Billing systems must evolve to keep with the reality of modern, complex networks. Customer trust and revenue assurance hinge on accuracy, yet legacy QA approaches still rely on static, rule-based checks at the end of the billing cycle. This results in costly delays, manual errors, and millions lost annually through billing-related support calls and goodwill credits. This Catalyst proposes a new approach: using genAI and real-time billing (RTB) to predict anomalies before bills reach customers. Instead of waiting until production, the platform analyzes data mid-cycle, enabling earlier, smarter detection. It works by ingesting large, diverse datasets—including usage, CRM, point-of-contact, and financial data—and applying genAI models trained to detect subtle, evolving patterns. These models continuously learn, improving anomaly detection and adapting to new billing structures like IoT and dynamic pricing. Unlike traditional QA systems, this solution identifies issues based on real-world behavior, not just pre-defined thresholds. Importantly, it integrates with existing billing infrastructure, supporting smooth adoption and operational continuity. Mid-cycle analysis distributes QA workloads more evenly, easing end-of-cycle pressure, boosting billing accuracy, and reducing manual rework. The platform aims to cut undetected anomalies, reduce operational overhead, and improve CSAT and NPS through accurate, proactive billing experiences. CSPs also benefit from reduced revenue leakage, fewer complaints, and less reliance on goodwill credits. As complexity grows, this Catalyst provides CSPs with a critical advantage: the ability to scale billing quality without scaling costs. By applying genAI at the back-end, not just the front-end, this Catalyst turns billing assurance into a real-time, intelligent, and cost-efficient process—helping CSPs build trust, protect revenue, and move faster with confidence.