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  • Gartner Recognizes HyperSense for AI and Data Science

    Gartner listed Subex as a representative vendor for multi-persona Data Science and Machine Learning (DSML) platforms in its recently published ‘Market Guide for Multipersona Data Science and Machine Learning Platforms’ report.

    The representative vendors were evaluated on technical parameters such as: data access, exploration, visualization, model development, and advanced analytics. Other aspects such as user interface modalities, collaboration, infrastructure, performance, and scalability were also considered.

    About the report

    The Market Guide is the latest report published by Gartner in the area of Data Science and Machine platforms, replacing the Magic Quadrant for the same category. The report highlights the rising relevance of data science and machine learning due to data democratization and looks into the rising prominence of AI and data science as organizations start executing their AI strategies. It provides an in-depth view of the DSML market, key recommendations for data and analytics leaders, and touches upon the questions addressed by Data Science and Machine Learning platforms. Key takeaways are:

    • DSML platforms offer comprehensive analytics and business intelligence coverage through descriptive, prescriptive, and predictive insights.
    • These are evolving into a multi-disciplinary approach by enabling meaningful collaboration between advanced data scientists, citizen data scientists, business leaders, and enterprise teams.
    • Strong governance is needed, considering the prominent role Data Science and Machine Learning will play in automated decision-making.

    The Significance of DSML platforms

    The rate at which data is generated requires high computing and intelligent processing power to make sense of information at a speed that can deliver value to businesses. Right now, organizations use several siloed applications to peer into different datasets (that seem most relevant to the specific function) and get insights. However, the power of data lies in its gestalt, and this is why enterprises need a centralized and powerful platform that ingests diverse, unstructured data in an automated manner. Furthermore, as technology investments in 5G, IoT, AR/VR, etc., continue to grow, organizations turn to AI models to handle exploding data volumes. However, moving from data democratization to AI orchestration is a task typically done by advanced data scientists, who are in short supply.

    Yet, AI and data science are in high demand. Gartner predicts that the AI and data science market will exceed US $10 billion by 2025. Early adopters of AI are already running pilot programs while those still in the planning phases want simpler implementation methods. Thus, the onus falls on Data Science and Machine Learning platforms to drive this growth.

    Data Science and Machine Learning platforms, in their no-code automation way, allow business users with good digital understanding to double up as citizen data scientists and start using AI/ML models for business needs. AI-driven decision analytics coupled with strong orchestration makes AI accessible and scalable across business units and organizational levels. In a nutshell, Data Science and Machine Learning platforms help organizations keen on implementing AI to create a useable talent pool, demonstrate early wins, and scale and federate AI-led initiatives.

    The underlying lever of Data Science and Machine Learning platforms is that they augment user support through data democratization. What sets such platforms apart is their ability to deliver and scale enterprise AI through well-governed, risk-proofed, and responsible AI/ML models powered by data science. They empower organizations by:

    • Providing access to many user groups that may be skilled with digital technology and can now create models that use data science, analytics, and intelligence.
    • Automating AI pipelines in a user-friendly and no-code way for improved productivity and efficiency.
    • Accelerating time to value through pre-built use cases and models that are performant, scalable, and secure, thereby increasing adoption.

    How to make better decisions with AI through HyperSense

    HyperSense AI is a cloud-native and SaaS-based platform that democratizes and orchestrates AI across the entire data value chain. Through HyperSense AI, business users can easily unify data from disparate sources, automate tedious and complex data science processes, and convert data into insights through auto visualization. These insights can be translated across organizational hierarchies so leaders can make the best decisions for their teams based on real-time, reliable data.

    With a host of pre-built use cases, HyperSense AI is composable and extremely reusable. The platform has in-built AutoML to automate many data science workflows and MLOps to foster impactful collaboration between enterprise teams.

    The Gartner feature comes on the heels of Subex being named a representative vendor in Gartner’s Market Guide on AI in CSP Customer and Business Operations through HyperSense AI.

