Data-Driven Change Control: Algorithmic Risk Evaluation in Financial and Legal Decision Frameworks

Abstract

The growing dependence of large organizations on algorithmically mediated decision systems has profoundly reshaped the architecture of risk governance, particularly within enterprise Change Control Boards, which are responsible for approving, delaying, or rejecting modifications to complex technological and organizational infrastructures. Change Control Boards historically relied on expert judgment, financial forecasting, and legal compliance checks performed by human analysts, but these mechanisms have proven insufficient in environments characterized by high operational velocity, regulatory complexity, and data-intensive risk landscapes. The emergence of predictive artificial intelligence systems capable of integrating financial, legal, and operational data has generated both unprecedented opportunities and serious epistemic challenges. Recent work on predictive risk scoring for Change Advisory Boards has demonstrated that algorithmic systems can anticipate downstream failures and compliance violations with a level of granularity previously unattainable through traditional risk matrices, but these systems also introduce new forms of opacity, bias, and governance uncertainty (Varanasi, 2025). This article develops a comprehensive theoretical and empirical framework for understanding how algorithmic risk scoring models reshape decision-making authority, accountability structures, and organizational rationality within Change Control Boards when financial and legal artificial intelligence systems are integrated into enterprise environments.

Drawing on a synthesis of scholarship in machine learning fairness, legal artificial intelligence, financial risk modeling, and autonomous database management, this study conceptualizes Change Control Boards as socio-technical institutions whose epistemic foundations are being reconfigured by predictive models that quantify uncertainty, assign probabilistic risk values, and recommend intervention strategies. Building on political philosophy perspectives on algorithmic fairness and bias, the article argues that predictive risk scoring does not merely support human decision makers but actively transforms how risk itself is defined, communicated, and legitimized within organizations (Binns, 2018; Angwin et al., 2016).