mlsanityData to 14 Oct 2026

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

arXiv 2502.00201 ↗First posted 31 Jan 2025cs.LG cs.AI q-fin.STcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 5 of its 64 references that are in the map. First posted on arXiv 31 Jan 2025.

Builds on

5 of 64 references.

References · 5

  1. 2023Financial Fraud Detection using Quantum Graph Neural Networkscited by 35
  2. 2020Deep Q-Network-based Adaptive Alert Threshold Selection Policy for Payment Fraud Systems in Retail Bankingcited by 10
  3. 2021Financial Crime & Fraud Detection Using Graph Computing: Application Considerations & Outlookcited by 4
  4. 2022Spatio-Temporal Graph Representation Learning for Fraudster Group Detectioncited by 3
  5. 2022Blockchain and Machine Learning for Fraud Detection: A Privacy-Preserving and Adaptive Incentive Based Approachcited by 2

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Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025User Behavior Analysis in Privacy Protection with Large Language Models: A Study on Privacy Preferences with Limited Datacited by 8
  2. 2025A Narrative Review on Large AI Models in Lung Cancer Screening, Diagnosis, and Treatment Planningcited by 4
  3. 2025Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017cited by 4
  4. 2025Efficient or Powerful? Trade-offs Between Machine Learning and Deep Learning for Mental Illness Detection on Social Mediacited by 2