Intelligent Software Testing and Continuous Delivery Frameworks for Financial Platforms with Machine Learning–Based Fraud Prevention

Article Sidebar

PDF
Published: 2023-10-12
DOI: https://doi.org/10.15662/IJRPETM.2023.0606017
Keywords:
Intelligent Software Testing, Continuous Delivery, DevOps, Financial Platforms, Machine Learning, Fraud Detection, Risk-Based Testing, CI/CD, Automated Testing, Financial Security

Main Article Content

Pal Halvorsen
Independent Researcher, Switzerland

Abstract

Financial platforms operate in highly regulated, high-risk environments where software defects and security vulnerabilities can lead to severe financial, legal, and reputational consequences. The rapid evolution of digital banking, mobile payments, and fintech ecosystems demands intelligent software testing and continuous delivery (CD) frameworks capable of ensuring reliability, security, scalability, and compliance. This study explores the integration of intelligent software testing methodologies with continuous delivery pipelines tailored for financial systems, incorporating machine learning–based fraud prevention mechanisms. The proposed framework leverages automated testing strategies, risk-based testing, AI-driven test case generation, and real-time monitoring within DevOps practices. Additionally, it embeds machine learning models into the deployment lifecycle to detect anomalous patterns and prevent fraudulent activities proactively. By integrating fraud detection models into CI/CD workflows, financial institutions can achieve secure, resilient, and adaptive software releases. The research outlines architectural components, governance mechanisms, data pipelines, and validation strategies for implementing such frameworks. The findings demonstrate that intelligent automation combined with predictive fraud analytics significantly enhances system reliability, reduces release cycle time, improves compliance adherence, and strengthens proactive threat detection in modern financial platforms.

Article Details

Issue

Vol. 6 No. 5 (2023): International Journal of Research Publications in Engineering, Technology and Management

Section

Articles

How to Cite

Intelligent Software Testing and Continuous Delivery Frameworks for Financial Platforms with Machine Learning–Based Fraud Prevention. (2023). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(5), 9755-9764. https://doi.org/10.15662/IJRPETM.2023.0606017

References

1. Genne, S. (2022). Designing accessibility-first enterprise web platforms at scale. International Journal of Research and Applied Innovations, 5(5), 7679–7690.

2. Keezhadath, A. A., Kota, R. K., & Selvaraj, A. (2021). Dynamic pricing optimization for global hospitality: Real-time data integration and decision making. American Journal of Autonomous Systems and Robotics Engineering, 1, 131–165.

3. Navandar, P. (2022). SMART: Security model adversarial risk-based tool. International Journal of Research and Applied Innovations, 5(2), 6741–6752.

4. Surisetty, L. S. (2023). Proactive Threat Mitigation in API Ecosystems through AI-Powered Anomaly Detection. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(1), 7633-7642.

5. Gangina, P. (2022). Resilience engineering principles for distributed cloud-native applications under chaos. International Journal of Computer Technology and Electronics Communication, 5(5), 5760–5770.

6. Panda, M. R., & Kondisetty, K. (2022). Predictive fraud detection in digital payments using ensemble learning. American Journal of Data Science and Artificial Intelligence Innovations, 2, 673–707.

7. Anumula, S. R. (2022). Transparent and auditable decision-making in enterprise platforms. International Journal of Research and Applied Innovations, 5(5), 7691–7702.

8. Anand, L., & Neelanarayanan, V. (2019). Feature selection for liver disease using particle swarm optimization algorithm. International Journal of Recent Technology and Engineering, 8(3), 6434–6439.

9. Chennamsetty, C. S. (2023). Standardizing Software Delivery: Unified Data Models and Scalable Infrastructure for Subscription Ecosystems. International Journal of Computer Technology and Electronics Communication, 6(2), 6658-6665.

10. Inampudi, R. K., Pichaimani, T., & Surampudi, Y. (2022). AI-enhanced fraud detection in real-time payment systems: Leveraging machine learning and anomaly detection to secure digital transactions. Australian Journal of Machine Learning Research & Applications, 2(1), 483–523.

11. Mudunuri, P. R. (2022). Engineering audit-ready CI/CD pipelines for federally regulated scientific computing. International Journal of Engineering & Extended Technologies Research, 4(5), 5342–5351.

12. Ponlatha, S., Umasankar, P., Balashanmuga Vadivu, P., & Chitra, D. (2021). An IoT-based efficient energy management in smart grid using SMACA technique. International Transactions on Electrical Energy Systems, 31(12), e12995.

