In today’s telecom world, where choice, flexibility, and simplicity go hand-in-hand, CSPs deploy several strategies and resources to drive excellent customer experience at the point of contact. Despite all efforts, CSPs continue to spend significantly on handling billing-related calls. Such calls also result in other expenses, such as providing multi-million-dollar goodwill credits to dissatisfied customers. Our innovative approach is to shift the application of Gen AI at the point of contact and apply it to proactively identify anomalies and potential bill errors while they are still in production, and with an event-based approach for real-time billing (RTB), the data can be analyzed mid-cycle. Quality assurance (QA) is critical in identifying pre-production billing errors. In a batch-based approach, CSPs rely heavily on traditional rule-based QA parameters to identify and eliminate errors at the end of the cycle. It is time-restrictive and often overlooks human-based errors and discrepancies. Our proposed solution, Gen-AI-powered Predictive QA for Billing, harnesses the power of RTB with Gen-AI to isolate mid-cycle anomalies beyond rule-based methodologies. This reduces end-of-cycle production pressure and leverages Gen AI to proactively detect and address billing anomalies. With advanced predictive capabilities, CSPs can enhance customer trust, safeguard billing accuracy & mitigate revenue leaks.