Information and analytical systems for risk monitoring in enterprise activities

Abstract

Abstract. The article examines theoretical and applied aspects of building information-analytical systems (IAS) for enterprise risk monitoring. It is substantiated that in conditions of non-linearity, fragility, and cascading risk interactions characteristic of the BANI environment, traditional static approaches to risk management (periodic audits, risk matrices, one-off assessments) lose their managerial relevance and require replacement with continuous monitoring using leading indicators, threshold values, and escalation procedures. The evolution of approaches from reactive to proactive and predictive risk management paradigms is analysed with reference to ISO 31000 and COSO ERM standards. The IAS architecture is considered as a sociotechnical system integrating four core circuits: collection of internal and external data, integration and reconciliation of reference data, an analytical core (from quantitative and qualitative methods to machine learning and early warning systems), and visual and procedural support for managerial decision-making (dashboards, triggers, escalations). The limits of risk monitoring automation are defined, and key IAS "blind spots," which are predominantly institutional rather than technical in nature, are identified: diffused accountability, formalistic KRIs, and the absence of a link between forecasts and managerial action. The capabilities and limitations of ML/AI in risk scoring are explored, including model risk, data bias, and the "black box" effect. Particular attention is paid to the Ukrainian context, where full-scale war has transformed the risk profile of enterprises and created a need for IAS as a tool not only of control but also of adaptability and strategic resilience. EU integration factors (CSRD, DORA, ESRS) that shape external demand for formalised risk monitoring are analysed, and a comparison with EU country practices (Poland, Czech Republic, Estonia) is conducted. Prospects for further research in the direction of empirical verification of IAS effectiveness and the development of standard architectural solutions for resource-constrained enterprises are outlined.