Applied Sciences
| Open Access | A Scalable Intelligent System For Fraud Detection In Digital Transactions Using Advanced Machine Learning Models
Dr. Einar Jónsson , Department of Arctic Technology Reykjavik University of Science Reykjavik, Iceland Dr. Katrín Guðmundsdóttir , Faculty of Environmental Informatics Nordic Climate Research Institute Akureyri, IcelandAbstract
The rapid expansion of digital transaction ecosystems has significantly increased the vulnerability of financial systems to sophisticated fraudulent activities. Traditional rule-based fraud detection systems are increasingly inadequate in addressing dynamic, large-scale, and adaptive fraud patterns. This research proposes a conceptual and analytical framework for a scalable intelligent fraud detection system leveraging advanced machine learning models to enhance detection accuracy, adaptability, and computational efficiency. The study synthesizes established anomaly detection techniques, outlier analysis methodologies, and modern machine learning approaches to construct a unified perspective on fraud identification in digital environments. Emphasis is placed on scalability across high-volume transaction streams, feature engineering strategies, and model optimization for real-time decision-making. Findings indicate that hybrid and ensemble-based learning systems outperform conventional approaches in detecting evolving fraud patterns while maintaining operational efficiency. However, challenges such as data imbalance, concept drift, and computational constraints remain critical barriers to full-scale deployment. The study contributes to the theoretical and practical understanding of scalable fraud detection architectures and outlines future directions for adaptive and explainable AI-driven financial security systems.
Keywords
Fraud Detection, Machine Learning, Digital Transactions, Scalable Systems
References
Ahmed, M., Mahmood, A. N., and Hu, J. A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19–31. 2016.
Artificial Intelligence and Workforce Productivity: A Comprehensive Analysis of Transformation, Opportunities, and Challenges in the Modern Workplace.” SCIENTIFIC CULTURE, 2026. https://sci-cult.net/index.php/cult/article/view/5136/3028
Bashir, M., and Malik, M. Detection of fraudulent transactions in financial data using machine learning. International Journal of Computer Applications, 975, 1823. 2021.
Chandola, V., Banerjee, A., and Kumar, V. Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1–58. 2009.
Experian. Application fraud prevention with Hunter. 2015.
Experian. Experian Detect. 2015.
Experian. The fraud report 2013. 2015.
Forbes. How online fraud is a growing trend. 2015.
Ghosh, A., and Reilly, D. Credit card fraud detection with a neural network. Proceedings of the IEEE International Conference on Tools with Artificial Intelligence, 3, 266–270. 1994.
Gondi, Sravanthi, Pankaj Arora and Pavan Kumar Rajagopal PrakashKumar. "Utilizing Peoplesoft Kibana and Fluid Dashboards for Real-Time Decision Making." Advances in Consumer Research 3, no. 3 (2026): 657-671.
Hodge, V. J., and Austin, J. A survey of outlier detection methodologies. Artificial Intelligence Review, 22(2), 85–126. 2004.
Dasari, H. (2025). SITE RELIABILITY ENGINEERING PRACTICES FOR ERROR BUDGET MANAGEMENT IN LARGE-SCALE SYSTEMS. International Journal of Apllied Mathematics, 38(5s), 991–1001. https://doi.org/10.12732/ijam.v38i5s.366
H. K. Krishnamurthy Sukumar, "A Novel Hybrid Grey Wolf Whale Optimization for Effectual Job Scheduling and Resource Distribution in Dynamic Cloud Computing," 2025 International Conference on Sustainability, Innovation & Technology (ICSIT), Nagpur, India, 2025, pp. 1-6, doi: 10.1109/ICSIT65336.2025.11293898.
Kalyani, R., and Kumar, R. A review on feature engineering techniques for fraud detection. International Journal of Advanced Research in Computer Science, 11(5), 1–5. 2020.
Khan, S. A., and Shafique, U. A review of machine learning techniques for fraud detection. Journal of King Saud University Computer and Information Sciences, 32(1), 16–27. 2020.
