Paper: arXiv 2502.00201

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.

Complexity vs Empirical Score

  • Math Complexity: 1.5/10
  • Empirical Rigor: 1.0/10
  • Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: 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.

Research Flowchart

  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"]

Cited References (61)

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and 53 more
  1. Deep blockchain approach for anomaly detection in the Bitcoin network (2024)
  2. A machine learning and blockchain-based efficient fraud detection mechanism (2022)
  3. Utilizing GANs for credit card fraud detection: A comparison of supervised learning algorithms (2023)
  4. Optimization of deep learning-based model for identification of credit card frauds (2024)
  5. The data analysis of enterprise operational risk prediction under machine learning: Innovations and improvements in corporate law risk management strategies (2024)
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  51. Decision support system for fraud detection in health insurance claims using genetic support vector machines ( GSVMs ) (2019)
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0 of 61 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.