(untitled) (gjstudies, 2025)
This paper introduces a novel, bio-inspired computational framework for evaluating and enhancing internal control effectiveness (ICE) within large organizations,moving beyond traditional compliance-based checklists. We propose that internalcontrol systems can be modeled as adaptive, neural-like networks that process financial and operational signals to mitigate risk. Drawing inspiration from distributedbiological systems and swarm intelligence, our methodology conceptualizes controlactivities as autonomous agents operating within a decentralized organizationalecosystem. These agents communicate via a simulated pheromone-based protocol,dynamically allocating audit resources and adjusting control sensitivity based onreal-time risk signals and historical anomaly patterns. We developed a multi-agentsimulation platform populated with synthetic organizational data representing fiveyears of transactions, control logs, and risk events across a hypothetical multinational corporation. Our results demonstrate that the bio-inspired adaptive controlnetwork (BI-ACN) achieved a 34.7% higher detection rate for sophisticated, multivector financial risks compared to a static, rule-based control model, while reducingfalse positives by 22.1%. Furthermore, the system exhibited emergent properties,such as the self-organization of control clusters around nascent risk areas beforethey manifested as significant losses. The framework’s predictive capability, measured by its ability to flag control deficiencies that later correlated with financialstatement errors, showed a precision of 0.87. This research contributes a fundamentally new paradigm for internal control, one that is proactive, self-optimizing, andcapable of evolving with the complexity of modern organizational risk. It challengesthe prevailing audit-centric view of controls as constraints, instead positioning themas an intelligent, distributed nervous system for the enterprise.