Mapping Contagion in AI-Integrated Financial Systems

The CFC-Prop framework provides a graph-based method for calculating cascading liquidity failure following an AI vendor compromise. It maps the dependencies between AI providers and financial institutions to measure how a single point of failure within a model provider translates into a liquidity crisis across the broader interbank market. The model treats infrastructure dependencies as directed edges in a graph, allowing researchers to simulate how failure flows from a vendor to an institution and subsequently ripples through liquidity channels to other connected banks.

Mapping the Contagion

The authors model a network consisting of AI vendors, financial institutions, interbank liquidity exposures, and customer accounts. The system uses a stochastic epidemic-and-clearing model to simulate how a compromise at a vendor node triggers a failure sequence and subsequent liquidity impacts. The propagation speed is defined by the latency of incident detection and the rate at which banks can shift to redundant infrastructure. By calculating the expected loss distribution across the network, the model identifies which institutions are most vulnerable based on their degree of centrality and their reliance on high-risk vendors.

How the Early-Warning Mechanism Works

To predict potential failures, the research introduces CFC-GNN, a Graph Neural Network trained to monitor systemic health. The model processes incident telemetry as input node features across the financial graph. The GNN determines if a localized error will escalate into a liquidity crisis by calculating the 'clearing value' of each node—specifically, it evaluates whether the debt-to-equity ratio of an institution, once adjusted for the projected loss of AI-driven revenue or operational capacity, breaches the threshold for clearing its interbank obligations. It performs this by analyzing the density of connectivity and the pattern of anomalous signal propagation across edges, effectively identifying if a 'failed' node will trigger a solvency test failure at its neighbors.

If a vendor shows a spike in error rates, the GNN predicts whether that error will stay localized or reach a threshold that forces liquidity contraction in linked banks. For those building these tools, the mechanism necessitates exposing incident telemetry through standardized interfaces. The GNN requires this granularity to calculate node centrality accurately and to map the ripple effects before they consolidate into a systemic event.

A Practical Example

Imagine a large financial institution that integrates a third-party LLM for automated loan adjudication. If that LLM experiences a sustained outage, the institution needs to switch to a fallback model. In the CFC-Prop model, this is an edge transition. If the institution's primary vendor failure is correlated with the failures of three other major banks using the same infrastructure, the GNN observes the aggregate surge in 'down-time' telemetry. By assessing the institutional coupling, the model signals a high risk of interbank liquidity withdrawal. The failure is about the sudden inability of the bank to process credit requests, which triggers an immediate reassessment of the institution's solvency by its counterparties. Without granular telemetry, the GNN fails to predict liquidity contraction before the third hop in the dependency chain. While the exact threshold for 'systemic criticality' remains sensitive to private data, the model provides a critical relative ranking of institution vulnerability that allows regulators to prioritize oversight even when total visibility is restricted by corporate confidentiality.