PwC’s 2022 Global Economic Crime and Fraud Survey which involves 1,296 executives across 53 countries, claimed that about 51 percent of companies globally experienced fraud in the preceding 24 months.
It’s hard to grasp the sheer scale of the global fraud threat. In 2022, the Association of Certified Fraud Examiners (ACFE) released a report stating that the average duration for the detection of a typical fraud case is 12 months, resulting in a median loss of $117,000.
Addressing this threat is complex and demanding for businesses, but the broad scale and cost of fraud makes clear the imperative to do so. In this hectic world of fraud risk management, machine learning fraud detection offers a powerful tool for accurate and real time fraud detection covering both known and emerging risks.
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 in near real time. This is the perfect arena for machine learning models 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.
One of the key advantages of machine learning and AI fraud detection is its ability to operate in real-time. Unlike conventional systems that rely on manual intervention and delayed reporting, machine learning algorithms can continuously monitor transactions, user behavior, and other relevant data points in real time.
Where traditional rules-based approaches respond within rigid predetermined frameworks, machine learning models such as Neural Technologies’ Fraud Management solution can learn and adapt over time, providing a flexible, scalable and real time fraud detection solution that’s able to change to fit the dynamic landscape.
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 models provide the speed of response required in a rapidly changing landscape, offering automated real time fraud 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. Fraud detection machine learning also offers flexibility to identify and address emerging fraud threats, drawing attention to new activities of concern to support better real time fraud detection and decision making.
Unsupervised machine 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.
The projected growth in the machine learning market is nothing short of phenomenal. With a market value of USD 19.20 billion in 2022, the industry is expected to cross the USD 26 billion mark by the end of 2023, marking a significant increase in just one year. Looking further ahead, the market is set to experience an explosive expansion, with a staggering projection of USD 225.91 billion by 2030, according to Fortune Business Insights.
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 AI fraud detection and protection landscape.
Speak to the experts. Contact Neural Technologies to explore our advanced fraud management solutions.