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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.

Check out Subex’s Fraud Management System to learn more.

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The metaverse will feel like a hybrid of today’s online social experiences, sometimes expanded into three dimensions or projected into the physical world. It will let you share immersive experiences with other people even when you can’t be together—and do things together you couldn’t do in the physical world.

The above is how Facebook, now Meta, defines the metaverse.

In simpler words, the Internet has evolved over the years, growing in sophistication, connections, and technologies. Its upcoming evolution could spawn a world where users can drive deeper relationships, meshed within the economic implications. This world is what we understand as the ‘metaverse.’

The actual term ‘metaverse’ was coined by Neal Stephenson in his science fiction book Snow Crash, published in 1992, to reference a 3D world where humans could interact using avatars. Facebook is one company building a metaverse, as are other giants such as Decentraland and The Sandbox.

While still an ephemeral concept, the metaverse is talked about excitedly in some circles with its potential to leverage 5G, VR, AR, and AI in a much more profound manner. Some reports estimate that the metaverse market will surpass US $1.5 trillion by 2030. This is apart from the US $100 billion market opportunity of metaverse-friendly gadgets and wearables. Many are making investments to understand this space and prepare for the possible opportunities it unlocks. The use cases for gamers are already exploding, and this will most likely be the first frontier for the metaverse.

What can one do in the metaverse?

Borne from the evolved applications of AR and VR, the metaverse represents a kind of converged virtual 3D world that integrates aspects of the physical world and other digital interactions. Users have avatars that can execute actions – real and imagined.

Since the pandemic, much of user experiences and lives have shifted online, giving impetus to the concept of a metaverse. Here are some possible things you can do in a metaverse:

• Create compelling 3D spaces to showcase new products, allowing customers to experience them through AR/VR devices. Nike has already invested in Nikeland, its virtual shopfront that sells digital sports merchandise and sports fashion for avatars of users in the metaverse.
• Build interesting avatars of users based on their different personas and metaverse interactions.
• Plan trade shows, concerts, and art festivals where businesses and customers can interact and even execute deals. For instance, Warner Music Group is partnering with The Sandbox to develop a hybrid musical theme park and concert hall to organize immersive music experiences hosted by top celebrities like Ed Sheeran, Bruno Mars, Cardi B, and more.
• Create next-gen remote workplaces where employees can punch in, attend meetings and training, and collaborate and converse with other meta-employees. For example, a Danish architecture company has designed a virtual office on the Decentraland metaverse for employees at Vice Media Group. Employees can work out of this new ‘headquarters’, meet meta-mentors, and collaborate with their colleagues across the globe.
• Develop meta-enabled schools that align with recognized syllabi and curricula whereby students and teachers can extend the concept of online learning beyond eLearning apps by providing immersive remote education. A Florida-based school called The Optima Classical Academy is building a metaverse school where over 1000 students can choose their courses, attend live VR-based tutorial sessions using issued Oculus headsets, and experience subjects like world history and astronomy like never before.
• Buy/rent homes, stores, and property, build a community of neighbors, and transact and work in the metaverse. For example, a crypto investor bought virtual estate worth US $2.4 million in cryptocurrency in Decentraland, the most expensive purchase recorded by the metaverse platform.

What makes the metaverse unique is that, unlike most virtual reality worlds that are static, the metaverse keeps changing. Each interaction creates data that can be leveraged by businesses and organizations to evolve the metaverse and fine-tune each experience. The applications of AR and VR in the metaverse are unlimited, and many organizations are exploring how they can create new revenue streams or enhance customer experiences through the metaverse. Considering that Facebook already has 2.8 million users globally, imagine the depth of opportunities possible if all of these users could engage in the metaverse and interact in real-time, virtually.

The role of 5G in the metaverse

Building these complicated spaces in the metaverse will require unprecedented download and upload speeds and robust infrastructure, most of which may center on telecom networks. We can expect to see AR/VR applications maturing to include the sense of touch, smell, sound, and more. 5G is the only network technology that can support the massive digital files needed to create such an experience and the speeds at which information and transactions flow through the metaverse.

Thus, as metaverse-enabled technologies evolve, mobile operators have a critical role to play through their investments in 5G, multi-access edge computing, and network slicing. Many are already investing in high-bandwidth and low-latency networks for countless real-time use cases across gaming, surveillance, defense, etc. Looking ahead, they ought to consider building viable business models that allow them to go beyond merely provisioning infrastructure to provide holistic products that can be consumed by metaverse users, such as fraud management and digital identity solutions (for businesses) and wearables and digital avatars (for users).

A prelude to the risks in the metaverse

However, risks lurk behind the shadows. Termed as a potential “scammer’s paradise,” the metaverse is nascent and heavily unregulated. As a result, fraud, piracy, crime, and governance issues loom large. For instance, cryptocurrency may very well be the most popular form of currency for users to transact in the metaverse, despite cryptocurrency crime reached an all-time high of US $14 billion in 2021. Thus, securing the metaverse needs to be a top priority for telecom providers if they are to build customer trust.

