Citation :

Robin Verma, 2024. "Building Robust AI Systems in Finance: The Indispensable Role of Data Engineering and Data Quality" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 2, Issue 1: 80-89.

Abstract :

Due to the constantly evolving environment and rapidly growing competition in the financial sector, utilizing AI for systematic goals and gaining optimum benefits has been a major focus. This article analyses the importance of Data Engineering when it comes to the development of sound AI structures in the field of finance. It underscores the need for quality data in any AI project to be effective, efficient, and accurate in its operations. This study focuses on the significance of the global large-scale data architecture, the effective data pipeline, sufficient data governance, and high-quality requirements for efficient financial and artificial business systems. Key consideration areas include the development of good standards in data engineering to ensure quality data, good data quality management frameworks, and constant reviewing and monitoring of data processes. The paper then supports the uses and benefits of AI in finance through case studies and examples nowadays that are backed up by proper data engineering and quality assurance solutions. The results strengthen the argument that no matter how sophisticated the AI systems are, they cannot be optimally utilized within the financial sector if the data that feeds them are not high quality and if the data engineering process is not efficient.

References :

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Keywords :

Artificial Intelligence (AI), Data Engineering, Data Quality, Financial Sector, Scalable Data Architectures, Data Pipelines, Data Governance.