The telecommunications industry loses close to 2% of global revenues every year to fraud. The Communications Fraud Control Association (CFCA) conducts a survey every two years about fraud in the telecommunications industry. Its 2019 report found that two-thirds of respondents were experiencing an increase in fraudulent activity. The report also identified the most prevalent types of fraud which include subscription and payment fraud; PBX and IP-PBX hacking; Wangiri callback schemes; abuse of weaknesses in networks or devices; dealer fraud; subscriber and identify fraud; account takeover; and internal (employee) fraud. To detect fraudulent activities, Bell Canada has partnered with Amdocs to deploy an innovative solution that uses AI and machine learning – through self-learning and auto-tuning capabilities, the model automatically adapts to learn the constantly-changing behavior of fraudsters. Bell Canada wanted to be able to detect changes in the behavior of fraudsters much more quickly and identify customers at risk of committing fraud. Importantly, the company needed to be able to distinguish fraudsters from legitimate customers. Bell Canada is using a solution from Amdocs that is capable of “self-tuning”, which means it uses machine learning and an AI algorithm to automatically retrain the model and adapt it to enable near real-time detection of fraudsters. For example, if the system initially identifies an account as a fraud risk, and it is later determined that they are not, the system can auto-tune the model based on what it has learned. In machine learning and AI language, this is known as a “false positive”. Similarly, the system can learn from a “false negative”.