The business case for AI in revenue cycle management

Revenue cycle management (RCM) covers the entire revenue process from order placement to settlement and underpins the financial health and operational efficiency of the business. But, as the ecosystem complexity continues to increase exponentially, the RCMs that have done a ‘good enough’ job for years are not keeping up.  

Your RCM needs to be built with the same mindset as the rest of the business. If it cannot handle the complexity baked into the current ecosystem and is still using workarounds in legacy solutions, you will feel the impact of holes in the system.  

Holes that ‘leak’ revenue through high costs, inaccuracy, and inefficiency.  

They are leaks that probably sound very familiar as some of the biggest challenges that AI is not just promising to overcome but delivering on. RCM is one of the areas where using AI has the potential to have a quick and significant impact on the health of your business, so how do you get there – fast? 

Getting AI ready

The very first question when looking at utilizing AI for RCM is ‘Can my BSS support AI?’. There are two key aspects to consider when answering this question: firstly, integration and secondly, data quality.  

AI and legacy systems make for a difficult combination. For them to function well together, you may need to put in workarounds that don’t align with the efficiency goals you are looking to achieve. To leverage AI for RCM, you need a BSS system with AI capabilities baked in and ready to be used or that has the option to integrate AI technology from your BSS provider or a third party. This is the straightforward bit – if the answer is yes (for ZIRA customers, be reassured the answer is yes), that’s great! Carry on reading for more on making the most of AI. If the answer is no, then getting in place a BSS with AI-integration capabilities is the first step.

Find out everything you need to know about modern-day BSS.

Learn more

The second component we mentioned was data quality. BSS systems are the data workhorse of your business, organizing and processing huge volumes of data. For day-to-day operations, this needs to be done in a way that is fully compliant with telecom regulations – GDPR, PCI-DSS, etc – and maintains data security. To unlock AI capabilities, this data needs to be accessible, in the right format, and of a sufficient quality for AI systems to interpret. Without good data quality, AI cannot deliver the efficient, more accurate decisions and processes it promises. This is fundamental to the success of any AI-powered tool. Data quality is essential, and the starting point for ensuring data quality is in BSS platforms designed with AI in mind.  

The role of AI in telecom revenue cycle management

Now that we have the underpinning BSS in place, let’s look at how AI can impact Revenue Cycle Management in detail. The high-level argument for using AI in RCM is not a difficult one to make. But when we want to make the more detailed business case, we need to consider where and how to integrate AI for maximum impact. This breaks down into four key areas.  

1. Streamlining billing and invoicing

CSPs are supporting a growing number of models tailored to meet customer needs: usage-based charges, roaming, prepaid, postpaid, and hybrid models. Billing has never been more complicated and, as a result, open to inaccuracy and slow processes.  

AI-driven solutions can digest that complexity and automate billing workflows to improve accuracy and give real-time billing updates that help customers feel in control and give the CSP a better understanding of their relationship with each customer. This is the bare minimum that integrating AI into billing should achieve, but it can also be used to expand what is possible with billing and invoicing.  

Real-time adjustments and predictive billing models  

AI forecasting is one of the most promising AI applications when it comes to billing. It facilitates dynamic pricing, predictive billing, the ability to make real-time adjustments. This gives CSPs an even greater number of options for how they engage with customers allowing both sides to feel the benefits of increased accuracy and flexibility.  

AI-driven workflows

Automation is critical for CSPs given the scale of their operations. A natural follow-on from this is to integrate AI for smarter automation. AI that can understand the nuance of each customer relationship when it comes to rating, charging, and billing allows more processes to be fully automated without the need for manual intervention.  

Fraud detection

Fraud is a nightmare for CSPs, especially when it comes to handling billing. By integrating AI across the billing ecosystem, CSPs can detect possible fraud in real-time and resolve the problems it causes in a much shorter timeframe.  

2. Optimizing dispute and error resolution

Billing errors and disputes on billing have a major impact on relations between CSP and customer. We’ve seen how AI can reduce the occurrence of errors, but it can also make it easier to manage customer relationships if errors or disputes arise.  

AI-powered analytics

By having an AI layer across the RCM, CSPs can identify discrepancies early. This enables CSPs to minimize the time and resources spent addressing issues that arise and minimize the impact of errors to improved revenue assurance.  

Improving dispute management

AI can also be used to automate resolutions for faster dispute handling to minimize damage to customer relationships and reduce churn as a result of poor customer experience. It also allows resources to be prioritized with small disputes requiring no manual intervention and allowing teams to focus on high-impact disputes where an in-person interaction has the potential to add the most value.  

3. Enhancing customer experience  

As well as significantly improving back-office operations. AI can also be used to improve customer experience with a variety of features tailored to the customer.  

Improved visibility

Alongside interpreting the huge volumes of data generated by CSP systems for better internal processes, AI can also translate this data to keep customers better informed and supported. This could be via a dashboard that gives personalized billing insights, through proactive alerts for overdue payments, or real-time balance tracking. The point is that whatever format the CSP chooses, the customer benefits from improved transparency and a personalized experience. This fosters increased customer loyalty with minimal additional effort needed from the CSP.  

AI-enabled chatbots

Alongside offering features like personalized dashboards and alerts, AI-enabled chatbots allow for a greater level of self-service. Reducing the response time for billing queries by leveraging AI that is integrated across other areas of the RCM again improves the overall customer experience. 

4. Ensuring compliance and security  

Alongside the business operation benefits, integrating AI into the RCM can also work to support compliance and security.  

Data handling  

Complying with regulations across a range of geographies can be a challenge. In addition, different data needs different treatment depending on if it is financial or contains personal details, for example. AI can be trained to ensure data is handled correctly and in line with all regulatory requirements with minimal additional resources.  

Threat detection  

AI for RCM can also be used to introduce advanced threat detection so any possible issues can be handled early. It also supports the secure handling of sensitive billing data, reducing risk to customer and business data.  

Best practices  

This is a good, but by no means exhaustive, overview of how AI can be used to improve RCM performance. The business case is clear, but before undertaking an AI journey, businesses need to be prepared for the cost of implementation, the additional training needed to make AI integrate seamlessly as part of the business, and the task of overhauling processes to achieve the desired outcomes.  

It is not a small task but can be made easier by concentrating on three key points: 

The right partner

We discussed the importance of BSS, but it is essential to have the right BSS partner in place that fully understands AI’s potential and how to integrate it into an organisation.  

Start small

Yes, there are ways AI can be used across every aspect of BSS but start off small with pilot schemes, see the benefit and scale. This is the best way to ensure every AI application is reaching its full potential.  

High impact

In addition to starting small, select the use cases that will impact your business most. This allows you to balance starting small with delivering ROI quickly. 

Conclusion

We talk about undertaking an AI journey because as much as AI can have a transformative impact on a business, it isn’t a silver bullet or a magic wand. Deriving AI benefits doesn’t happen overnight. It needs a strategic approach that understands the challenges of AI implementation and is designed to deliver tangible results at every stage. By understanding the range of AI opportunities in RCM and the practical considerations of implementation, businesses are in the best possible position to design an AI strategy that serves them and their customers in the short, medium, and long term.