The telecommunications industry is rapidly growing due to the pervasiveness of smartphones and connected devices such as smart watches. It is estimated that there will be over 3 billion smartphone users by 2021, saturating networks with increasing volumes of traffic.
Unfortunately, network operators stand to lose billions in annual revenue due to fraudulent activity, network congestion, and theft. Artificial intelligence is a promising solution that can potentially alleviate these concerns but challenges persist.
Skymind is a platform that helps product and engineering teams ship AI capabilities into enterprise applications faster.
Empower engineering teams to build AI functionality for Java applications in enterprise environments, using the language and environments that they are familiar with.
Eliminate dependencies and interoperability issues between data science and engineering teams. Get enterprise-grade model reliability, performance, and scaling without maintenance, all on the JVM stack.
Build AI-powered capabilities into existing workflows using streaming or stored data from ERP and CRM systems, data lakes, Spark, Kafka, and elsewhere. Improve model performance over time with quality monitoring, A/B testing, and winner selection.
Engineers can integrate and deploy Python-based models developed by data science research teams into JVM environments and applications, or build their own models in a tracked and collaborative environment.
With the prevalance of connected devices, our lives are more tied to technology than ever before. As a result, tellecommunication networks, which enable these devices to communicate with each other, have accumulated petabytes of sensitive customer data and call detail records. With so much sensitive information on the line, Telcos must leverage cutting edge technologies to stay ahead of fraud, theft, and customer privacy concerns.
At Skymind, we have applied AI to a common type of telco fraud called SIM box fraud which accounts for billions of dollars in lost revenue annually. A fraudster would host boxes of pre-paid SIM cards, allowing users to route calls over VoIP and onto mobile networks, thereby bypassing international fees and taxes that would normally be levied. Beyond the lost revenue, SIM box fraud can also create increased traffic that impacts call quality for legitimate customers.
Although this type of fraud is pervasive, it is often difficult to detect. The red flags that suggest SIM box fraud is taking place are often subtle and appear like regular traffic. Tracking SIM box fraud is also notoriously difficult thanks to the staggering volume of cellular traffic and ever-adapting fraud activities. Machine learning can be a powerful tool and boost existing fraud-detection systems by rapidly finding signs of fraud in massive amounts of data. Since machine learning is an adaptive tool, it is also able to keep up with new fraud tactics being implemented by frausters.
Beyond fraud detection, machine learning offers a wide range of solutions to Telcos. The possibility of 24/7 customer service is available with rapid-learning, interactive chat bots. Predictive maintenance powered by machine learning can help get the most out of equipment and cut down on repair costs. Robotic Process Automation (RPA) can dramatically increase back office productivity and free up workers to focus on more meaningful tasks. Telcos that adopt these and other machine learning tools have an advantage in both security and efficiency.