A Symbolic Approach to Detecting Structural Risk in Financial Networks Using Graph-Based Constraint Solving

Systemic risk in financial systems is frequently thought to rise largely from market volatility, but the structural complexity of inter-institutional relationships plays a key role in it. However, traditional models often rely on stochastic processes or empirical data, but sometimes can’t capture deterministic vulnerabilities embedded within a network’s architecture. This paper introduces a symbolic framework for identifying structural risk in financial networks using graph based constraint solving. In this, institutions are modelled as nodes in a directed, weighted graph where edges represent financial dependencies—which include obligations, exposures, or liquidity lines. Constraints symbolically encode capital thresholds, solvency conditions, and counterparty relationships. With a constraint based satisfaction engine, hypothetical scenarios of node failure to detect deterministic propagation paths can be explored, which reveals hidden system vulnerabilities. Validation with synthetic networks is proposed with preliminary analysis indicating how symbolic reasoning can expose non-obvious critical nodes and substructures. This approach provides a transparent, reproducible, and data-agnostic method for systemic risk assessment, suitable for exploratory modeling and early warning system design.