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

ArXiv ID: 2502.00201 “View on arXiv”

Authors: Unknown

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.

Keywords: Financial Fraud Detection, Deep Learning, Transformers, LSTM, Convolutional Neural Networks, Financial Services

Complexity vs Empirical Score

  • Math Complexity: 1.5/10
  • Empirical Rigor: 1.0/10
  • Quadrant: Philosophers
  • Why: The paper is a literature review that summarizes existing deep learning models and metrics without presenting new mathematical derivations or complex theoretical frameworks. It does not contain any code, backtests, or original experimental datasets, relying instead on a systematic synthesis of prior studies.
  flowchart TD
    A["Research Goal<br>Identify advancements & gaps in<br>DL for Financial Fraud Detection"] --> B{"Systematic Literature Review<br>57 Studies (2019-2024)"}
    
    B --> C["Data & Inputs<br>Financial Domains: Credit Cards, Insurance, Audits"]
    
    C --> D["Computational Processes<br>DL Models: CNN, LSTM, Transformers<br>Metrics: Precision, Recall, F1, AUC-ROC"]
    
    D --> E{"Key Findings & Outcomes"}
    
    E --> F["Effectiveness<br>High performance across domains"]
    E --> G["Challenges<br>Imbalanced data, Interpretability, Ethics"]
    E --> H["Future Directions<br>Automation, Privacy-preserving techniques<br>e.g., Blockchain, PCA"]