A Data-Driven AI Framework for Governance and Decision Intelligence in Financial Services

Abstract

Financial services companies are swiftly utilizing artificial intelligence (AI) for essential functions such as risk assessment, fraud detection, credit scoring, and regulatory compliance. Many current AI-driven financial systems lack regulation, transparency, and real-time decision-making capabilities, hence undermining their reliability in regulated contexts. Model opacity, bias, and protracted decision-making engender trust difficulties that hinder widespread adoption in critical financial applications. This paper introduces a Data-Driven AI Governance and Decision Intelligence Framework (DD-AIGDI) to address these challenges in financial services. It integrates data governance, machine learning operations (MLOps), explainable AI (XAI), and real-time decision intelligence under a unified framework. It facilitates comprehensive lifecycle management, from data acquisition to decision auditability, ensuring that every phase of the AI pipeline is regulated and traceable. The proposed system may function on streaming financial data, facilitating adaptive decision-making through a hierarchical design that includes data input, governance enforcement, AI modeling, decision orchestration, and auditability modules. This design fosters compliance alignment, reduces bias, and enhances transparency in automated decision-making. Experimental findings indicate that the DD-AIGDI system enhances decision accuracy, reduces latency, and improves regulatory compliance relative to alternative AI pipelines. Large-scale IT provides a dependable, scalable solution for contemporary financial decision intelligence systems.