publicly available Kaggle Credit Card Fraud Detection dataset, which comprises 284,807 transactions including 492 fraudulent cases, the model is trained exclusively on legitimate transactions to learn typical behavioral patterns. Prior to training, the dataset underwent feature anonymization using Principal Component Analysis (PCA), and numerical columns such as