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  • The surge of Flash Calls: How can CSPs safeguard their revenues

    The surge of Flash Calls: How can CSPs safeguard their revenues

    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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  • Assuring Customer experience in an Omnichannel World

    Assuring Customer experience in an Omnichannel World

    As per the Grand View Research report, the opportunity for Customer experience management (CEM) solutions looks extremely promising and competitive, with the market size valued at USD 7,540.6 million in 2020 and an expected CAGR of 17.5% from 2021-2028.

    Shifting Customer experience towards an Integrated Customer-centric strategy

     With Digitalization, customer experience management is shifting towards an integrated customer centric strategy that demands Omnichannel data from multiple channels like Store, Kiosks, Social, Mobile, Web, Field service, Contact center, Direct sales etc. With digital transformation and rising competition, Communication service providers (CSPs) need to adopt the way of Omnichannel customer engagement, to lead on customer satisfaction, opportunity gain, cost optimisation etc.

    Omni-channel strategies for businesses deliver several benefits such as self-serve agility, rapid dispute resolution, cross-selling, personalized experiences, and more. This helps to build cross channel integration and improve customer journeys to achieve overall business growth.

    Need of Digital Customer Journey Analytics adoption

    Communication service providers often focus on improving network speed and quality, they also aim to increase agility via self-service, converged billing, and consolidated resource management systems. However, they are not sufficiently agile to deploy flexible, automated, personalized, intelligent, real-time customer-centric services. Omnichannel experiences have become table stakes for CSPs. In fact, a Forrester study reveals that telecom companies are conservative about omnichannel investments.

    The other issues which are hampering the overall Omnichannel adoption process are factors like low sponsorship to drive real-time customer engagement, siloed B/OSS, and limited cloud enablement for CSPs. From a solution perspective, omnichannel needs high compute and storage for data analytics, data transition, and customer and partner intelligence, which raise investment challenges.

    CSPs need digital customer journey analytics adoption for the CSPs to bring optimal experience in terms of customer expectations. In the absence of customer journey analytics adoption, the process of bringing positive business outcomes remains incomplete and does not deliver the expected customer experience. In that case, it might lead to some prodigious Opex or Capex bottlenecks and process inefficiency, causing less profitable growth.

    Why CSPs struggle with end-to-end Omnichannel CX and role of Business Assurance:

    With 5G bringing multiple complexities in terms of bundling, huge network investment, and dynamic technology change the Omnichannel engagement to improve Customer experience becomes key for CSPs. It requires effective personalization across channels, chatbots as front-line tools, and operate social channels as central that will bring privacy, security, and trust at every touchpoint. With such personalization, if the CSPs are utilizing an Omnichannel system, it has the potential to cause leakages in several areas such as:

    1. Customer experience

    Analysing customer complaints and contacts to ensure process integrity is a key area to ensure a long-term customer journey. Today, the customer has hyperdynamic nature of interaction with a high expectation of getting their needs qualified on a dynamic basis but achieving the accurate real-time result with multiple segregated data storage is a challenge for CSPs. Operators lack to identify such mass customer impact. Ex- Customer reaching out multiple channels with no access to information increases the person cost.

    2. Product profitability

    CSPs should get a detailed P&L accounting view for all cost components based on their usage and derived cut-off price point. But it becomes tricky for them to identify some products with high sales revenue, negative or very low margin with a low holistic view and average analysis tools which causes high leakage. Availability of data and designed analytics approach is crucial to bring visibility on the net profits.

    3. Ecosystem

    Customers need a proper end-to-end delivery of Digital Services, as the value-chain involves a complete ecosystem of different partners, networks, products, etc. With the increasing line of controls such as third-party quality, OTT, IoT services, non-telco platform, service quality towards platforms for M2M, partners, monitoring & SLA, etc., it becomes complex for CSPs to ensure one mode interaction with digitization.

    4. Provisioning/ Activation

    Vulnerability rises if the customer orders are not recorded accurately and fully, as it becomes critical to monitor, capture and investigate all provisioning errors within agreed timeframes. Failure to keep the customer or business partner reference data synchronized across all systems affects the customer journey. Also, the lack of charging or overcharging non-used services relative to customer demands/requests affects customer behaviour and business.

