Revenue assurance solutions for the telecom industry ensure that billing and contracts accurately reflect the commercial agreement between the customer and the service provider. Improving data processes and data quality that power revenue assurance solutions can improve margin, profits and cash flow beyond fluctuations in demand and reduce risk in revenue.
As the telecom industry steadily undergoes digital transformation, it is seeing immense potential in Artificial Intelligence (AI) and Machine Learning (ML) technologies for revenue assurance and fraud management.
Need of Revenue Assurance in Telecom
Fraud is one of the major factors which leads to loss of revenue today in the telecom sector. Telecom companies bleed profits to fraud and revenue leakage, which could arise due to human and/or computer errors. Revenue leakage happens for several reasons such as:
● Introduction of new billing cycle by telecom
● Incorrect compilation of usage details
● Value added service partner payments
● Roaming reconciliation issues
● Poor coordination between teams and network elements
● Error prone manual processes for billing verification and audit
With several thousands of contracts to check, the range of data involved is enormous. When data reconciliation operations are done manually or through disconnected systems for such large amounts of data, it is natural for revenue leakage instances to go unnoticed or happen after a time lapse.
AI and ML-based Revenue Assurance
Artificial Intelligence applications in the telecom industry use advanced algorithms to look for patterns within the data. Considering the vast amounts of data that gets generated, it would be almost impossible to sift through all the data manually free of errors. The use of sophisticated tools and algorithms are necessary to harness the potential of the large amounts of the data generated. AI and ML in telecom have emerged as preferred tools that can cope with such large amounts of complex data in order to provide better insights.
AI and ML can reduce manual intervention and speed up data reconciliation against downstream billing data to provide collection assurance.ML models are able to browse both historic and simulated data and put forth their conclusions. This enables the organizations to detect and predict network anomalies. For all internal purposes and any conflicts with customers, revenue assurance departments receive a single source of evidence.
A large number of telecom companies have already embarked on their digital transformation journey in order to prepare for 5G services. Revenue assurance and fraud prevention solutions are also seeing rapid transformation.
The key business impact of AI and ML in telecom include:
1. Keeping Pace
Central to the growing focus on revenue assurance is the fact that even though networks, partnerships and customer base has grown, billing and revenue collection processes have not scaled to keep pace. This needs to be remedied.
AI’s real-time processing reverses the industry’s 24-hour latency for detection of leakages and revenue leakage exposure time. This helps to keep a tight rein on the revenue since leakages and frauds are detected even before the bills are generated. This prevents the billing anomalies from passing on to the customer.
2. Analytics-driven Business Insights
AI in the telecom industry offers business insights into many fields of the business such as marketing, finance, sales, networks and so on. Specific insights can help the organization to enhance product performance, network-based margin, partner risk analytics and so on.
3. ML-driven Root Cause Analysis
The latest ML techniques can help automate root cause analysis for issuing service assurance alarms. Locating the source of the problem to analyze historical and current alarms will help telcos be better prepared for future issues that may arise.
4. Revenue Reporting
Revenue reporting is a critical area of Communication Service Provider (CSP) business. Telecom operators face several challenges such as computing revenue earned and unearned, compliance and recording of financial data. Identifying leakages within an order in all scenarios- cash processes (any leakage of revenue when the customer places an order), voucher generation, starter kits, handset bundles, pre-provisioning leading to miscalculation, inaccuracy in collection of customer data, overstatement/understatement - are all possible with ML.
5. Improved Customer and Partner Experience
The experience of fraud and/or being overcharged has a huge impact on customers and the service provider. Modernizing business assurance technology will help to reduce the risk to customers as well as the service provider. This paves the way for better performance and experience.
6. Greater Flexibility and Control
The flexibility to adopt business assurance control framework to provide higher protection for every new service is possible with AI and ML. There is also a significant reduction in the cost of business assurance controls by allowing reuse of controls.
7. Ability to Detect Unknown Issues
Developments in supervised and unsupervised ML algorithms include behavior analysis, natural language processing and analysis of business flaws in business assurance - all of which were previously impossible, can be done with great ease today using ML.
Telecom fraud and revenue inconsistencies cost the communication industry $29 billion per year according to the Communications Fraud Control Association (CFCA). Fraud detection systems are overworked considering that telecoms are establishing complex partnerships and venturing into content distribution. AI has the potential to revolutionize telecom fraud management and business assurance. AI and ML help not only in detecting fraudulent activities and bad debt risks but also in forecasting churn and leakage.
Looking to upgrade your fraud management using AI and ML? Get in touch with our experts to maintain your competitive edge through revenue assurance.