Deep Learning Approach for Anomaly Detection in Digital Financial Transactions - Mahmud; Akhmad Sayuti (Journal Innovation in Information and Computer Technology, 2025)
The rapid growth of digital financial services has significantly increased the volume and complexity of financial transactions, creating new challenges in maintaining transaction security and preventing fraudulent activities. Anomaly detection has become an essential mechanism for identifying suspicious transaction patterns that may indicate potential fraud within digital financial systems. This study aims to develop a deep learning–based approach for detecting anomalies in digital financial transactions by analyzing behavioral patterns within transaction datasets. The proposed method utilizes a deep neural network architecture to analyze multiple transaction attributes such as transaction amount, transaction time, user identification, merchant category, and device information. Prior to model development, transaction data undergoes several preprocessing stages including data cleaning, data transformation, and feature normalization to ensure data quality and compatibility with deep learning algorithms. The dataset is divided into training and testing subsets to evaluate the effectiveness of the proposed model in identifying anomalous transactions. Experimental results demonstrate that the deep learning model achieves strong performance in anomaly detection tasks. The proposed model obtained an accuracy of 96.8%, precision of 94.5%, recall of 92.7%, and an F1-score of 93.6%. In addition, the ROC-AUC value of 0.97 indicates that the model is highly effective in distinguishing between normal and anomalous transactions. These results show that deep learning techniques are capable of capturing complex transaction patterns and improving the reliability of fraud detection systems in digital financial environments. The findings of this study suggest that the integration of deep learning–based anomaly detection models into financial monitoring systems can significantly enhance transaction security and assist financial institutions in detecting suspicious activities more efficiently. As digital financial ecosystems continue to evolve, intelligent anomaly detection systems will play an important role in strengthening financial security and supporting the development of secure digital financial services.