A Symbolic Approach to Detecting Structural Risk in Financial Networks Using Graph-Based Constraint Solving
Ananya Bhat
Affiliation: Novi High School
IJSCAR Vol. 2, Issue 2 (2025) · pp. 14–15
Abstract
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.
Keywords: Systemic Risk, Symbolic Computation, Constraint Solving, Financial Networks, Graph Theory, Risk Modeling