Designing a Scalable AI-Driven Data Engineering Framework for Automated Financial Data Management

Abstract

In this paper, the authors suggest a proposal for a cloud-based, AI-driven data engineering framework to manage data in financial systems automatically. Financial growth requires pipelines to be of a traditional nature and therefore, are not very scalable, reliable, and/or governed by traditional governance. The framework proposed is based on cloud-native solutions and artificial intelligence to provide automated ingestion, processing, monitoring, and compliance. The financial transactions analysis written in Python illustrates the concentration of workload, non-uniform distribution of values, time series, and the existence of relationships. Findings suggest that there is an increment in operational efficiency, detection of anomalies, and compliance readiness. The research adds a scalable, smart solution that enables motivated, data-driven decision-making in recognizing the present financial conditions.