AI-Driven Explainable Decisioning in Pega: Enhancing Transparency across Regulated Enterprise Systems

Abstract

In highly regulated sectors such as finance, healthcare, and telecommunications, artificial intelligence (AI) has evolved from being a supplementary analytical component to serving as the core engine for operational decisioning and process optimization. However, as AI models increasingly assume responsibility for mission-critical decisions such as credit risk evaluation, fraud detection, and personalized recommendations the demand for transparency, accountability, and explainability has become more urgent than ever. Traditional AI systems often function as opaque “black boxes,” raising ethical and legal questions surrounding bias, fairness, and compliance with data protection regulations.

This paper presents a comprehensive exploration of Pega’s AI-driven Explainable Decisioning Framework, emphasizing how Explainable AI (XAI) principles are seamlessly embedded within Pega Customer Decision Hub (CDH) and Adaptive Decision Manager (ADM) to deliver auditable, interpretable, and regulation-aligned decision outcomes. The study introduces an Explainable Decisioning Architecture (EDA)   a modular construct that operationalizes transparency by integrating interpretability mechanisms, bias diagnostics, and governance alignment with global standards, including the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and NIST AI Risk Management Framework (AI RMF). Through this architecture, enterprises can sustain trust and accountability in AI-driven decisions while meeting compliance and ethical obligations at scale.