Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review
It addresses the critical problem of financial fraud for the financial sector by synthesizing existing research, but it is incremental as it reviews rather than proposes new methods.
This paper systematically reviews advancements in deep learning techniques for financial fraud detection from 2019 to 2024, analyzing 57 studies to highlight the effectiveness of models like CNNs and LSTMs across domains such as credit card transactions and insurance claims, with performance evaluated using metrics like precision and F1-score.
This paper systematically reviews advancements in deep learning (DL) techniques for financial fraud detection, a critical issue in the financial sector. Using the Kitchenham systematic literature review approach, 57 studies published between 2019 and 2024 were analyzed. The review highlights the effectiveness of various deep learning models such as Convolutional Neural Networks, Long Short-Term Memory, and transformers across domains such as credit card transactions, insurance claims, and financial statement audits. Performance metrics such as precision, recall, F1-score, and AUC-ROC were evaluated. Key themes explored include the impact of data privacy frameworks and advancements in feature engineering and data preprocessing. The study emphasizes challenges such as imbalanced datasets, model interpretability, and ethical considerations, alongside opportunities for automation and privacy-preserving techniques such as blockchain integration and Principal Component Analysis. By examining trends over the past five years, this review identifies critical gaps and promising directions for advancing DL applications in financial fraud detection, offering actionable insights for researchers and practitioners.