Subex Blog

Telecom is buzzing with new trends that promise to disrupt its current role as a network provider. To illustrate, 5G will turn out US $13.2 trillion in global economic value by 2035, brought on by enhanced mobile broadband, massive machine-type communication, and ultra-reliable low latency networks. The metaverse is emerging as an alternate immersive digital world that merges with the physical one through augmented, virtual, and mixed reality experiences. It could spell new opportunities for telcos to collaborate with hyper-scalers for seamless service delivery. Meanwhile, developers excitedly look to using 5G’s low latency features to enable edge computing, spawning numerous use cases for telcos around autonomous transport, virtual gaming, predictive asset maintenance, and smart grids. The time is now for telcos to start preparing for a future where their roles shift from infrastructure and network providers to strategic ecosystem players that offer vital services and solutions. Where opportunity lies, so does risk The emergence of new technologies also throws open the CSPs’ scope of work into an infinite expanse ranging from monitoring network security and tearing down emergent cyber threats to evaluating new products, services, partners, etc. In the new world, risk professionals may have to refocus their sights on:

• Understanding implicit patterns within data to profile risk so as to protect company investments. High variability in data makes it difficult to arrive at the right decisions. Risk professionals must consider variability metrics such as deviation, ranges, and variances when dealing with multi-dimensional data and seasonality trends.
• Processing a wide variety of data, such as streaming data, data lakes, and protocol data, that are saved in unstructured and semi-structured formats.
• Checking data veracity, such as where it is collected from and the reliability of source datasets, means spending effort to check if data is useable for risk prediction models.
• Dealing with velocity or high-speed data to automate decisions within milliseconds, such as denylisting a blocked call or alerting on fake sales and campaigns.
• Exploding data volumes that need sophisticated tools to handle signaling data, behavioral profiling, partner tracking, transaction monitoring, and more
• Visualizing data using modern techniques so they can unlock value and glean useful insights quickly is a challenge considering the dynamic way data behaves.

Three must-have parameters in AI-led fraud management system Considering the multi-faceted data that risk professionals must handle, they need sophisticated tools to deal with different risk types, including fraud, AML, data theft, and security breaches. Unfortunately, traditional techniques falter at handling the fluid behavior of today’s data, and this is where machine learning plays a key role. ML models can mine insights, offer interpretability of results, and provide recommendations. For instance, in some cases, users may very well find that the power of AI is best delivered when accessible at the edge in what is known as edge AI. ML also powers use cases of predictive analytics, network security, and more. From a fraud perspective, it can boost the ability of risk professionals to detect fake caller profiles, illegal access, and incidents of different fraud types, including IRSF, CLI spoofing, etc., through behavioral monitoring of global CSP networks. When evaluating the business case of investing in an AI-first approach to fraud management systems (FMS), telecom operators should adopt a long-term view of what AI delivers. Here are three aspects that AI-led Fraud Management System must cater to and three ways it reinforces your existing fraud management strategy:

• Self-serve – Risk professionals must make faster decisions to keep pace with the business. For this, they need agile and reskilled teams. AI-based fraud management systems give users agility. It leverages APIs to integrate with any source dataset and feed information into risk decision engines. When risk scores are hosted through APIs, it remains available for other teams to access as a microservice, heightening the reusability of AI in a self-serve manner. Moreover, AI frees resources from repetitive tasks through automation pipelines that work with predictable outputs. It also supports various analytics types, such as statistical, behavioral, predictive, and protocol analysis. Adopting AI itself drives a culture of re-skilling as risk management teams must improve their data literacy and create citizen data scientists who are proficient enough to work with AI models, generate output, and translate the results into business outcomes for faster decision-making on new products and service launches.
• Explainability and recommendations – AI-powered decision-making is crucial for reliable and effective fraud management. By providing the explainability of AI logic, AI-led FMS enables model accuracy, fairness, and transparency. It helps users trust model outcomes and leverage its recommendations with confidence. Moreover, an intuitive user interface with data workflows and a pipeline for risk identification, investigation, and actioning will further enrich the user experience.
• Technology – Cloud-native architecture enables scalability, security, and interoperability. New features, model enhancements, and upgrades can be easily deployed to keep the system resilient and relevant. Additionally, building the tech stack as a containerized model can support the niche requirements of CSPs at lower TCO.

A holistic risk assessment program outfitted with the right tools and processes is key to survival in this disruptive age. Subex’s AI-First fraud management, powered by HyperSense, leverages AI in every step of the fraud management process to enhance accuracy, coverage, and time-to-market and also to enable using AI in a sustained manner. It has over 350 fraud use cases supporting risk professionals in delivering next-gen digital services.