Skip to content
Fraud Detection using AI and Machine Learning - Neural Technologies
Neural Technologies4 min read

Solving Fraud with Machine Learning

Fraud detection with Machine Learning

Global telecom providers lost an estimated USD39.89bil of revenue to fraud in 2021 according to the Communications Fraud Control Association, equivalent to around 2.22% of their total revenue. That marks a 28% increase in fraud losses since 2019 and demonstrates the remarkable risk level in today’s fraud landscape. 

It’s hard to grasp the sheer scale of the global fraud threat. A 2020 survey of over 5,000 business respondents across the world by PwC revealed companies were experiencing an average of six major fraud events in a 24-month period, incorporating both internal and external fraud threats. A staggering 47% of companies experienced fraud in the 24 months prior to the survey.

Addressing this threat is complex and demanding for businesses, but the sheer scale and cost of fraud makes clear the imperative to do so. In this hectic world of fraud risk management, machine learning offers a powerful tool to tackle the risk of fraud rapidly and robustly.

Managing fraud risk

Human analysts have been the frontline against fraud for centuries, but the modern landscape requires sophisticated new solutions to respond to the sheer volume and complexity of fraud threats. Machine learning is a technology with huge promise in this landscape. 

This opportunity is particularly powerful in data-driven telecommunications businesses. These enterprises, by their nature, operate within data-rich ecosystems with huge volumes of data which can inform fraud detection decisions. This is the perfect arena for machine learning which derives greater value the more data it is provided. 

Any technology solution needs to be able to respond rapidly to emerging and evolving fraud risks. There’s no point having a static defense line that’s incapable of adjusting to new fraud techniques. Machine learning provides an adaptive solution that can react to changing fraud tactics. 

Where traditional rules-based approaches respond within rigid predetermined frameworks, machine learning such as our own Fraud Risk Management product can learn and adapt over time, providing a flexible and scalable solution that’s able to change to fit the operating landscape. 

It’s clear that a growing number of organizations are turning to these advanced solutions to meet their fraud risk management needs. The global fraud detection and prevention market has seen significant growth in recent years, as a growing number of enterprises recognize the need for more sophisticated fraud risk management solutions. The market is projected to achieve a compound annual growth rate of 18.% in coming years, growing from USD 22.8 billion in 2021 to USD 53.4 billion by 2026.

Accelerating the adoption of responsive solutions

The need to act quickly to react to data has seen greater focus since the COVID-19 pandemic, as enterprises look to automated solutions to boost their business resilience. A further study by PwC found that 52% of companies had accelerated their AI and machine learning adoption plans as a result of the crisis, with 86% now framing these as essential business technologies. 

These sophisticated machine learning solutions provide the speed of response required in a rapidly changing landscape, offering automated detection based on a flexible risk profile. 

This technology also provides the capability to scale rapidly, with implementation of our Fraud Management offering a rapidly deployed solution that enables simple scale-up to meet expanding enterprise demand.

Finally, they provide the flexibility of customized implementation that enables rapid fraud detection while also freeing up your human analysts to focus on more complex cases of fraud. That means the resources available to provide more robust human fraud oversight across the organization. Machine learning also offers flexibility to identify and address emerging fraud threats, drawing attention to new activities of concern to support better fraud detection decision making.

Unsupervised learning techniques can be applied to identify abnormal/unusual types of activity in terms of usage, payments, billing, commissions, activations, and more, providing adaptive fraud detection to protect enterprises and their customers. It can take a holistic view of data from all sources within a data ecosystem, learning typical patterns of activity. From these trained models, ongoing events in your business can be monitored and abnormal events can be better identified. 

Neural Technologies’ machine learning algorithms allow us to exploit such models to also identify what and why such events are abnormal, and this allows identification of which unusual events should be alerted as potential abuse.

According to a study by SP Global, 59% of global enterprises now have machine learning initiatives either in production, or at proof-of-concept stage. The same study shows that reducing risk exposure and lowering costs are in the top 10 most significant benefits expected from deployment of machine learning. 

At Neural Technologies, we have over 30 years’ experience that prove the concept of our own advanced machine learning solution for managing fraud risks, with extensive deployment understanding across industries such as telecommunications, finance, and more. 

The modern fraud landscape is vast, and vastly complex. Sophisticated machine learning solutions are a vital tool in the arsenal of enterprises seeking to mitigate their risk. Those companies who act now stand to build a more robust fraud detection and protection landscape.

Speak to the experts. Contact Neural Technologies to explore our advanced fraud management solutions.