Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review
Journal:
arXiv
Published Date:
Jan 31, 2025
Abstract
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.
