Enhancing Machine Learning Outcomes in Banking Through Effective Data Governance Strategies

Abstract

The Integration of machine learning (ML) in the banking sector is changing the way financial institutions deal with fraud detection, credit risk sensitivity, loan forecasting, and customer individualization. Nonetheless, these ML applications are highly dependent on the quality of the data they are based on, consistency and regulatory compliance. It is a review study of how data governance can improve the effectiveness, transparency, and accountability of ML models in banks. It describes how powerful data governance policies aid the entire ML chain of events, including the acquisition of data, training, and deployment model, without compromising its privacy laws and regulatory requirements like GDPR, RBI standards, and Basel directives. The paper rationally investigates the actual investment opportunities of ML in banking, the principles of data governance, and a direct interaction between the two. An inclusive literature survey points out the latest research findings, issues like data silence and past infrastructure, and the course of action in the future of flexible and proportional oversight strategies. With the help of this work, the financial institutions be able to gain a more precise idea of how to harmonize data governance and ML practices in a manner that allows them to achieve innovation, risk reduction, and create confidence in stakeholders in the digital banking age.