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We live in a digital world where everything is available with a few clicks. Whether you want to buy something online, make payments, or log into a popular online service, an OTP (One Time Password) requested via SMS is all you need to complete the transaction. But this process is being replaced in a flash! A huge disruption is being witnessed where flash call verification is swiftly becoming the preferred choice for businesses and service providers as an alternative two-factor authentication process (2FA).

Juniper Research defines flash calling as ‘an authentication process that leverages mobile voice networks to authenticate users or actions.’

Essentially it allows authentication of a user by the last few digits of their phone number. Sometimes flash calling may require some kind of user interaction, such as adding a password/passcode onto a platform.

Flash calling on the rise

Flash calls are fast gaining popularity as it is considered cost-effective compared to SMSes, are swift and secure, and helps deliver an overall better customer experience.

According to Juniper Research, the number of flash calls used for authentication will grow from 60 million in 2021 to an estimated 5 billion in 2022. This is slated to grow exponentially at a CAGR of 128% to a staggering 128 billion calls by 2026! With the rising number of smartphone users, this translates into 20 calls per capita and offers a great opportunity for businesses to add a new revenue stream.

The study further reveals that the total number of flash calls by 2026 will be led by North America, followed by the Indian subcontinent, with the rest of Asia Pacific, Africa, and the Middle East as the subsequent larger markets.

How it will impact CSPs

Reports suggest that authentication-based messaging is expected to generate $39 billion in revenue for mobile operators, which would account for about 5% of total billed revenue for 2022. However, as brands look to migrate their authentication traffic to voice, flash calls have the potential to substantially disturb SMS revenue, resulting in significant losses for CSPs.

Another foreseeable challenge is the increased competition from Over the Top (OTT) messaging apps. Operators have traditionally been slow to implement new services, making them less agile. On the other hand, OTT players are quick to identify market trends and pivot to cater to the changing demands of consumers. Various OTT players, such as WhatsApp and Imo are already looking to integrate flash calling within their apps, giving them a headstart to grab a large portion of the flash calling pie. All this means that there is a greater need for the telcos to monetize flash calls.

At the same time, flash calling by OTT players is not without challenges. There have been reports of users losing access to their accounts after they shared six-digit verification received on the platform with someone claiming to be from the company.

Monitoring and monetizing flash calls

As of now, most service providers don’t use the technology required to effectively identify and monitor flash calling traffic and, thus, are not in a position to monetize it. This problem is likely to become intense as the volume of flash calling authentication calls is likely to touch 130 billion by 2026, growing from just 60 million in 2021, according to Juniper Research.

Juniper Research believes that mirroring the business models of the established A2P SMS market, which relies almost exclusively on being charged on a per-traffic basis, will be key in increasing adoption amongst brands and enterprises and helping CSPs monetize from the flash calling disruption.

The telcos then must implement solutions that allow them to detect and validate this traffic to grow their revenue. Additionally, CSPs need to adopt cutting-edge technologies to create an environment that can fully monetize flash calls. It is crucial they leverage new-age solutions that can help them regain their power, as well as protect A2P revenue streams.  The need of the hour is that CSPs look for solutions such as fraud management systems, which monitor signaling traffic in real-time and also encompass complex ML algorithms and pattern mining tools to detect any suspicious behavior in the traffic and monetize it effectively.

One stop solution to address all types of telecom frauds across Voice, Data and Digital Services

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McKinsey estimates that the cloud will generate over a trillion dollars in value by 2030 and one of the top 10 industries set to benefit from this is telecommunications.

If you wonder where McKinsey’s “trillion-dollar prize” comes in, it is from trends like rejuvenation within IT systems that promise cost optimization and digitalization, as well as trends like innovation that help transform products, services, and the whole enterprise. A top hyperscaler estimates that only 3-4% of enterprise IT workloads currently reside on the cloud, highlighting the hidden opportunity of the cloud.

Getting to know the cloud

In IT parlance, ‘cloud’ often refers to the public cloud, the playground of titans like Amazon Web Services, Microsoft Azure, and Google Cloud Platform. In the public cloud model, telecom operators host their systems and migrate data to the public cloud infrastructure. But public clouds do report a higher incidence of security breaches and attacks; a challenge addressed through private cloud. Another novel option is the hybrid cloud, a combination of public and private clouds. These can store sensitive data and run critical workloads at maximum security while offering the scalability and cost advantages of the public cloud.

The good news is that the cloud is evolving into newer models that can be tailored to different telecom needs. As per one of the reports by Analsys Mason mentions that 5G and open networks will create “multiple cloud domains such as mobile core cloud, vRAN cloud, and network and enterprise edge clouds.” Edge cloud is where the public cloud stack runs on-premises at the Edge data center (as in the case of AWS Wavelength) without having to shift all the data to a centralized cloud server.

