Intelligent Data Quality Management Frameworks for AI-Driven Financial Decision Systems

Abstract

The study analyses smart data quality management models of the AI-based financial decision-making frameworks, overcoming the essential drawbacks of the traditional, manual ones. By considering secondary mixed process, the investigation also summarises the current literature, empirical evidence, and actual case studies to assess the use of AI-based data quality control, data governance, and scalability structures to improve the quality of decisions. The key trends that have been discovered include time-consuming, unstable, and imprecise traditional practice, but AI-moderated automation increases the detection of anomalies by up to 90%, compliance, and real-time financial decision-making. JP Morgan and Capital One Case Evidence Case studies in both financial institutions affirm that the current deep data quality paradigms, managed lifecycle, and embedded in data, are necessary to support trustworthy and sustainable AI-based financial decision-making.