Enterprises are operating with an increasingly complex data landscape, crowded with ever-changing applications, data sources, data types, and data volumes, creating a huge data and technology burden for business operations.
In the era of 5G and IoT proliferation, the complexity of this landscape is further compounded, particularly in the advanced applications such as digital payments and e-wallet services. The complexity coupled with the rapid influx of customer-generated data, presents a formidable challenge for revenue assurance and fraud management strategies.
Fundamentally, revenue assurance and fraud management hinge on effective oversight, emphasizing the importance of collecting, connecting, and analyzing data at the core of operational efficiency. Machine learning (ML) and artificial intelligence (AI) offer a powerful solution to this conundrum, creating connected and automated or semi-automated solutions that are effective within a complex and data-rich operating ecosystem.
Other advancements like deep learning algorithms further enhance these capabilities, enabling more sophisticated fraud detection, anomaly identification, and data analysis for revenue assurance and fraud management.
The Power of Machine Learning in Revenue Assurance and Fraud Management (RAFM)
Neural Technologies has been delivering pioneering machine learning solutions for over 30 years. Since our first machine learning model was implemented in the 1990s, we’ve seen the remarkable transformation of machine learning models into the field it is today.
Our extensive work across the globe has demonstrated the significant impact these technologies have on enhancing revenue assurance and fraud management, particularly within the telecommunications sector. Machine learning models for telecommunication operators is a perfect example of challenge and opportunity in action.
The modern telecommunication industry, driven by advancements in 5G technology, is constantly generating data - calls made, time of call, length of call, SMS messages, frequency of messaging, geolocation, customer tariffs, billing resolution, application data and more. This consumer-generated data is crucial for various applications such as IoT connectivity, digital payments, and e-wallet services, enhancing the efficiency and convenience of telecommunications operations.
The convergence of this data into a huge lake of information that presents a valuable asset for professionals in revenue assurance and fraud risk management, but also imposes a significant burden and demands an almost impossible commitment of resources for operations reliant solely on manual processing. In that case, machine learning and AI solutions can capture that data in systems which provide safe, secure and automated risk management that supports human specialists to optimize their revenue and fraud management operations.
AI/ML solutions must focus on how they can support core service delivery and improve overall user experience. Predictive analytics that can inform strategic planning, automated alarm clearance through continuous analysis of data, and preventative alerts based on system behavior are all examples of how AI-driven revenue assurance and fraud management solutions can help improve operational decisions in a landscape swamped with growing data volumes.
The sheer scale of data necessary for effective revenue protection processes today is immense. It’s estimated that 328.77 million terabytes of data are created each day, with a significant share of that created through the billions of smartphones and telecommunications devices across the world. Our machine learning models convert massive datasets into actionable insights, offering a dynamic and automated response to emerging fraud risks. Unlike traditional rules-based systems that struggle with high data volumes due to their inflexibility and slow adaptation, our machine learning solutions feature unique self-learning capabilities that quickly identify and adjust to new patterns of fraud, ensuring that decision-making is both timely and accurate.
Integrating Automated and Predictive AI Solutions
It’s not just the sheer volume of data that causes challenges, it’s also the variability and velocity of data which telecom operators need to face. In the 6Vs of big data, we explore how veracity, value, variety, volume, velocity, variability, and each represents pivotal facets of data use.
Incorporating predictive AI and machine learning models into revenue assurance and fraud management harnesses big data for valuable insights, ensuring a broad spectrum of data types and sources can be seamlessly integrated for timely and effective solutions. There’s no point in risk analysis discovering a potentially ruinous revenue leakage or fraud vulnerability if it does so six months after the event has occurred because of a huge backlog of data to review.
Machine learning and artificial intelligence solutions from Neural Technologies provide powerful, automated and semi-automated solutions that can provide review of structured and unstructured data regardless of the data source. The capability ensures that within a data-saturated revenue assurance framework, systems are in place for efficient and informed risk decision-making.
Our Revenue Assurance solution operates with unlimited data source integration and data quality automation, allowing users to configure integration of any data source, any format for assurance analysis, whether near-real-time or batch mode, thus guaranteeing the accuracy of analysis through automated data quality checks.
Moreover, the predictive capabilities of our AI and ML technologies lay a strong foundation for our Fraud Management solution (FMS). Our advanced FMS is designed to be source-agnostic, accommodating a vast array of data types and sources. It not only adapts to but also learns from expanding datasets, providing a dynamic response to both ‘known’ and ‘unknown’ fraud risks and evolving patterns. This approach not only anticipates but actively prevents fraud, ensuring telecom operators are always one step ahead in safeguarding their revenues.
Realizing the Value of Your Data with Machine Learning
Machine learning in telecommunication ecosystems isn’t just about overcoming a challenge, it’s also about realizing the truly monumental value of that data for risk assurance and fraud management.
Neural Technologies’ solutions portfolio bridges the gap between data generated, and actions informed, overcoming the huge issues of the crowded data landscape, and allowing enterprises to incorporate customer-generated data alongside a broader enterprise data ecosystem.
Using advanced machine learning technologies and data analytics, Optimus Revenue Assurance can support accurate charging, billing, and accounting of all revenue generating events from both customers and partners. This sophisticated machine learning solution incorporates all standard processes for revenue assurance in order to mitigate revenue leakage and quickly identify and alert businesses the root cause of any such vulnerabilities.
Powerful root cause analysis enables our ML solutions to identify the causes of previous revenue leakage events, informing areas of focus, and allowing us to suggest workflows that can solve key challenges. At the top-down level, this solution also allows ML to help confirm the accuracy of revenue reports, informing better executive decisions.
Our approach incorporates a hybrid AI/ML design, employing both classic declarative and non-declarative approaches with a unique deep learning functionality. Open-source API architecture means it can integrate for data mediation across a wide range of applications. It is powered by multiple analysis engines that offer classification, prediction, clustering, anomaly detection and more.
Fundamentally this is about informing and supporting revenue protection decision makers, which is why our solutions incorporate an intuitive and flexible dashboard that allows users to manage and audit cases from system generated alerts and reports, or through ad-hoc investigations using a multi-dimensional explorer.
Combining this revenue assurance with our advanced Fraud Management solution will create a robust revenue protection safety net, benefiting from a highly customizable solution that can analyze and identify fraud events in real-time or near-real-time.
What effective machine learning models like our own are focused on is realizing the value of data, by ensuring it’s captured, analyzed, and trusted. Our scalable, flexible machine learning solution is able to adapt and customize to your unique business circumstances, offering a genuinely future-proof solution to the needs of a high-volume revenue assurance and fraud risk landscape.
When considering what the data landscape means for your revenue protection systems, you no longer need to be caught up on the challenges of your data ecosystem. With the right ML/AI solution, you could be powering up to a trusted and optimized revenue assurance and fraud risk management model that underpins your business success.
