How to assure customer trust and revenue protection through genAI invoicing

Commercial context

In today's telecom industry, CSPs emphasize choice, flexibility, and simplicity to deliver outstanding customer experiences. However, despite their commitment to excellence, billing remains an ongoing challenge that requires substantial attention and resources. Billing problems create significant customer frustration. Industry reports indicate that bill-related issues account for up to 60% of all calls to CSP contact centers, and billing inaccuracies directly contribute to 7-10% of annual customer churn, demonstrating substantial impact on customer retention.

Beyond damaging customer relationships, billing errors lead to considerable financial losses. For instance, top-tier CSPs allocate more than $50 million each year just to handle billing-related customer support calls. Additionally, they spend over $80 million annually on goodwill credits to resolve billing disputes with unhappy customers. The financial consequences extend even further. Global CSPs experience approximately $40 billion in revenue leakage every year, with billing errors playing a major role in these losses. Specifically, non-fraudulent billing mistakes alone cost CSPs about 2.92% of their total revenues, highlighting the urgent need for improved billing accuracy.

The evidence clearly demonstrates that accurate billing is crucial for maintaining customer trust and ensuring revenue. However, quality assurance (QA) methods for billing are often applied at random, and frequently occur only after the billing cycle ends. Most QA processes remain essentially incomplete, often involving manual or semi-manual checks limited by predetermined rules. Consequently, error rates stay unacceptably high, and customers frequently receive bills with errors that were not detected in the first place, while unevenly distributed QA workloads demand unnecessary additional computing power.

Billing teams face significant challenges in both identifying and resolving errors. They lack clear and transparent UI user interfaces to flag all detected issues and options for resolution. This is why customer service representatives routinely issue goodwill credits simply to retain frustrated customers. Additionally, billing errors postpone customer payments, which immediately impacts cash flow and revenue recognition.

The solution

To address these systemic issues, the ‘AI-powered billing QA to tackle revenue leaks and enhance customer satisfaction’ Catalyst project implements genAI to detect anomalies and potential billing errors during the production phase. By combining this with an event-driven real-time billing (RTB) approach, data analysis can occur during the billing cycle rather than after it concludes. Consequently, it enables constant data analysis for earlier anomaly detection before bills reach customers. Ultimately, these predictive capabilities will enable CSPs to strengthen customer confidence, ensure billing precision, and reduce revenue loss.

The platform processes comprehensive datasets including usage statistics, CRM information, customer service records, and financial data. Furthermore, its genAI models detect complex patterns while continuously learning and adapting. Unlike conventional QA systems limited by fixed rules, this solution identifies discrepancies based on actual customer behavior patterns.

Significantly, the solution integrates smoothly with current billing systems to ensure uninterrupted operations. By analyzing data throughout the cycle, it balances QA workloads more effectively. This approach reduces peak-period strain while improving accuracy and minimizing manual corrections. Operational benefits include fewer undetected errors, lower overhead costs, and improved customer satisfaction scores. Additionally, CSPs gain from decreased revenue loss, reduced complaint volumes, and diminished reliance on goodwill credits.

As billing complexity increases, this solution offers CSPs scalable quality control without proportional cost increases. By implementing GenAI throughout the entire billing process, it creates an intelligent, real-time assurance system. Ultimately, this helps communication providers enhance customer trust, safeguard revenues, and accelerate operations with greater confidence. The Catalyst makes use of TM Forum APIs, namely TMF678 Bill Management, TMF620 Catalogue, TMF-C030 Bill Presentment and TMF921 AI Management Framework. In addition, it makes use of the Business Process Framework (eTOM).

Wider application and value

Kim Taylor, Manager Digital Billing Engineering at Telus, noticed a range of direct benefits during Telus’ engagement in the project. Those included reduced customer churn, lower costs (fewer support calls and manual rework), reduced revenue loss, boosted customer trust, and efficient scaling without added operational overheads. Taylor also noted wider impacts, including accelerating the adoption of AI in core telecom operations.

The Catalyst project delivered significant benefits by enabling CSPs to detect and resolve billing anomalies proactively before customers received their bills. First, customer satisfaction improved dramatically since bills now matched expectations more accurately. As a result, customer churn decreased by 35%.

Additionally, call center traffic dropped substantially, saving millions of dollars each year. Moreover, the need for goodwill credits fell by 80% as billing accuracy improved. This directly enhanced revenue assurance, with billing errors reduced by the same percentage.

The real-time billing approach also accelerated revenue collection. Specifically, payment times shortened by 25% thanks to faster processing. Finally, the more balanced distribution of QA workloads decreased computing resource usage while improving sustainability.