Optimizing Data Management Strategies for Enhanced Performance of Artificial Intelligence in the Banking Sector
Keywords:
Artificial Intelligence, Data Management, Banking Sector, Data Governance, Machine Learning, Financial Technology, Risk Management, Data Quality, Big Data, Digital BankingAbstract
Artificial Intelligence (AI) is transforming the banking sector by automating services, enhancing risk management, improving customer experiences, and detecting fraud. However, the efficacy of AI models is fundamentally reliant on data quality and management strategies. This paper explores optimized data management practices to enhance the performance of AI systems in banking. By reviewing pre-2023 literature and integrating structured methodologies, the study analyzes how robust data governance, integration, cleansing, and ethical handling of data elevate AI capabilities. Furthermore, practical frameworks and visual models are presented to outline best practices for implementation. This research provides insights for financial institutions aiming to balance innovation with operational reliability and compliance.
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