Adaptive Risk Intelligence for Enterprise Change Advisory Boards Integrating Predictive Analytics Machine Learning and Cyber Resilience Across Insurance and Digital Infrastructures

Abstract

The accelerating pace of digital transformation has fundamentally altered how large enterprises govern technological change, assess operational risk, and preserve organizational continuity. Change Advisory Boards, which historically functioned as procedural oversight bodies for approving and sequencing information system modifications, are now confronted with unprecedented volumes of heterogeneous data, complex cyber threats, and dynamically shifting business environments. This article develops a comprehensive theoretical and methodological framework for embedding predictive risk scoring driven by artificial intelligence into Change Advisory Board decision making, with a particular emphasis on the insurance and digitally mediated financial ecosystem. The study is anchored in recent advances in data driven modeling, scalable machine learning architectures, cyber threat analytics, organizational learning theory, and explainable artificial intelligence, while critically extending the predictive governance paradigm introduced by Varanasi (2025) in the context of automated Change Advisory Board decision support.

Drawing upon the convergence of big data analytics in insurance, adversarial machine learning in cybersecurity, and concept drift in organizational data streams, this article argues that modern Change Advisory Boards must evolve from static approval structures into adaptive intelligence systems. Through an integrative methodological design based on cross domain literature synthesis, this research constructs a descriptive analytical model that explains how predictive risk scoring can transform change governance by quantifying technical, organizational, and cyber security risks in real time. The methodological contribution lies in a text based modeling architecture that unifies data readiness, scalable machine learning, adversarial resilience, and interpretability within a single Change Advisory Board risk governance framework.

The discussion advances a deep theoretical synthesis of organizational memory, machine learning interpretability, adversarial robustness, and real time analytics. It highlights the epistemological shift from rule based governance to probabilistic, learning driven oversight, while addressing ethical, operational, and governance challenges. The article concludes that predictive risk scoring, when embedded within transparent and scalable analytical infrastructures, can redefine Change Advisory Boards as strategic engines of resilience enterprises.