AI‑driven framework for contract risk automation and compliance in oracle CPQ

Contract management and quoting processes are mission-critical operations in modern enterprises, yet they remain prone to inefficiencies, compliance risks, and human error-particularly in IT and SaaS sectors where contract complexity is high. This paper introduces the Artificial Intelligence Contract Risk and Intelligence Model (AICRIM), a simulation-validated, prototype-level framework that integrates generative AI (GPT-4) and BERT-based semantic matching into Oracle Configure-Price-Quote (CPQ) systems to perform real-time compliance and risk assessment during SaaS and cloud deal negotiations. AICRIM autonomously identifies anomalies in data privacy clauses, service-level agreements (SLAs), and pricing terms by interpreting unstructured contract text and benchmarking it against GDPR, HIPAA, and ISO 27,001 regulatory standards. The framework is evaluated through structured simulation experiments over a corpus of 500 synthetic SaaS contract documents, using manual legal review and rule-based NLP as baselines. Simulation experiments demonstrate a 27-32% reduction in contract errors, a 38.2% reduction in deal cycle time, and a compliance detection accuracy of 92.1%, with statistically significant improvements over both baselines (p < 0.01). These results are simulation-derived and serve as indicative upper-bound estimates pending real-world validation. AICRIM also embeds AI governance controls-AES-256 encryption, bias monitoring, and structured audit trails-to ensure ethical and auditable deployment. By extending Oracle CPQ via REST API event hooks, this model provides enterprises with a scalable, reproducible approach to contract risk automation.