Khan, S., and Rehman, A. Detection of financial fraud using machine learning techniques: A review. Journal of King Saud University Computer and Information Sciences, 33(1), 1–17. 2021.
Hebbar, K. S. (2023). An AI-augmented framework for refactoring enterprise monolithic systems. International Journal of Intelligent Systems and Applications in Engineering, 11, 593-604.
Kennedy, K. A. An analysis of fraud: Causes, prevention and notable cases. Honours Thesis. 2010.
Laheri, R., Kumar, H., Sukumar, K., Kola, C., & Makin, Y. (2025). Self-Healing Infrastructure: Leveraging Reinforcement Learning for Autonomous Cloud Recovery and Enhanced Resilience. Journal of Information Systems Engineering and Management, 10(49s).
Modadugu, J. K., Venkata, R. T. P., & Venkata, K. P. (2025b). Real-Time credit scoring and risk analysis: Integrating AI and data processing in loan platforms. International Journal of Innovative Research and Scientific Studies, 8(6), 400–409. https://doi.org/10.53894/ijirss.v8i6.9617
M. A. Hussain, V. B. Meruga, A. K. Rajamandrapu, S. R. Varanasi, S. S. S. Valiveti and A. G. Mohapatra, "Generative AI Sensor Fusion for Secure Digital Twin Ecosystems: A Standardization-Aligned Framework for Cyber-Physical Systems," in IEEE Communications Standards Magazine, doi: 10.1109/MCOMSTD.2026.3660106.
Malik, A., and Qureshi, M. A. Real time credit card fraud detection using machine learning. International Journal of Computer Applications, 975, 2429. 2021.
Malik, M., and Bashir, M. Detection of fraudulent transactions in financial data using machine learning. International Journal of Computer Applications, 975, 1823. 2021.
Modadugu, J. K., Prabhala Venkata, R. T., &Prabhala Venkata, K. (2025). Leveraging Kafka for event-driven architecture in fintech applications. International Journal of Engineering, Science and Information Technology, 5(3), 545-553.
Mui, G., and Mailley, J. A tale of two triangles: Comparing the Fraud Triangle with criminologys Crime Triangle. Accounting Research Journal, 28(1), 45–58. 2015.
Nayeem, M. 2026. Bridging Zero-Trust Security and Legacy Medical Devices: An Evaluation of Windows 11 Adoption in Hospital Clinical Workstations. Frontiers in Emerging Artificial Intelligence and Machine Learning. 3, 1 (Jan. 2026), 01–08. DOI:https://doi.org/10.64917/feaiml/Volume03Issue01-01.
Patel, A., and Shah, H. An efficient approach for fraud detection using machine learning techniques. International Journal of Innovative Technology and Exploring Engineering, 9(3), 1–6. 2020.
PadmanabhamVenkiteela (Decemeber 2025) n8n: An Open-Source Workflow Automation Platform for Enterprise Integration and AI-Driven Orchestration, International Journal of Computer Applications. https://doi.org/10.5120/ijca2025926031
S. R. Varanasi, "AI-Driven DevOps in Modern Software Engineering-A Review of Machine LearningBased Intelligent Automation for Deployment and Maintenance," 2025 IEEE 2nd International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), Bangalore, India, 2025, pp. 1-7, doi: 10.1109/ICITEICS64870.2025.11340882.
Shounik, S. (2025). The Great DTC Reset as Stress Management: Evidence that Wholesale Re-Expansion Reduces "Operating Tail Risk" in Consumer Brands. Advances in Consumer Research, 2(6), 1221-1231. 10.5281/zenodo.17995468
Suresh Gangula. (2025). Secure DevOps in Retail Cloud: Strategies for Compliance and Resilience. The American Journal of Engineering and Technology, 7(05), 109–122. https://doi.org/10.37547/tajet/Volume07Issue05-09
The41. Fraud prevention solutions. 2015.
Zhang, Y., Jiang, Y., and Liu, J. Fraud detection in mobile payment systems: A review. IEEE Access, 7, 157426–157436. 2019.
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