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

2025-01-31

AI Summary

This study systematically investigates the evolution and challenges of deep learning in financial fraud detection—spanning credit card transactions, insurance claims, and financial statement auditing—from 2019 to 2024. Following Kitchenham’s guidelines, we conduct the first five-year longitudinal systematic literature review (SLR), synthesizing 57 peer-reviewed studies employing CNNs, LSTMs, Transformers, PCA-based dimensionality reduction, blockchain integration, and imbalance-aware learning techniques. We identify three critical barriers: (1) privacy-compliance frameworks impeding model deployment, (2) insufficient model interpretability hindering regulatory acceptance, and (3) poor cross-domain generalizability. As a key contribution, we propose a “privacy-enhanced automated detection” technical roadmap, establish empirically grounded best practices for precision, recall, F1-score, and AUC-ROC, and pinpoint two major research gaps: explainable AI (XAI) for audit-ready decision justification and lightweight federated learning for privacy-preserving, resource-efficient collaboration.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Machine Learning: Deep Neural Architectures and Foundation Models
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Security and Privacy: Blockchains and distributed ledgers
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Deep Learning
• Financial Fraud Detection
• Challenges and Opportunities

Innovation

• Deep Learning
• Financial Fraud Detection
• Privacy Protection