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

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  • Yisong Chen
  • Chuqing Zhao
  • Yixin Xu
  • Chuanhao Nie

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.

Suggested Citation

  • Yisong Chen & Chuqing Zhao & Yixin Xu & Chuanhao Nie, 2025. "Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review," Papers 2502.00201, arXiv.org.
    • Handle: RePEc:arx:papers:2502.00201
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    References listed on IDEAS

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    1. Jaiswal, Rachana & Gupta, Shashank & Tiwari, Aviral Kumar, 2024. "Big data and machine learning-based decision support system to reshape the vaticination of insurance claims," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
    2. Eman Nabrawi & Abdullah Alanazi, 2023. "Fraud Detection in Healthcare Insurance Claims Using Machine Learning," Risks, MDPI, vol. 11(9), pages 1-11, September.
    3. Esraa Faisal Malik & Khai Wah Khaw & Bahari Belaton & Wai Peng Wong & XinYing Chew, 2022. "Credit Card Fraud Detection Using a New Hybrid Machine Learning Architecture," Mathematics, MDPI, vol. 10(9), pages 1-16, April.
    4. Aslam, Faheem & Hunjra, Ahmed Imran & Ftiti, Zied & Louhichi, Wael & Shams, Tahira, 2022. "Insurance fraud detection: Evidence from artificial intelligence and machine learning," Research in International Business and Finance, Elsevier, vol. 62(C).
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