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Research Paper | Information Technology | Volume 15 Issue 2, February 2026 | Pages: 1732 - 1734 | United States
Towards Fully Autonomous Financial Data Ecosystems: An Evolutionary Extension of Predictive AI Pipelines
Abstract: Financial data engineering has advanced from labor-intensive manual processes to sophisticated AI-assisted automation. Yet most systems today still rely on reactive mechanisms and frequent human oversight, limiting their ability to keep pace with the relentless velocity, volume, and complexity of modern financial data. This paper proposes Autonomous Financial Data Ecosystems (AFDEs)- a conceptual and architectural evolution that transforms predictive AI pipelines into self-regulating, closed-loop systems. Built upon the established MAPE-K (Monitor?Analyze?Plan?Execute?Knowledge) feedback model, AFDEs introduce predictive scaling, proactive self-healing, adaptive anomaly handling, and continuous knowledge-driven evolution. These capabilities allow infrastructure and intelligence layers to adapt in real time, often without any human intervention. Through large-scale simulations and industry-aligned case studies, the framework demonstrates dramatic improvements: scaling latency reduced from minutes to sub-second levels, pipeline reliability enhanced by approximately 85%, and fraud detection accuracy reaching 99.4% at latencies under 100 milliseconds. Beyond technical gains, AFDEs point toward a future where financial data infrastructure becomes resilient, intelligent, and inherently adaptive- freeing engineers and analysts to focus on higher-value innovation rather than operational firefighting. This work positions full autonomy as the logical next foundation for financial systems in an era of accelerating data demands and regulatory scrutiny.
Keywords: Autonomous AI, Financial Data Engineering, Self-Healing Systems, Predictive Orchestration, MAPE-K Framework, Cloud-Native Autonomy, Adaptive Data Pipelines
How to Cite?: Preeta Pillai, "Towards Fully Autonomous Financial Data Ecosystems: An Evolutionary Extension of Predictive AI Pipelines", Volume 15 Issue 2, February 2026, International Journal of Science and Research (IJSR), Pages: 1732-1734, https://www.ijsr.net/getabstract.php?paperid=SR26228082456, DOI: https://dx.dx.doi.org/10.21275/SR26228082456