Telecom fraud is still a lucrative business for fraudsters - with $29 Billion lost by CSPs worldwide in 2017. And while overall CSP fraud has decreased by 23% since 2015 due to advances in improved fraud controls, 5G and new digital platforms are providing a perfect storm for fraudsters to make a comeback - but not necessarily in the forms that CSPs are accustomed to. In this blog, we outline why Machine Learning is critical in the fight against the next generation of telecom fraud.
Machine Learning has the potential to save CSPs millions of dollars
According to Gartner, new implementations of Machine Learning (ML) with CSP fraud management will reduce fraud losses by 10% by 2022. While traditional rule-based fraud systems still have a role to play in preventing abuse, such as spotting and stopping the types of fraud they are designed to stop, but Machine Learning takes fraud management to the next level. In fraud management, where the value of ML stands out is on the more complex fraud schemes, which are increasingly being designed to mimic human behaviors and fly under the radar of rule-based systems. International revenue share fraud (IRSF), along with interconnect bypass and voice/wholesale fraud, all share this characteristic and are prime use cases where ML can offer value by tracking anomalies more quickly.
IoT and 5G will create a fertile breeding ground for fraud
The IoT is a treasure trove for cybercriminals, providing billions of vulnerable devices, a huge attack surface, no regulation and vast quantities of personal data. It’s not only the devices that CSPs need to guard against, IoT’s growing ecosystem of partners, platforms and services bringing added complexity that will increase the opportunity for bad actors to find new ways of breaking the system. For example, IoT devices will be equipped with eSIMs, which will welcome new opportunities for traditional types of telecom fraud to make a resurgence, such as subscription fraud, IRSF and traffic pumping. In addition to this, the growing use of digital channels are leaving CSPs vulnerable to new types of online fraud such as synthetic identity fraud and account takeover.
Machine learning capabilities can now be used to identify the ‘outliers’ or abnormal behavior that could signal new fraud and security schemes that we have never seen before. This is vitally important as we move into an era where the risk can go beyond dollars and cents, to even impacting the health and safety of customers who are using new services like connected cars, e-health or security.
Fraudsters are already using automation
There is no doubt that fraudsters are leveraging automation and technology to commit scams, such as Wangiri and subscription fraud, faster and more effectively. However, ML is bringing greater sophistication to fraudsters by helping them to detect whether a carrier has a fraud management system in place – and then target those who don’t.
This underpins the notion that to fight these efforts, we can no longer depend solely on human judgment. Machine Learning and Artificial Intelligence (AI) capabilities are required to help fraud managers synthesize the right decisions at the right time by combining data with context, and repeatedly adding the latest new data to the accumulated history. Machine Learning and automated contextual analysis expedites how to respond to suspicious behavior and provides helpful background information, exactly when it is needed. When fraudsters access your network, the challenge is to single them out of the crowd, especially when they seek to trick your controls by replicating the behavior of ordinary customers.
Not only are advanced analytics, AI, Machine Learning and automation required to process and manage the sheer volume, velocity and variety of data that a 5G network will generate, this technology will also improve the ability for CSPs to spot known fraud threats - and new ones as well. Fraudsters already have AI and automation in their arsenal of weapons, giving them the ability to organize their actions over a huge scale - and then essentially train a computer to keep repeating and optimizing the process as it searches for new opportunities to commit fraud. Like it or not, when it comes to telecom fraud, CSPs are in an automation arms race. It’s time to fight fire with fire.
This article is an abridged version of ‘Why Machine Learning is critical in the fight against the next generation of telecom fraud’, which can be read in full in Connect World APAC.