Recent Case Study Highlights How AI Can Reduce Revenue Leakage
Business + Managementposted by April Miller January 19, 2022 April Miller
As the global pandemic extends into another year, individuals continue working in remote or flexible environments. When more employees work outside of traditional office settings, they require efficient and independent telecommunication services. As the demand for reliable services increases, companies must improve the efficiency of their distribution processes.
Many telecommunication companies experience functional limitations due to inefficient technologies. When their service distribution and cash collection programs fail, they may experience a decrease in profits. Program engineers search for solutions to revenue leakage, improving the profitability and reliability of telecommunication companies.
Revenue Leakage for Service Providers
In the digital age, computerized systems are supporting various industries, delivering services, and accounting for the exchange of funds. The combination of human labor and automated computer work increases opportunities for error. Human-induced errors can have adverse effects on consumers and businesses.
When companies overcharge their clients, they may lose business and receive poor customer service ratings. If businesses undercharge their clients, they can experience revenue loss. Researchers found that telecommunication companies lose nearly $38 billion each year from missed collections and fraud.
Revenue assurance processes involve reactive tracing and sourcing the root of the problem. The practice is time-consuming and complex, and sometimes it leads to zero conclusions or solutions.
Other times, investigators find slow servers or inaccurate tariff plans cause revenue leakage. To uncover the full range of influences, engineers have developed a leakage-reduction system using artificial intelligence (AI).
The Case Study
One telecommunication provider in the Middle East significantly decreased their profit losses using a computer learning system. Assessing the common forms of leakage helps engineers develop revenue assurance technologies.
A research team explored the connection between call detail records (CDRs) and billing systems, assessing the different revenue loss routes. The CDRs help telecommunication providers determine the amount of data consumers use to create their monthly bills. CDRs distribute a variety of information to companies, helping them identify a customer’s usage patterns.
The records break down data usage by call durations, phone numbers, call type, and results. Mediation systems collect the CDRs and convert them into a compatible format. After reconfiguring the information, the billing system can translate the information and determine monthly costs.
When professionals evaluated the connections between the CDRs, mediation systems, and billing systems, they noticed multiple forms of revenue leakage. Some of the billing systems dropped or were suspended from the records, creating profit losses. After assessing their findings, engineers developed a revenue leakage solution using AI.
The Leakage-Prevention Solution
Developers began searching for a proactive solution to revenue leakage. They planned to create a system that identifies problems in real-time so professionals can respond efficiently. The technology could also identify the source of profit losses instead of keeping employees guessing.
Using the power of machine learning, engineers developed a tracking system that identifies common anomalies in the data collection and billing process. The technology collects information from the CDR, mediation, and billing systems and identifies common revenue loss patterns. When the AI technology identifies activity outside of the conventional activity range, it may flag the data and notify employees.
The system effectively decreases fraud and improves the accuracy of billing. Adopting the technology also increases a telecommunication company’s profitability. The technology can additionally identify a customer’s likelihood of committing fraud, protecting a provider from revenue loss.
Machine Learning for Direct Carrier Billing
When individuals travel outside of the country or make long-distance phone calls, telecommunication providers may add additional charges to their monthly bills. Making third-party purchases through your phone adds a complicated layer to the traditional billing process. Direct carrier billing requires further research, creating a layer of revenue assurance for seamless charges.
Direct carrier billing can increase a provider’s profitability, and it requires AI-powered protection. Machine learning systems can closely track the stages of data usage, additional charges, and billing to reduce revenue leakage. Researchers are still working on developing the technology to effectively track direct carrier billing and identify red flags in the collection and billing process.
The Benefits of Applying AI to Telecommunication
Telecommunication companies can increase their profitability and customer satisfaction ratings by adopting AI technology. They may use the systems to maximize efficiency, decreasing the time employees spend tracking billing processes. Additionally, the technology increases the accuracy of billing by distributing precise data to consumers.
Consumers also benefit from the AI systems. Telecommunication providers have an extremely low overcharge rate when using accuracy-enhancing technologies. Overall, the AI systems have minimal risk alongside considerable profit advantages.