Explainable AI Driven Preemptive Fraud Detection for Transparent Cloud-Based Financial Risk Assessment
DOI:
https://doi.org/10.15662/IJARCST.2025.0806035Keywords:
Explainable Artificial Intelligence, Fraud Detection, Cloud Computing, Financial Risk Assessment, Machine Learning, Transparency, Preemptive Detection, XAI, Financial Security, Risk ManagementAbstract
The rapid adoption of cloud-based financial services has transformed fraud detection by enabling organizations to process large volumes of transactional data in real time. However, conventional fraud detection systems often function as opaque models, making it difficult for financial institutions, auditors, regulators, and customers to understand why a transaction has been classified as fraudulent or risky. This study proposes an Explainable Artificial Intelligence (XAI)-driven preemptive fraud detection framework for transparent cloud-based financial risk assessment. The proposed approach integrates machine learning-based fraud prediction with explainability techniques to identify suspicious transactions before significant financial losses occur while providing interpretable reasons for each risk decision. The methodology incorporates transaction characteristics, customer behavior, temporal patterns, device information, and contextual risk indicators within a scalable cloud environment. Supervised and anomaly-based learning techniques are considered to address both known and emerging fraud patterns. Explainability methods are incorporated to identify influential features and generate human-understandable explanations for individual predictions and overall model behavior. Model performance is evaluated using precision, recall, F1-score, area under the ROC curve, false-positive rate, detection latency, and explanation quality. The proposed framework aims to improve fraud detection effectiveness while strengthening transparency, accountability, trust, and regulatory compliance in cloud-based financial services
References
1. Rajula, A. (2024). Adaptive privacy-aware retrieval for federated clinical language models. International Journal of Research and Applied Innovations (IJRAI), 7(3), 10812–10822.
2. Challa, R. (2023). Reliability Engineering for Zero-Downtime Integration of HPC Systems into Regulated Environments. International Journal of Research and Applied Innovations, 6(6), 10082–10092.
3. Begum, S., Jobiullah, M. I., Fatema, K., Mahmud, M. R., Hoque, M. R., Ali, M. M., & Ferdausi, S. (2025). AttenGene: A deep learning model for gene selection in PDAC classification using autoencoder and attention mechanism for precision oncology. Well Testing Journal, 34(S3), 705–726.
4. Vani, M., & Dadlani, D. (2023). Smart home – “Aashraya” IoT automation system. International Journal of Computer Technology and Electronics Communication, 6(1), 6393–6403.
5. Chaba, A. (2018). A platform-independent API integration architecture for scalable enterprise commerce solution. International Journal of Research Publication in Engineering, Technology and Management, 1(1), 9–13.
6. Rehan, H., Sunkara, G., Sannamuri, V., Malik, M., Jayabalan, K., & Shanthi, K. (2025, September). DeepShield: Privacy-preserving malware classification using split learning. In 2025 6th International Conference on Electronics and Sustainable Communication Systems (ICESC) (pp. 1812–1819). IEEE.
7. Vemireddy, S. (2022). Modernizing enterprise financial platforms through distributed cloud architectures. International Journal of Science, Research and Technology (IJSRAT), 5(2), 7420–7426.
8. Beeram, S. (2024). AI-driven intelligent traffic management in Microsoft Azure: Predictive routing for resilient cloud applications. International Journal of Advanced Engineering Science and Information Technology, 7(4), 14534–14538.
9. Mathew, A., & Romasco, L. (2024). Forensic Investigation of Artificial Intelligence Systems. Research Updates in Mathematics and Computer Science Vol. 4, 154–164.
10. Awopejo, T. E., Adigun, P. O., & Oyekanmi, T. T. (2023). Emerging applications of artificial intelligence and machine learning in modern earthquake science. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8142–8154.
11. Sivakumer, D. (2024). The role of work models in influencing organizational performance and employee wellbeing: A comparative study on full-time, remote, and hybrid paradigms. International Journal of Advanced Engineering Science and Information Technology, 7(4), 14496–14510.
12. Gopinathan, V. R. (2023). Intelligent Cloud Security through Continuous Threat Detection and Risk Assessment. International Research Journal of Innovative Engineering, 7(6), 13571–13581.
13. Onteddu, A. R., Kundavaram, R. M. R., & Naveen, V. J. (2021). AI and deep learning-based intelligent drug recommendation system for patient health monitoring in IoT-enabled healthcare. Journal of Informatics Education and Research, 1(3), 80–92.
14. Suddala, V. R. A. K. (2024). Machine learning for operational excellence: Real-world applications. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 13917.
15. Soundappan, S. J. (2023). Designing Intelligent Enterprise Platforms Using Machine Learning Driven API Engineering and Cloud Native Security. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9074–9081.
16. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273–287.
17. Chevva, P. (2025, June). Balancing Accuracy and Efficiency in Distributed Machine Learning Systems: A Framework for Policy and Risk Management. In International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025) (pp. 702–712). Atlantis Press.
