Machine Learning Doesn’t Just Respond, It Evolves
Neural Technologies is a pioneering leader in the use of machine learning for revenue protection, credit risk scoring, bad debt reduction and customer engagement solutions. While other companies are still talking about the potential of these technologies, we’re expanding on over three decades of experience unlocking their benefits for customers.
Data is the key driving force for successful machine learning, which is why Neural Technologies’ ability to deliver effective solutions has been further strengthened in recent years with the expansion of our product portfolio in areas of enterprise data integration and transformative Optimus Platform solution.
As trailblazers in the industry, we recognize that understanding the power of machine learning and where it is, and perhaps more importantly, when it is not applicable, is critical for operators looking to embrace this potential to enhance their enterprise operations.
The Cambridge Dictionary defines machine learning as “the process of computers changing the way they carry out tasks by learning from new data, without a human being needing to give instructions in the form of a program.”
That idea of naturally evolving to better perform tasks is critical to how Neural Technologies’ revenue protection, credit management and customer engagement solutions empower our customers.
The power of these advanced solutions offers a pathway to reduce revenue leakage, increase customer revenue, reduce bad debt risk, optimize business operations, and ensure a more effective and competitive business in our modern operating landscape.
Evolution powers opportunity
Early concepts of neural networks and machine learning date back to the 1940s, with major breakthroughs in an area known as ‘back propagation’ going on to further unlock the potential of these opportunities in the 1980s.
The field of machine learning continued to evolve, with the sophistication of these technologies advancing significantly in recent decades including Deep Learning and Generative Adversarial Networks. A true enabler has come in the form of significant improvements to the availability of data, and the rapid growth in computing power such as GPUs ready to power these solutions.
“Only in recent years has the availability of both computing power and readily available data enabled machine learning to really grow into common business usage… A growing awareness under the umbrella of machine learning operations (MLOps) and in-production monitoring is enabling organisations to deliver long-term business solutions,” argues Dr. George Bolt, Head of Analytics at Neural Technologies.
We can see the power of these solutions in action at Neural Technologies. Our White Paper series offers insight into the remarkable range of use cases that machine learning can be deployed in.
These impacts aren’t simply theoretical. We have over 30 years’ experience delivering on the benefits of machine learning for customers in the telecommunications space and beyond. That includes implementation in areas as diverse as mineral exploration, manufacturing monitoring to finance.
Our machine learning expertise provides the basis for solutions to tackle fraud such as premium rate services (PRS), international traffic, roaming, non-payment, subscription fraud, scam call mitigation and a wide range of other advanced solutions for key business needs. Adaptation and evolution is at the heart of those implementations.
Adapting to deliver
That ability to learn and adapt means an effective machine learning solution doesn’t simply offer a static answer to a business problem — it adapts to respond to changing circumstances.
“By learning, it means that computers through exposure to data can generate a solution to a problem. This data-driven approach is in contrast to algorithmic software programming where a human engineer has to design and code step-by-step how the solution is achieved,” highlights Dr. George Bolt.
That’s particularly important in an area like revenue protection where threats are constantly evolving. Fraud prevention and revenue protection are two key areas for our machine learning portfolio, providing an automated solution that adapts to meet changing fraud risks in a high-volume landscape.
Dr. George Bolt also makes clear the importance of understanding the right kind of learning to evolve. “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.”
It’s vital that machine learning techniques support detailed reasons analysis, and providing an explanation of decisions and understanding of information extracted from data is equally important to appropriate use. This is an area where extensive experience across many business domains has allowed Neural Technologies’ machine learning engineers and data scientists to provide solutions which can be leveraged by a wide range of businesses and roles, and not simply technical experts with deep domain expertise.
Neural Technologies’ machine learning solutions don’t just respond to expected events, they learn and adapt to ensure a fit-for-purpose solution that evolves with business needs. That fundamental learning process reduces revenue risks, optimizes operational efficiency, and provides a confident pathway to better business operations.