AXION FinTech AI: A Deterministic and Governance-Constrained Credit Decision Framework Integrating Stochastic Risk, Irreversibility, and Causal Inference

This study introduces a deterministic and governance-constrained credit decision framework that integrates stochastic risk modeling, irreversibility-aware capital dynamics, network-based systemic risk, liquidity constraints, information uncertainty, and causal inference. Unlike traditional credit risk models that focus on probabilistic prediction, the proposed AXION FinTech AI framework formalizes decision-making as a constrained optimization problem under uncertainty, where risk, time, irreversibility, and information quality jointly define feasible decision spaces. The model incorporates hazard-based survival analysis, stochastic differential equations, network contagion mechanisms, entropy-based uncertainty measures, and structural causal inference. In addition, governance constraints such as human-final authority, deterministic execution, and auditability are embedded directly into the decision process. The results demonstrate that integrating advanced financial mathematics with governance-first principles improves decision stability, transparency, and regulatory alignment. The framework provides a scalable foundation for next-generation financial decision infrastructures.