Unlocking Reliable Insights: Data Integrity in AI/ML Banking Analytics

Abstract

In the rapidly evolving landscape of AI/ML banking analytics, ensuring data integrity has emerged as a critical imperative. Accurate and reliable insights drive informed decision-making, risk assessment, and customer engagement. This paper delves into the multifaceted challenges of maintaining data quality and integrity throughout the AI/ML pipeline in banking analytics. From tackling inaccuracies and bias to handling missing data and optimizing feature engineering, a comprehensive framework is presented to address these challenges. Ethical considerations and regulatory compliance are also discussed in the context of responsible AI/ML deployment. The paper concludes by outlining future directions for leveraging cutting-edge technologies and collaborative efforts to unlock the full potential of reliable insights in AI/ML banking analytics.