    AI capabilities for Omnichannel enablement

    The Ecosystem Omnichannel enablement’s could be effectively built with AI and analytics-based models and implemented in the right direction with an approach of Business Assurance. This can be achieved by following two main aspects to this. First is to formulate the right approach that enables digital, measures value, and integrates outcomes. Second is to design a roadmap based on key themes that ensure a successful omnichannel CX program.

    How business Assurance plays significant role as business enabler for digital Ecosystem?

    As the enterprises are growing customer-centric and striving hard to stay close to the customers, technology convergence and system integration becomes extremely important to engage, record, and intelligently analyze data from various sources. Business Assurance plays a significant role as a business enabler in this evolving digital ecosystem. For the Omnichannel ecosystem, seamless integration and cohesive interface requirements, it can be effectively used as a means of a single source of truth that empowers organizations to transform into data-driven analytical organizations. It also enables to achieve optimum customer behavior in terms of complete, accurate, and on-time delivery of the expected business outcomes.

    Business Assurance can enable digital customer journey analytics adoption for the CSPs and bring optimal experience in terms of customer expectations such as efficient response time, easy interaction, educational guidance, collaborative study, and built trustful decisions. The Ecosystem Omnichannel enablement’s could be effectively built with AI and analytics-based models and implemented in the right direction with an approach of Business Assurance.

    Transforming the Omnichannel Customer Experience for Modern Telcos with Business Assurance.

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  • Keeping up with the Cloud: Telecom Fraud Management

    Keeping up with the Cloud: Telecom Fraud Management

    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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  • Top 3 challenges to AI adoption and how the barriers are overcome

    Over the past few years, AI has made its way to every boardroom discussion. Be it giants like Google, Netflix, or Amazon or small and medium businesses; everyone has benefitted from AI. While many companies have rolled out successful proofs-of-concept and have even been successful in deploying AI in production, there are still some challenges in adopting AI.

    Accenture’s report reveals that 75% of executives believe they risk going out of business in 5 years if they don’t scale AI. Some organizations have even operationalized their AI and machine learning strategies, with projects proliferating with best practices and pipelines. Today, companies at the leading edge of the AI maturity curve are using AI at scale. While they are making efforts to deploy and scale AI, awareness about the challenges of this journey is also necessary.

    Top 3 challenges to AI adoption

    Challenge #1: Data quality and quantity

    Technologies such as Artificial Intelligence (AI) and Machine Learning (ML) have the potential to help businesses make better use of the massive volumes of data. Still, these techniques depend on the computing power, quality, and quantity of the data provided. Getting consistent and accurate quality and quantity of the data is a challenge, since, there are no commonly accepted and widely adopted standards of data definitions and governance in enterprises.

    Many enterprises are pursuing a range of AI initiatives and modernizing data infrastructure. But current data practices are an issue, as several companies haven’t attained a high level of sophistication with crucial data-related aspects. In fact, many organizations have stopped mid-way when pursuing AI initiatives because the data is not good enough; hence predictions and insights would also be unreliable. Therefore, many companies tend to postpone their AI journey in favor of a data journey before starting the AI leg.

     Solution:

    In order to overcome this challenge, a robust data management strategy, data quality, and governance framework should be in place to ensure that all the data generated in the organization is captured, processed, and stored effectively. Also, the right blend of cloud and traditional data warehouse setup will help organizations achieve optimal performance. There should also be a focus on a forward-looking approach, i.e., on future integrating and scaling of data. It ensures that integrating data from various new sources is not a challenge later.

    Challenge #2: Hiring the talents with AI skills

    One of the major challenges while AI adoption is finding or hiring the right team with AI skills to work with. The right talent is the key to success for any initiative, and the same is true with AI as well. As per a Juniper research survey, 41% of respondents are worried about the training of current employees to operate the AI systems. Also, 32% concentrate on recruiting the already trained talents to cope with it. AI is a far more complex skill to build, and therefore there is certainly a demand and supply gap in the marketplace.