18. Padmanabham, S. (2022). Enterprise identity and access management architecture for large financial institutions. International Journal of Research and Applied Innovations, 5(1), 9486–9490.
19. Pasumarthi, H. (2024). Engineering Large-Scale WMS Integrations: A Practical Guide to Implementing Blue Yonder with IBM ACE, Datapower, MQ, and SAP. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(2), 10008–10016.
20. Balaraman, N. K., Patel, K., Mahendran, P. K. R., & Kasarla, N. R. (2025, August). AI Driven Predictive Maintenance in Water and Wastewater Systems: Enhancing Efficiency, Reliability, and Sustainability. In 2025 IEEE 16th Control and System Graduate Research Colloquium (ICSGRC) (pp. 93-98). IEEE.
21. Hoque, M. J., Akter, F., & Mohammad, A. R. (2019). Data-Driven Decision Support System for Restaurant Business Optimization. American Journal of Economics and Business Management, 2(2).
22. Bandaru, P. K. (2024). Testing multi-ECU communication networks in software-defined vehicles. International Journal of Research and Applied Innovations, 7(4), 11178–11183.
23. Sabin Begum, R., & Sugumar, R. (2019). Novel entropy-based approach for cost-effective privacy preservation of intermediate datasets in cloud. Cluster Computing, 22(Suppl 4), 9581–9588.
24. Koganti, H. (2024). Beyond reactive scaling: A review of AI-driven proactive and context-aware auto-scaling in cloud-edge environments. International Journal of Engineering & Extended Technologies Research, 6(3), 8175–8183.
25. Gangavarapu, R., Kundurthy, O. H., Shirdi, A., Vikram, S., Eripilla, J., & Jonnalagadda, A. K. (2025, July). Model-Centric Data Validation: A Feedback-Loop Approach to Dynamic Quality Control. In 2025 3rd World Conference on Communication & Computing (WCONF) (pp. 1–7). IEEE.
26. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
27. Polamarasetty, V. K., Reddem, M. R., Ravi, D., & Madala, M. S. (2018). Attendance system based on face recognition. International Research Journal of Engineering and Technology, 5(4).
28. Vimal Raja, G. (2024). Intelligent data transition in automotive manufacturing systems using machine learning. International Journal of Multidisciplinary and Scientific Emerging Research, 12(2), 515–518.
29. Anand, L. (2022). Integrating Kubernetes Microservices with Privileged Access Security and Real-Time Fraud Detection for Modern Enterprise Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(5), 7453–7461.
30. Alam, A., Gazi, M. S., Abdullah, S. M., Tasnim, M., Himeluzzaman, M., Nabil, M. A., ... & Akter, S. (2021). Quantum-resilient federated intrusion detection: A hybrid quantum-classical framework for safeguarding US critical infrastructure in the post-quantum era. International Journal of Advances in Signal and Image Sciences, 7(1), 57–72.
31. Chandra, G., Redd, P., & Kadali, R. S. (2022). Lightweight Linux–Powered Edge Systems: Facilitating Secure and Efficient Container Workloads at the Edge. J. Electrical Systems, 18(4), 151–161.
32. Mohan, A. (2025). Recommender Systems in the Insurance Sector: Personalizing Customer Experiences. Journal of Computer Science and Technology Studies, 7(5), 129–133.
33. Abd-Rouf, M. S. K., Adigun, P. O., Alalade, E. O., Oyekanmi, T. T., Faniyi, A. J., Oladapo, B., Awopejo, T. E., Adegoke, O. S., Jamiu, A., Michael, O. B., Obisesan, A., Ajala, S., Adekanye, M. A., Yambali, P. M., & Abd-Rouf, A. B. (2024). From molecular profiling to predictive algorithms: A conceptual machine-learning framework for mechanism-informed therapy selection in multidrug-resistant cancer. International Journal of Science, Research and Technology (IJSRAT), 7(3), 12085–12101.
34. Karakondu, M., Jambagi, G., & Tatavarthi, S. (2024). Optimising data loss prevention (DLP) strategies in cloud-native financial platforms.
35. Bellundagi, M. (2022). Performance Optimization Techniques for Enterprise Java Applications Using Middleware and Messaging Systems. International Journal of Computer Technology and Electronics Communication, 5(3), 5158–5168.
36. Raja, G. V. (2020). Metadata gets a makeover: The machine learning approach. International Journal of Computer Technology and Electronics Communication, 3(6), 2900–2903.
37. Islam, M. S., Shokran, M., & Ferdousi, J. (2024). AI-powered business analytics in marketing: Unlock consumer insights for competitive growth in the US market. Journal of Computer Science and Technology Studies, 6(1), 293–313.
38. Chundi, V. R. K. (2025). AI-based Sustainable Vehicle Monitoring System for Existing Internal Combustion Vehicles. London Journal of Research In Computer Science and Technology, 25(3), 1–7.
