TY - JOUR AU - Sinanc, Duygu AU - Demirezen, Umut AU - Sağıroğlu, Şeref PY - 2021/02/11 Y2 - 2024/03/29 TI - Explainable Credit Card Fraud Detection with Image Conversion JF - ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal JA - ADCAIJ VL - 10 IS - 1 SE - Articles DO - 10.14201/ADCAIJ20211016376 UR - https://revistas.usal.es/cinco/index.php/2255-2863/article/view/ADCAIJ20211016376 SP - 63-76 AB - <p>The increase in the volume and velocity of credit card transactions causes class imbalance and concept deviation problems in data sets where credit card fraud is detected. These problems make it very difficult for traditional approaches to produce robust detection models. In this study, a different perspective has been developed for this problem and a novel approach named Fraud Detection with Image Conversion (FDIC) is proposed. FDIC handles credit card transactions as time series and transforms them into images. These images, which comprise temporal correlations and bilateral relationships of features, are classified by a convolutional neural network architecture as fraudulent or legitimate. When the obtained results are compared with the related studies, FDIC has the best F1-score and recall values, which are 85.49% and 80.35%, respectively. Since the images created during the FDIC process are difficult to interpret, a new explainable artificial intelligence approach is also presented. In this way, feature relationships that have a dominant effect on fraud detection are revealed.</p> ER -