Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review

TL;DR

This systematic review analyzes recent deep learning methods for financial fraud detection, highlighting advancements, challenges, and future research directions based on 57 studies from 2019 to 2024.

Contribution

It provides a comprehensive overview of deep learning techniques, challenges, and trends in financial fraud detection, identifying gaps and promising future research avenues.

Findings

Deep learning models like CNNs, LSTMs, and transformers are effective in fraud detection.

Challenges include imbalanced datasets, interpretability, and privacy concerns.

Emerging solutions involve blockchain, feature engineering, and privacy-preserving methods.

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.