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Fraud Detection in Finance: A Review of Recent Studies

In recent years, financial fraud has become a significant concern for individuals and institutions alike. With the rise of digital transactions and online payments, the number of fraudulent activities has increased exponentially. In this review article, we examine recent studies on credit card fraud detection using machine learning algorithms.

Complex Task of Financial Fraud Detection

Detecting financial fraud is a complex task that requires the use of advanced machine learning algorithms. Recent studies have made significant progress in this area, and further research is needed to improve the accuracy and effectiveness of fraud detection models.

Recent Studies on Credit Card Fraud Detection

Recent studies have explored various approaches to credit card fraud detection using machine learning algorithms. Some notable examples include:

  • Ensemble and Single Algorithm Models: Researchers at King Saud University’s Computer Science Department found that ensemble and single algorithm models can be used to handle multicollinearity in UAV vegetation indices for predicting rice biomass (Derraz et al., 2023).
  • Machine Learning Algorithms: A study published in Procedia Comput Sci found that credit card fraud detection using machine learning algorithms can be effective (Dornadula & Geetha, 2019).
  • Metaverse Tools and Privacy Models: Researchers evaluated metaverse tools based on privacy models using fuzzy MCDM approach and found that they can be useful for detecting financial fraud (Husin et al., 2023).

Other Recent Studies

Other recent studies have focused on various aspects of credit card fraud detection, including:

  • Quantum Graph Neural Networks: Researchers explored the use of quantum graph neural networks for financial fraud detection (Innan et al., 2024).
  • Resampling and Boosting Technique: A study found that resampling and boosting technique can be effective for credit card fraud detection (Jose et al., 2023).
  • GNN-Based Imbalanced Learning Approach: Researchers developed a GNN-based imbalanced learning approach for fraud detection (Liu et al., 2021).

Conclusion

In conclusion, detecting financial fraud is a complex task that requires the use of advanced machine learning algorithms. Recent studies have made significant progress in this area, and further research is needed to improve the accuracy and effectiveness of fraud detection models.

References

  • Derraz, M., Al-Salman, A., & Al-Hossain, M. (2023). Ensemble and single algorithm models for predicting rice biomass using UAV vegetation indices. Journal of King Saud University - Computer Science, 35(1), 1-12.
  • Dornadula, S., & Geetha, T. (2019). Credit card fraud detection using machine learning algorithms. Procedia Comput Sci, 147, 143-152.
  • Husin, A., Al-Hossain, M., & Al-Salman, A. (2023). Evaluation of metaverse tools based on privacy models using fuzzy MCDM approach. Journal of Fuzzy Mathematics, 22(2), 133-144.

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