Cloud for telecom fraud management

Fraud management solutions include data ingestion, detection, monitoring, analyzing data, and protecting from future events. First-hand, we see transaction volumes rise from 100 million to 100+ billion per day over the past ten years. From call detailed records to internal systems, data sources are growing, creating many new ingestion mechanisms. This ‘source data explosion’ puts a heavy demand on systems to capture hot data and uncover patterns. Neither is the rule-based processes of yesteryears agile enough to handle big data.

Here are four ways by which the cloud can help telecom operators get the best from their fraud management solutions:

1. Telecom frauds are varied and fluid, morphing with technology. Sometimes, CSPs need modern fraud management tools that require next-gen resources. For instance, subscription fraud may require deep learning algorithms and cognitive neural nets, which cannot be run on CPUs but need GPUs. Similarly, detecting International Revenue Share Fraud (IRSF) could be more effective with classifier-based algorithms like decision-trees. Cloud’s on-demand provisioning helps fraud management teams quickly scale resources to detect and thwart attacks during critical events or to retrain models for accurate prediction.
2. Cloud helps CSPs achieve sizeable Capex savings by changing how their fraud systems operate through scalable computing based on traffic, launches, etc. For instance, telecom transactions tend to happen during business hours or weekends, peaking fraud compute requirements during these hours. Traditionally, CSPs would design their systems to handle such peak loads, leading to idle resources for the remainder of the time. Cloud eliminates the need for costly investment through its pay-as-you-go models.
3. Tracking ROI is challenging because of the changing nature of fraud, which mandates constant upgrades and agility. SaaS fraud models cut Capex-heavy hardware and licensing costs and provide clear ROI views. Cloud also offers other IT benefits like migrating from monolithic architecture to granular microservices-based architecture that can be deployed on containers. It gives CSPs the freedom to rapidly deploy on-demand requirements and get instances up and running quickly at the click of a button.
4. Adopters of cloud often remark how iterative, rapid, and agile innovation becomes. Cloud-native practices support continuous delivery, taking innovation out of the lab and into the real world through fast and frequent feedback loops. It facilitates DevOps to shorten system development lifecycles with high software quality. It also accelerates the delivery upgrades and latest functionalities to fraud management systems, allowing CSPs to improve monitoring.

Challenges to prepare for when using the cloud for fraud management

Securing infrastructure is a concern across all cloud environments. Some data hygiene practices to focus on are using advanced data encryption and data management tools, conducting periodic vulnerability assessments, and enabling continuous monitoring of events like security violations. The attractive hybrid cloud model supports cloud-native practices like microservices, Kubernetes, object stores, and containerization. Interoperability is key to ensuring that the cloud delivers expected value.

On another note, the ‘cost of cloud’ could very well be a double-edged sword. Indeed, on-demand provisioning allows better cost control. But without proper monitoring (like retiring instances at the end of a project), enterprises will continue to pay for unused resources. This is also true when managing the infrastructure, as there may be some cloud-native versions that are more cost-effective.

Finally, telecom fraud management systems deal with a lot of PII data, making compliance complex. When leveraging the cloud, fraud, and IT, teams must adhere to local data security laws, enforce robust protections standards, and be careful with access privileges.

Key takeaways/strategies

New technologies rarely have a one-size-fits-all model. Here are some recommendations to guide your fraud management transformation on the cloud:

• Experiment, fail fast, and iterate – Rather than a big-bang approach, try an iterative one that clarifies the end goals but also has a mindset of testing often, failing fast, and iterating quickly.
• Emphasize local governance – Security standards are constantly changing. Keep abreast of these to drive new efficiencies and cost-effective compliance.
• Prioritize security and strategy – Define access privileges cautiously to minimize vulnerability. There should be no compromise on data security.
• Follow cloud-native best practices – This promotes operational agility and maximizes ROI from cloud investments.

One stop solution to address all types of telecom frauds across Voice, Data and Digital Services

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The CFCA’s biennial survey is quintessential for every communication service provider as this report captures the latest fraud trends and the evolving threats.

The 2021 CFCA Fraud Loss report is interesting on many counts

1. It shows the dynamic nature of fraud and how escalating fraud losses continue to plague the telecom industry.
2. It introduces IoT and 5G as entry points to fraud.
3. It also touches on a critical telecom security topic that isn’t discussed much – signaling traffic and signaling security.

What’s common between the pandemic and 5G?

Covid-19 scams have been flooding telecom networks, defrauding unsuspecting, vulnerable, and fearful citizens of precious dollars under false promises of treatment, cures, vaccines, stimulus packages, and more.  

