From Data to Decisions: Real-Time Risk Management Using Multiagent Systems

ABSTRACT

In increasingly complex risk environments, traditional sequential and manual risk identification approaches are reaching their limits. Multiagent systems offer an innovative and scalable architecture in which autonomous, specialized agents analyze distinct risk domains, such as financial stability, operational disruptions, and regulatory compliance, and synthesize their findings into a consolidated risk assessment. Drawing on a case study from a global logistics enterprise, we demonstrate how a multiagent system enables holistic and near-instantaneous risk evaluations. Grounded in sociotechnical systems theory, the approach integrates technical capabilities with human, structural, and task factors to jointly optimize risk management outcomes. The system achieved comprehensive risk assessments in under 62 seconds and still offered domain-specific analytical depth. Although multiagent systems require careful orchestration of agent communication and knowledge integration, the approach enhances efficiency, consistency, and the ability to detect interdependencies between risk categories.

JEL Classifications: M41; M15; M48; G32; C88.