    AI comprises a range of technologies that covers advanced analytics with the ability to predict outcomes, Conversational AI, Natural Language Processing (NLP), Robotic Process Automation (RPA), Deep Learning, etc. The sheer vastness of the technology makes it difficult to find the right talent for both the creation and implementation of an end-to-end AI journey across an organization. Also, AI takes time to evolve and requires constant creative and material investment till it starts maturing and providing a level of acceptable accuracy. Therefore, we need someone with creative brains who can also innovate the use-cases for the technology.

    Solution:

    To address this challenge, an organization needs to build a culture where business teams can think about the use of AI in day-to-day operations. Once this culture is built, the organizations have more champions beyond the innovation group to motivate the rest of the people in the organization to walk the same path.

    Companies will have to invest in the right talent, train internal resources with the right aptitude and the know-how in related technologies, add people to the creative team, and think unconventionally while solving business problems. Also, low-code/no-code AI technologies empower technical and non-technical programmers to become citizen data scientists and build AI applications with little to no coding knowledge.

    Challenge #3: Eliminating Bias and AI Governance

    AI governance means monitoring and evaluating ROI, risk, bias, and effectiveness algorithms. However, while hugely interested in AI adoption, companies are reluctant to build their AI governance strategy. Also, bias in any AI model impairs the possibility of making the right decisions. After seeing positive gains using biased models, businesses might get a false assurance, but the models with biases are not solving the problem they are supposed to solve.

    Solution:

    This usually happens due to the use of datasets that tend to be discriminative against arbitrary groups. So, the datasets used for training, especially evaluating the models, should be balanced so that they don’t reflect real-world biases. Unknown biases still sneak into the systems. To overcome this challenge, strong Quality Assurance (QA) processes are important to have in place. These QA processes should be extended post-integration, too, since data drift and feedback loops after deployment can still bring biases to the system. Also, Explainable AI and Ethical AI capabilities ensure transparency and interpretability in the models with the fair usage of AI. With these capabilities, it helps eliminate biases in the model.

    Getting consistent and accurate data, filling the AI skills gaps, and AI governance might be challenging. So, get ready for a long game, try pilots of your projects before the final run, and set the metrics to measure the progress of AI adoption over time. Simultaneously, it would help businesses monitor and evaluate algorithms that impact the business daily. This is an opportune time to climb this mountain towards AI-powered decisions, step-by-step, with balanced, courageous actions.

    Enhance your customer experience with our AI Orchestration Platform

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  • From Monitoring CDRs to Signaling Traffic: Why CSPs Must Make the Shift

    From Monitoring CDRs to Signaling Traffic: Why CSPs Must Make the Shift

    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

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

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  • How is Augmented Analytics Transforming Business Intelligence?

    How is Augmented Analytics Transforming Business Intelligence?

    Data is the ultimate means of making excellent decisions, and why not? It helps determine the problems and challenges as they come our way, highlights opportunities, and helps see transformations, so we can work towards the desired goals.

    As data becomes more convoluted with every passing moment, managing it and extracting valuable insights using traditional BI systems is not efficient. Addressing this challenge is Augmented Analytics.

    In this blog, we will see how Augmented Analytics is transforming business intelligence. Additionally, we will also discuss use cases that benefit technology.

    Augmented Analytics

    Gartner, the technology research and consulting corporation, coined the term Augmented Analytics. Augmented Analytics can be expressed as the next step in analytics’ evolution. The technology enables data scientists and business users to use the technology of today, such as artificial intelligence (AI) and machine learning (ML), to find and visualize information from unstructured data.

    Data scientists can employ augmented analytics to analyze data without bias or previous views about how variables in the data are related. It eliminates the necessity for a specialist in the creation and management of advanced analytics models. It allows data scientists and developers to integrate ML/AI into applications that provide data science and machine learning content. Data scientists with evolved skills get better options to dedicate themselves to innovative creation and construct the most relevant models.

    How Does Augmented Analytics Work?

    While comparable to other forms of BI in its analytical workflow, augmented analytics enhances data analysis using ML, NLG, and AI furthermore. Here’s how:

    Data Preparation

    Data preparation is all the work done on data for query and analysis. It includes the collection, filtering, connection, and validation of datasets. And usually, it demands the expertise of developers and data scientists to conduct.

