In many enterprise change management processes, risk assessment is still performed manually, while recurring low-risk change patterns may be pre-authorized or assigned default classifications. This paper presents an automated, quantitative risk assessment framework for software change requests that combines large language model (LLM) evaluations of change artifacts with rule-based operational risk scoring. With this system, risk is divided into two orthogonal dimensions: change risk, which evaluates plaintext change plans, test plans, rollback plans, based on a combination of LLM inference and timing and blast radius heuristics; and asset risk, which uses operational metrics such as code coverage, availability, incident history, and open defects to assess the operational health of the target software asset. These scores are adjusted by configurable multipliers for environment type, asset business criticality, and peak event periods. Deployed within a large financial technology organization and integrated with an ITSM platform, a messaging platform, and operational metrics databases, the framework has evaluated tens of thousands of change requests in production. This paper describes the system architecture, the scoring algorithms, the LLM integration strategy, and discusses design decisions that allow the framework to operate reliably at enterprise scale while remaining interpretable and auditable.