AI-Driven Financial Infrastructure: Deep Learning for Risk Detection, Zero-Downtime System Upgrades, and the Evolution of Intelligent Automation in Life Insurance and Banking
DOI:
https://doi.org/10.15662/IJRAI.2025.0804004Keywords:
AI-driven financial infrastructure, deep learning, risk detection, zero-downtime upgrades, intelligent automation, life insurance, banking, fraud detection, self-healing systems, cloud orchestration, reinforcement learning, real-time analytics, regulatory complianceAbstract
The financial sector is undergoing a paradigm shift toward automation, intelligence, and resilience, driven by the integration of deep learning and AI-based frameworks. This study presents an AI-driven financial infrastructure model that enhances risk detection, ensures zero-downtime system upgrades, and enables adaptive automation in life insurance and banking ecosystems. Leveraging deep learning architectures—such as convolutional and recurrent neural networks—the proposed framework identifies complex risk patterns across high-dimensional financial data, enabling real-time fraud prevention and dynamic policy management. A novel self-healing infrastructure powered by AI orchestrates system updates and migrations without service interruptions, thus maintaining operational continuity and regulatory compliance. Additionally, intelligent automation modules supported by reinforcement learning optimize claim settlements, underwriting processes, and loan portfolio management through contextual decision-making. The research demonstrates that the fusion of deep learning, cloud orchestration, and AI-based automation transforms traditional financial systems into intelligent, scalable, and self-managing digital infrastructures—reshaping the future of life insurance and banking.
References
1. Merćep, A., Mrčela, L., Birov, M., & Kostanjčar, Z. (2021). Deep Neural Networks for Behavioral Credit Rating. Entropy, 23(1), 27. MDPI
2. Gonepally, S., Amuda, K. K., Kumbum, P. K., Adari, V. K., & Chunduru, V. K. (2021). The evolution of software maintenance. Journal of Computer Science Applications and Information Technology, 6(1), 1–8. https://doi.org/10.15226/2474-9257/6/1/00150
3. Konda, S. K. (2024). Zero-Downtime BMS Upgrades for Scientific Research Facilities: Lessons from NASA’s Infrared Telescope Project. International Journal of Technology, Management and Humanities, 10(04), 84-94.
4. DeLise, T. (2023). Deep Semi Supervised Anomaly Detection for Finding Fraud in the Futures Market. arXiv:2309.00088.
5. Raju, L. H. V., & Sugumar, R. (2025, June). Improving jaccard and dice during cancerous skin segmentation with UNet approach compared to SegNet. In AIP Conference Proceedings (Vol. 3267, No. 1, p. 020271). AIP Publishing LLC.
6. “Deep Unsupervised Anomaly Detection in High Frequency Markets”. Journal of Finance and Data Science, 2024. ScienceDirect
7. “A Contrastive Learning Framework for Detecting Anomalous Behavior in Commodity Trading Platforms”. Applied Sciences, 2023, 13(9), 5709. MDPI
8. Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks (Schreyer, Sattarov, et al.), 2019. arXiv
9. Balaji, P. C., & Sugumar, R. (2025, June). Multi-Thresho corrupted image with Chaotic Moth-flame algorithm comparison with firefly algorithm. In AIP Conference Proceedings (Vol. 3267, No. 1, p. 020179). AIP Publishing LLC.
10. FraudJudger: Real World Data Oriented Fraud Detection on Digital Payment Platforms (Deng & Ruan), 2019. arXiv
11. Chunduru, V. K., Gonepally, S., Amuda, K. K., Kumbum, P. K., & Adari, V. K. (2022). Evaluation of human information processing: An overview for human-computer interaction using the EDAS method. SOJ Materials Science & Engineering, 9(1), 1–9.
12. A novel combined approach based on deep Autoencoder and deep classifiers for credit card fraud detection. Expert Systems with Applications, 2023. ACM Digital Library
13. Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances. Expert Systems with Applications, 2021. ScienceDirect
14. Time Series Anomaly Detection: A Survey. ACM Computing Surveys. ACM Digital Library
15. Behavioral risk profiling: Measuring loss aversion of individual investors. Journal of Banking & Finance, 2024. ScienceDirect
16. Detecting Anomalies in Financial Data Using Machine Learning Algorithms. Systems, 2022, 10(5), 130. MDPI
17. Anomaly Detection in Financial Time Series by Principal Component Analysis and Neural Networks. MDPI Algorithms, 2022 (or around) MDPI
18. Exploring Anomaly Detection and Risk Assessment in Financial Markets using Deep Neural Networks. IJIRCST, 2024. ijircst.irpublications.org
19. Lin, T. (2024). The role of generative AI in proactive incident management: Transforming infrastructure operations. International Journal of Innovative Research in Science, Engineering and Technology, 13(12), Article — . https://doi.org/10.15680/IJIRSET.2024.1312014
20. Joseph, J. (2025). The Protocol Genome A Self Supervised Learning Framework from DICOM Headers. arXiv preprint arXiv:2509.06995. https://arxiv.org/abs/2509.06995
21. Gandhi, S. T. (2023). AI-Driven Compliance Audits: Enhancing Regulatory Adherence in Financial and| Legal Sectors. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(5), 8981-8988.
22. Peddamukkula, P. K. (2024). The Impact of AI-Driven Automated Underwriting on the Life Insurance Industry. International Journal of Computer Technology and Electronics Communication, 7(5), 9437-9446.
23. Lanka, S. (2024). Redefining Digital Banking: ANZ’s Pioneering Expansion into Multi-Wallet Ecosystems. International Journal of Technology, Management and Humanities, 10(01), 33-41.
24. A Deep Autoencoder enhanced LightGBM method for credit card fraud detection. (2022) PubMed
25. Reddy, B. T. K., & Sugumar, R. (2025, June). Effective forest fire detection by UAV image using Resnet 50 compared over Google Net. In AIP Conference Proceedings (Vol. 3267, No. 1, p. 020274). AIP Publishing LLC.
26. Amuda, K. K., Kumbum, P. K., Adari, V. K., Chunduru, V. K., & Gonepally, S. (2020). Applying design methodology to software development using WPM method. Journal of Computer Science Applications and Information Technology, 5(1), 1–8. https://doi.org/10.15226/2474-9257/5/1/00146
27. Deep Learning Applications in Financial Time Series Forecasting and Anomaly Detection. Hong Kong Journal of AI and Medicine, 2023. hongkongscipub.com
