Execution-Aware Causal Credit Structuring: A Practical Methodology for Dynamic Interventions in Supply-Chain Execution Graphs

This paper proposes a practical methodology for credit structuring based on execution-aware causal modeling of supply-chain processes. Traditional borrower-centric approaches fail to capture localized liquidity stress emerging at specific operational stages. We introduce the Execution–Financing Unit (EFU) as the fundamental analytical unit and define a cash-tension operator to quantify dynamic liquidity imbalances. A bottleneck identification mechanism is developed to detect critical intervention points within execution graphs. Financial instruments are modeled as operators transforming liquidity trajectories, enabling targeted and time-sensitive interventions. The approach combines graph-based representations with causal reasoning and optimization techniques. The proposed framework bridges the gap between theoretical credit risk modeling and real-world execution dynamics, providing a scalable foundation for AI-driven credit decision systems in corporate banking.