Traditional detection systems exhibit low accuracy

The gap

Evidence profile

Sourced from the limitations section and future work and conclusions and recommendations of the source papers, classified as general, spanning 4 journals.

Supporting evidence — 5 representative gaps

• Online Recruitment Fraud (ORF) Detection Using Deep Learning Approaches (2026) · Research Digest on Engineering Management and Social Innovations · doi

  Traditional detection systems exhibit low accuracy when dealing with complex and evolving fraud patterns. They suffer from high rates of false positives and false negatives. The proposed system can be further improved by incorporating advanced deep learning and artificial intelligence techniques.

  generallimitations section
  Keywords: traditional detection systems exhibit low accuracy dealing complex
• Online Payment Fraud Detection Using Machine Learning (2026) · International Journal of Latest Technology in Engineering Management & Applied Science · 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 realworld 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.

  generalfuture workevidence 5/5
  Keywords: fraud detection learning online payment using machine deep models applied model real system ensemble enhanced
• DevOps-Enabled Agentic Deep Learning for Insurance Fraud Intelligence (2026) · American Journal of Analytics and Artificial Intelligence · doi

  Future research will address the development of production-ready models and consider the implications of ML-supported fraud detection in the domain of insurance. These principles facilitate the scalable deployment of models tailored for the constant battle against ever-evolving fraudulent techniques, but scalability and operationalization remain to be proven.

  generalconclusionsevidence 5/5
  Keywords: models future address development production ready consider implications supported fraud detection domain insurance principles facilitate
• Integrating Support Vector Machine Classifiers for Real-Time Sentiment Analysis and Fraud Detection in A Fashion E-Commerce Platform (2026) · Scientific Journal of Engineering, and Technology · doi

  There are a number of extensions that would be useful in the system. Linking the fraud detection module to a real paying gateway like Paystack or Flutterwave would replace the simulated feature vector with real financial transaction data, resulting in a much more reliable fraud classifier in production. The sentiment model could be used in more languages and with more context dependent expressions like sarcasm and that would make it a more useful model in markets outside of the English speaking one. A product recommendation system based on browsing and purchase history would help support the existing review and fraud features. Lastly, the application would be deployed in a real production environment, with the appropriate security hardening, for a thorough performance assessment in a real environment with load. RECOMMENDATIONS Future applications of similar systems should incorporate a real payment gateway instead of simulated transaction data from the beginning of the project. Training datasets need to be continually increased and updated to ensure that the performance of the model remains accurate as fraud and review language patterns change over time. Developers need to make sure that their pipelines are continuous for retraining, not just one-time deployments. Any company that plans to implement a similar design should test extensively and in a staging environment before deployment, especially the fraud detection element, which seems to have operational costs both from false positives and false negatives. REFERENCES Banu, R., Ashok, A., Dwivedi, V. K., Reddy, K. A., Thulasimani, T., & Nishant, N. (2024). An innovative method for fraud detection in e-commerce using DCNN-multiclass SVM model. In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) (pp. 1-6). IEEE. https://doi.org/10.1109/IACIS61494.2024.10721774 Coherent Market Insights. (2023). Fashion e-commerce market analysis and growth projections. Retrieved from https:// www.coherentmarketinsights.com Kaggle. (2023a). Credit card fraud detection dataset. Retrieved https://www.kaggle.com/datasets/mlg-ulb/ from creditcardfraud Kaggle. (2023b). Women's e-commerce clothing reviews dataset. Retrieved from https://www.kaggle.com/datasets/ nicapotato/womens-ecommerce-clothing-reviews Kumar, S., Gunjan, V. K., Ansari, M. D., & Pathak, R. (2022). Credit card fraud detection using support vector machine. In Proceedings of the 2nd International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications: ICMISC 2021 (pp. 27-37). Springer, Singapore. https://doi.org/10.1007/978-981-16-6407-6_3 Mutemi, A., & Bacao, F. (2024).

  generalfuture workevidence 5/5
  Keywords: fraud detection real https model commerce kaggle environment datasets retrieved machine useful system gateway like
• Integrating Support Vector Machine Classifiers for Real-Time Sentiment Analysis and Fraud Detection in A Fashion E-Commerce Platform (2026) · Scientific Journal of Engineering, and Technology · doi

  Future applications of similar systems should incorporate a real payment gateway instead of simulated transaction data from the beginning of the project. Training datasets need to be continually increased and updated to ensure that the performance of the model remains accurate as fraud and review language patterns change over time. Developers need to make sure that their pipelines are continuous for retraining, not just one-time deployments. Any company that plans to implement a similar design should test extensively and in a staging environment before deployment, especially the fraud detection element, which seems to have operational costs both from false positives and false negatives. REFERENCES Banu, R., Ashok, A., Dwivedi, V. K., Reddy, K. A., Thulasimani, T., & Nishant, N. (2024). An innovative method for fraud detection in e-commerce using DCNN-multiclass SVM model. In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) (pp. 1-6). IEEE. https://doi.org/10.1109/IACIS61494.2024.10721774 Coherent Market Insights. (2023). Fashion e-commerce market analysis and growth projections. Retrieved from https:// www.coherentmarketinsights.com Kaggle. (2023a). Credit card fraud detection dataset. Retrieved https://www.kaggle.com/datasets/mlg-ulb/ from creditcardfraud Kaggle. (2023b). Women's e-commerce clothing reviews dataset. Retrieved from https://www.kaggle.com/datasets/ nicapotato/womens-ecommerce-clothing-reviews Kumar, S., Gunjan, V. K., Ansari, M. D., & Pathak, R. (2022). Credit card fraud detection using support vector machine. In Proceedings of the 2nd International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications: ICMISC 2021 (pp. 27-37). Springer, Singapore. https://doi.org/10.1007/978-981-16-6407-6_3 Mutemi, A., & Bacao, F. (2024). E-commerce fraud detection based on machine learning techniques: Systematic literature review. Big Data Mining and Analytics, 7(2), 419-444. https:// doi.org/10.26599/BDMA.2023.9020024 Sharma, H. D., & Goyal, P. (2024). Interpretable aspect based sentiment classification of online educational reviews using SVM model and explainable LIME-AI model. International Journal of Information Technology, 16, 4567-4578. https://doi. org/10.1007/s41870-024-02125-w Shopify. (2024). E-commerce statistics and trends: Social commerce revenue data. Retrieved from https://www. shopify.com/research Statista. (2023). Global fashion e-commerce market size and forecasts 2024-2030. Retrieved from https://www.statista.com Tabany, M., & Gueffal, M. (2024). Sentiment analysis and fake Amazon reviews classification using SVM supervised machine learning model. Journal of Advances in Information Technology, 15(1), 49-58.

  generalrecommendationsevidence 5/5
  Keywords: https commerce fraud model detection retrieved using kaggle reviews machine datasets international market learning applications

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