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.
When combined with the ever-accelerating pace of customer-generated data, this creates a significant and often confusing challenge for revenue assurance and fraud management.
Revenue assurance and fraud management are both fundamentally a question of good oversight. That means collecting, connecting, and analyzing data is at the heart of an effective system. 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.
The power of machine learning
Neural Technologies has been delivering pioneering machine learning solutions for over 30 years. Since our first machine learning module was implemented in the 1990s, we’ve seen the remarkable transformation of machine learning into the field it is today.
Our work with customers around the world shows the power of these technologies to deliver results for revenue assurance and fraud management in customers in industries such as telecommunications. That includes major communication service providers (CSPs) like Turkcell, where adoption of our advanced fraud prevention and revenue assurance solutions enabled the operator to increase fraud detection by 12%, reduce time between analysis and intervention by 27%, and improve collection amounts by 89%.
Machine learning for telecommunication operators is a perfect example of challenge and opportunity in action. The modern telecommunication industry 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 data combines into a huge lake of information that creates rich opportunities to inform the actions of revenue assurance and fraud risk management professionals, but also a huge burden and almost impossible resource commitment for fully-manual operations.
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 trends 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 revenue assurance and fraud management solutions can help improve operational decisions in a landscape swamped with growing data volumes.
Simply put, the volume of data available, and necessary, for appropriate revenue protection processes is now huge. It’s estimated that, on average, every person on Earth is creating 1.7 MB of data per second, with a significant share of that created through the billions of smartphones and telecommunications devices across the world. Machine learning provides a platform to transform that data into actionable insight.
The unique self-learning functionality of our own machine learning solutions offers a powerful, automated response to emerging fraud risks. Traditional rules-based systems face challenges in this high-volume environment, working on rigid structures that leave significant gaps for poor or ill-fitting decisions, and unable to quickly adapt to increasing data volumes.
“As part of the learning process, the machine learning algorithms identify potential data that is biasing outcomes away from what might more realistically be expected. This allows re-review to be performed and outcomes fed back into the learning process. Automation of learning is a task requiring extensive skill due to the vagaries of data and risk of bias,” highlights Dr. George Bolt.
Integrating automated data 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 all represent pivotal facets of data use.
What this essentially means for revenue assurance and fraud management is leveraging big data in a way that enables it to provide valuable insight, but also that a wide range of data types and sources can be integrated into an effective solution with timely resolutions.
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. That means in a revenue assurance landscape awash with data, you can employ systems which ensure a smooth flow of effective risk decision making.
Neural Technologies’ own 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 either near-real-time or batch, and ensure accuracy of analysis through automated verification of data quality.
This source-agnostic capability also offers an important foundation of our Fraud Management product, allowing the system to integrate with a wide range of data types and origins, and learn as that data grows, providing an adaptive solution to identify both ‘known’ and the ‘unknown unknown’ fraud risks and evolving fraud patterns.
Realizing the value of your data with machine learning
Machine learning in telecom 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.
We know from our own experience that a machine learning approach offers a powerful solution to these revenue leakage challenges. Risk and Assurance Group’s 2021 survey shows fully 30% of CSP respondents do not currently use machine learning, but target to do so in future, with 12% having recently adopted the technology. Just 14% of respondents had already adopted mature use of machine learning, putting them at the cutting-edge of revenue assurance and business assurance processes.
Using advanced machine learning technologies and 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- or near-real-time.
What effective machine learning solutions 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.
Want to connect to a smarter future for your revenue protection processes? Explore Neural Technologies’ machine learning solutions today.