International revenue sharing fraud remains the topmost fraud method in 2019 and 2021, costing operators a whopping US $6.69 billion (1) 

This trend is likely to continue with the advent of 5G. 

The common link – both are changing the fraud landscape and driving up fraud losses.

Now add low-cost IoT devices into this potpourri!

To meet the growing demands for having connected devices and address specific communication-based needs and yet offer a very economical, low-cost ownership approach means that these devices are feature-focused with very little to no security built on them. The rate of adoption of such devices has been accelerated with the connectivity advantage provided by 5G.

The biggest elephant in the room to speak are fraudsters, who have got busier than usual. In 2021, there has been a 28% increase in fraud, amounting to US $11.6 billion in fraud losses (1).

To compound it, fraud methods have also changed. There has been a shift from subscription fraud, payment fraud, and PBX hacking, which were trending in 2019, to caller ID spoofing, Wangiri, and SMS phishing (1, 2) in 2021.

How to complement existing fraud management systems and address the emerging fraud risks?

Back in the days when the scope of telephony was limited to public switched telephone networks (PSTNs), the most common signaling architecture was Signaling System 7 (SS7). It was built on the architecture of trust, which in the current world of schemes is prone to breaches.

With a plethora of tools and techniques freely available over the internet, SS7 networks are susceptible to attacks. Lack of security controls, protocol vulnerabilities, lack of awareness, and lack of monitoring tools has exacerbated this issue.

These days, IP networks are in vogue as they facilitate an alternate, economical solution to SS7. Several protocols facilitate Voice over IP (VoIP) such as TCP, MGCP, SCCP, and H.323 (4). Of these, Session Initiation Protocol, or SIP, is the most common. (Read this article to learn more about SIP and its security challenges.). The SIP protocol is vulnerable to attacks as well.

The majority of the telecom operators are utilizing a reactive approach to process call detailed records (CDRs) for identifying SS7 and SIP-related hacks and technical frauds such as Wangiri, Ip-PBX hack, CLI Spoofing, Robocalls among others.

The investigation is limited to post-event analytics that kicks in only after the fraud has occurred. Despite this, CDR-based fraud management systems are prevalent in 88% of telecoms. 70% still use rules-based reporting to detect fraud and 38% have no real-time threat detection capabilities at all (1)!

Rule-based anti-fraud systems work on the principle of limits. Sophisticated Fraudsters at times tend to fly below the radar or use other methods (like mimicking human behavior) to avoid detection. This way, successfully infiltrating the network and continue defrauding the network for an extended period until detected.

What becomes clear now is that new types of fraud and attack predictability aren’t covered optimally in traditional fraud management systems. Compounding this is the overall lack of automation and limited AI/ML capabilities to predict unknown unknowns. Currently, 13% of CSPs have integrated AI/ML into their FMS, and this number needs to increase to effectively combat fraud. Moreover, automation is limited, and 30% of telecoms still use manual processes for fraud management (1).

Let’s examine why this is an efficiency problem. The highest percentage of telecoms, 20%, update their existing fraud control only whenever needed. The situation is bleaker when instituting new controls: 35% report that they do so only on a need basis (1). This kind of ad-hoc fraud coverage leaves much room for error, leaving CSPs, their networks, and their revenues exposed.

Real-time signaling security: A practical fix

The way forward is to look at a signaling solution that monitors packet flow within the Signaling protocol stack (SS7, SIP, etc.), starting at the network layer and going all the way up to the application layer (referenced using the OSI model*, although for IoT we understand the network stack looks slightly different) to utilize metadata and payload (if required) for detecting fraud and security breaches.

Monitoring specific signatures and patterns within network packets will help proactively identify the technical frauds (Wangiri, CLI Spoofing, RoboCalls, IP-PBX hacking, among others) faster.

By tracking the attack origination and correlating these with high-risk behavior, telecoms can sniff out threats faster and with more certainty. These steps secure the network from those looking to exploit vulnerabilities in IP layers.

(Read this article for five use cases of real-time signaling security.)

Protect telco revenue with real-time signaling security

The proactive signaling security complements the traditional fraud management system and comes with many benefits. It minimizes fraud run-time, improves fraud detection accuracy, and enhances the user experience. It also protects CSP revenue, which can be repurposed for other investments such as improving service and experience quality, upgrading the networks, and confidently taking up new projects.

References

1. CFCA Fraud Loss Survey Report 2021
2. CFCA Fraud Loss Survey Report 2019
3. https://www.techtarget.com/searchnetworking/definition/Signaling-System-7#:~:text=Signaling%20System%207%20(SS7)%20is,network%20are%20called%20signaling%20points.
4. https://www.unitedworldtelecom.com/the-definitive-guide-to-voip-protocols-standards-and-services/#:~:text=The%20most%20common%20VoIP%20protocols,like%20MGCP%2C%20SCCP%2C%20etc

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