CONCLUSION AND FUTURE SCOPE This review has argued that predictive performance alone is insufficient for high-stakes financial fraud detection: a deployable system must also be feature interpretable,

The gap

Consensus across the literature

Supporting evidence — 4 representative gaps

• Explainable Credit Card Fraud Detection Using LightGBM And SHAP-Guided Feature Selection: A Review (2026) · doi

  CONCLUSION AND FUTURE SCOPE This review has argued that predictive performance alone is insufficient for high-stakes financial fraud detection: a deployable system must also be feature interpretable, auditable, efficient, and robust to changing transaction behaviour.

  Keywords: conclusion future scope review argued predictive performance alone insufficient high stakes financial fraud detection deployable
• Online Payment Fraud Detection Using Machine Learning (2026) · doi

  In this work, an effective online payment fraud detection system was developed using machine learning techniques. The combination of CatBoost and XGBoost models through an ensemble approach resulted in improved predictive performance. PCA was used for dimensionality reduction, and SMOTE was applied to address class imbalance, which significantly enhanced the model’s ability to detect fraudulent transactions. The system achieved high accuracy, precision, recall, and AUC scores, demonstrating its effectiveness in real- world scenarios. Additionally, the deployment of the model using Streamlit provides a practical interface for real-time fraud detection. Future work can focus on integrating deep learning approaches such as neural networks and graph-based models to capture more complex transaction patterns. Furthermore, real-time streaming data and largescale deployment can be explored to improve scalability and adaptability in dynamic financial environments. REFERENCES 1. M. Habibpour, H. Gharoun, M. Mehdipour, A. Tajally, H. Asgharnezhad, A. Shamsi, A. Khosravi, M. Shafie-Khah, S. Nahavandi, and J. P. S. Catalao, ''Uncertainty-aware Online payment fraud detection using deep learning 2021; arXiv:2107.13508. 2. A. Cherif, A. Badhib, H. Ammar, S. Alshehri, M. Kalkatawi, and A. Imine. "Online payment fraud detection in the era of disruptive technologies: A systematic review." J. King Saud Univ. Computer and Information Science, vol. 35, no. 1, pp. 145-174, Jan. 2023, doi:10.1016/j.jksuci.2022.11.008. 3. T. K. Dang, T. C. Tran, L. M. Tuan, and M. V. Tiep. "Machine learning based on resampling approaches and deep reinforcement learning for Online payment fraud detection systems." Appl. Sci., vol. 11, no. 21, p. 10004, Oct. 2021; doi: 10.3390/app112110004. Page 927 www.rsisinternational.org INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING, MANAGEMENT & APPLIED SCIENCE (IJLTEMAS) ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue IV, April 2026 4. Chaquet-Ulldemolins et al., ''On the black-box problem for fraud detection using machine learning (I): Linear models and informative feature selection,'' Applied Sciences, vol. 12, no. 7, p. 3328, March 2022, doi: 10.3390/app12073328. 5. E. F. Malik, K. W. Khaw, B. Belaton, W. P. Wong, and X. Chew. "Online payment fraud detection using a new hybrid machine learning architecture." Mathematics, vol. 10, no. 9, p. 1480, April 2022; doi: 10.3390/math10091480. 6. I. Benchaji, S. Douzi, B. El Ouahidi, and J. Jaafari, "Enhanced Online payment fraud detection using attention mechanism and LSTM deep model," J. Big Data, vol. 8, no. 1, p. 151, December 2021; doi: 7. 10.1186/s40537-021-00541-8. 8. E. Esenogho, I. D. Mienye, T. G. Swart, K. Aruleba, and G. Obaido. "A neural network ensemble with feature engineering for

  Keywords: fraud detection learning online payment using machine deep models applied model real system ensemble enhanced
• FraudGuard: Efficient Predictive Modelling for Financial Fraud Detection (2026) · doi

  Future research will explore streaming fraud detection with online XGBoost, graph neural networks for transactional network modelling, federated learning for privacy-preserving cross-institutional fraud detection, and adaptive retraining strategies to address concept drift in production deployments.

  Keywords: fraud detection future explore streaming online xgboost graph neural networks transactional network modelling federated learning
• Bidirectional fusion heterogeneous graph networks for semi-supervised Bitcoin transaction anomaly detection in dynamic transaction graphs (2026) · doi

  5.1 Conclusion In this paper, we carry out systematic research on bitcoin transaction anomaly detection task and propose several inno- vative methods: firstly, to meet the practical requirements, we define the dynamic heterogeneous graph semi-supervised bitcoin anomaly detection task and design the Bi-directional Fusion Heterogeneous Graph Network (BF-HGN) to construct the basic framework. Second, in feature extraction, we improve upon RGCN to construct EvolveRGCN and combines EvolveGCN to design a gradual scheme. It also introduces LSTM to capture temporal features and deeply mines dynamic features through a fusion strategy. Further, we propose the Multi-type Feature Fusion Extractor. This improves the dynamic relationship modeling capability by capturing the upper and lower time-point subgraph associations. Lastly, we address the class imbalance problem caused by unlabeled anomalous samples by designing Class-balanced Classifiers. These classifiers balance the training data class distribution by generating pseudo-abnormal nodes constrained by AA and AFSR loss function. 5.2 Outlook Future research can be extended to a broader range of financial transaction scenarios, thereby strengthening risk pre- vention and control capabilities. Further exploration of the optimization space of feature extraction and fusion strate- gies reveals potential associations in complex data and injects richer semantic information into the model. Meanwhile, continuous efforts should be made to refine the optimization path of loss functions to improve the generation quality of pseudo-anomalous nodes, so as to promote the security and stability of anomaly detection technologies in Bitcoin transactions and related fields. In addition to technical advancements, future studies should incorporate regulatory, ethical, and societal considerations into the design of anomaly detection systems. Inspired by the sociotechnical framework proposed by Rahman et al. [62], responsible and trustworthy FinTech development can be better supported in blockchain transaction surveillance, particularly with respect to regulatory compliance, transparency, and social accountability.

  Keywords: anomaly detection fusion bitcoin transaction dynamic design feature class task propose heterogeneous graph construct framework