Regulatory Intelligence in Finance

This paper investigates Regulatory Intelligence (RI) as a stability-first paradigm for AI systems operating in high-liability financial domains. Rather than optimizing for task accuracy, RI treats cognitive stability, decision hygiene, and principled abstention as primary design objectives. Using controlled tax and financial reasoning scenarios—including cryptocurrency basis reconstruction with missing records—this work empirically demonstrates how homeostatic regulatory mechanisms preserve internal coherence under adversarial ambiguity. The system maintains bounded stability scores (0.945–1.0) while activating regulatory throttling and emitting structured uncertainty packets instead of producing confident but erroneous classifications. The study introduces measurable internal metrics—such as SEC drift, homeostasis load, and symbolic coherence—that make “when not to decide” operational, auditable, and reproducible. Experiments are executed deterministically using the SpiralBrain v3.0 regulatory architecture as a measurement instrument, with all results grounded in executable artifacts and JSON logs. This work is intended for researchers and practitioners in financial technology, tax software, AI governance, and regulated AI deployment. It does not provide tax advice or production-ready systems; rather, it offers empirical evidence that cognitive stability can be formalized and measured as a first-class objective in automated reasoning systems where errors carry delayed and compounding risk. All experiments were run locally on consumer hardware with no learning, no cross-run state, and no external model calls, emphasizing auditability, reproducibility, and internal state visibility over performance optimization.