13. Hebbar, K. S. (2022). Machine learning-assisted service boundary detection for modularizing legacy systems. International Journal of Applied Engineering & Technology, 4(2), 401–414.

14. Prasanna, D., & Santhosh, R. (2018). Time orient trust based hook selection algorithm for efficient location protection in wireless sensor networks using frequency measures. International Journal of Engineering & Technology, 7(3.27), 331–335.

15. Balaji, K. V., & Sugumar, R. (2022, December). A Comprehensive Review of Diabetes Mellitus Exposure and Prediction using Deep Learning Techniques. In 2022 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) (Vol. 1, pp. 1-6). IEEE.

16. Gaddapuri, N. S. (2021). Big data storage observation system. Power System Protection and Control, 49(2), 7–19.

17. Murugamani, C., Saravanakumar, S., Prabakaran, S., & Kalaiselvan, S. A. (2015). Needle insertion on soft tissue using set of dedicated complementarily constraints. Advances in Environmental Biology, 9(22 S3), 144–149.

18. Ponugoti, M. (2022). Integrating API-first architecture with experience-centric design for seamless insurance platform modernization. International Journal of Humanities and Information Technology, 4(1–3), 117–136.

19. Vimal Raja, G. (2021). Mining customer sentiments from financial feedback and reviews using data mining algorithms. International Journal of Innovative Research in Computer and Communication Engineering, 9(12), 14705–14710.

20. Mathur, T., Muthusamy, P., & Mohammed, A. S. (2019). Federated Learning for Performance Anomaly Detection in Distributed Data Centers. European Journal of Quantum Computing and Intelligent Agents, 3, 33-66.

21. Ananth, S., Kalpana, A. M., & Vijayarajeswari, R. (2020). A dynamic technique to enhance quality of service in software-defined network-based wireless sensor network using machine learning. International Journal of Wavelets, Multiresolution and Information Processing, 18(1), 1941020.

22. Sreekala, K., Rajkumar, N., Sugumar, R., Sagar, K. D., Shobarani, R., Krishnamoorthy, K. P., & Yeshitla, A. (2022). Skin diseases classification using hybrid AI based localization approach. Computational Intelligence and Neuroscience, 2022(1), 6138490.

23. Nagarajan, C., Neelakrishnan, G., Akila, P., Fathima, U., & Sneha, S. (2022). Performance analysis and implementation of 89C51 controller based solar tracking system with boost converter. Journal of VLSI Design Tools & Technology, 12(2), 34–41.

24. Kamadi, S. (2021). Risk exception management in multi-regulatory environments: A framework for financial services utilizing multi-cloud technologies.

25. Perla, S. (2018). Modern practices in software testing and quality assurance. Nanotechnology Perceptions, 14, 155–163. https://d1wqtxts1xzle7.cloudfront.net/122737533/2_2018_Modern_Practices_in_Software_Testing_and_Quality_Assurance_1_-libre.pdf?1746930042=&response-content-disposition=inline%3B+filename%3DModern_Practices_In_Software_Testing_And.pdf&Expires=1771398914&Signature=LemD-3p1e5GTy0-uipklYsYpyzRP2CKTEzRx5SR7NUT7JxsaqHeZEjHNlEknI5IporHGiCykP~5dGYk4-UnLrOcs9~~8cHeuKj7z3j44j5FyvUdjZUyhUTrJkIbxLpNfA-u2aw-48iSKXQG5wBb8JfT9TeY7dGG5pgGvVFHoA9oTCA-P-IbakdKzl5BHgGlrEwz7Mhsj2d78jnCHwci1hZHvb35HiGP7E3bzLnCo9JepjSulSjLQg2SwqdAQVWhUT4GQU5bVyx~W6k~I7UrkXoZchEKePhRUttzbAWWu8lorTL7R33SxoO3yp1i1IwMV91jLhY1b-dWTeNeJlZBEWg__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA

26. Inbavalli, M., & Arasu, T. (2015). Efficient analysis of frequent item set association rule mining methods. International Journal of Scientific & Engineering Research, 6(4).

27. Adari, V. K. (2021). Building trust in AI-first banking: Ethical models, explainability, and responsible governance. International Journal of Research and Applied Innovations, 4(2), 4913–4920.

28. Vaidya, S., Shah, N., Shah, N., & Shankarmani, R. (2020, May). Real-time object detection for visually challenged people. In Proceedings of the International Conference on Intelligent Computing and Control Systems (pp. 311–316). IEEE.