    However, this process can be automated with augmented analytics tools. Data preparation and streamlining integrations of all your data sources can be run through automation systems — including data warehouses, cloud platforms, web service tools, and analytics platforms.

    Once the data (and metadata) has been added to the pipeline, everything from data filtering to dataset unification is done by the automating systems for you. This opens up time constraints for your data scientists, engineers, and developers to focus on creating new analyses to deepen insights.

    Insight Discovery

    Insight discovery is the part of the data analytics process where the algorithm analyzes the data via the curtains of a predefined model to discover answers to questions, such as quarterly revenue or customer acquisition rates. However, since models traditionally have to be developed by data scientists manually, insights can be blind in the specificity of metrics.

    With augmented analytics, insight discovery is both uncomplicated to initiate and thorough. Queries can be set up using natural language and voice inputs rather than hyper-specific keyword entries. Machine learning algorithms can drill through all of your data (no matter how many rows there are) to uncover detailed, targeted insights to find answers to your questions question.

    How Augmented Analytics is transforming Business Intelligence (BI)?

    Using powerful AI and ML algorithms, Augmented Analytics helps businesses reduce their dependence on manual processes and/or data scientists by automating the insight-generating process. It also reduces overlooks and inconsistencies because of human errors while generating insights. However, it is essential to make decisions in such a way as to provide a clear image of the situation, which is crucial for the system to work as intended. Revolutionizing how consumers engage with data, consuming it, and turning insights into action can all be automated.

    Augmented Analytics is changing key phases of Business Intelligence, which are currently still being conducted manually and are prone to human error, as discussed above.

    The automation offered by augmented analytics has transformed traditional business intelligence (BI) into self-serving business intelligence. While traditional BI used to be an uncommon tool that was majorly managed by the IT team, self-serving BI can be operated by business users who are the end-users in most cases.

    The significant disadvantages of traditional BIs are that it requires highly skilled data analysts and has a lengthy time-to-insight period with poorer quality of data compared to augmented analytics. Modern self-serving BI solutions powered by augmented analytics give us user-friendly graphical interfaces that end-users can understand. These solutions can handle an extensive amount of data from multiple unstructured sources quickly and efficiently. Intelligent BI also makes data security, governance, and access control simpler for the entire organization. They also help reduce the involvement of the IT team to manage business analytics.

    Some of the many advantages of using BI powered by augmented analytics include:

    • Deeper data analysis: Analysis of exhaustive data combinations and efficient discoveries of all the factors influencing your business are now possible.
    • Quicker results: Since there is no manual scanning of data, you get quicker results.
    • Better use of resources: When you automate a significant part of your analytics process, more complex and deeper research can now be addressed by your team.
    • Actionable insights: By streamlining the data analytics process, you get access to key insights that can help you make better data-driven decisions.

    Does your organization plan to adopt Augmented Analytics BI in the future? If yes, then how will it benefit an organization. Feel free to share your thoughts in the comments section.

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  • Telecommunication’s Generational Paradigm Shift – sundown for legacy, dawn of 5G

    Telecommunication’s Generational Paradigm Shift – sundown for legacy, dawn of 5G

    As the Communications Service Providers (CSPs) transition to the latest communications standards, 4G and 5G, the year 2022 will witness the shutdown of 3G networks or 3G Sunset, as it is generally referred to, in several parts of the world.

    Take the case of the Americas, where starting 2022, most CSPs are likely to shut down 2G and 3G entirely by 2025. Verizon plans to shut down 3G by the end of the current year, while Sprint will do so by March 2022 and T-Mobile by July 1 this year.

    On the other hand, the UK’s EE has announced plans to phase out 3G by 2023. In addition, several mobile network operators in Africa have also announced plans to sunset 2G and 3G services. Interestingly, European and Oceania MNOs are focusing on shutting down 3G networks while the regions plan to continue to operate 2G networks.

    Why are MNOs shutting down 3G networks? 

    There are several reasons for the MNOs to close down 3G networks. The latest 4G and 5G communications standards offer better speeds and enable MNOs to provide new and exciting use cases. 5G offers better throughput and extremely low latency, thus promoting innovative use cases like remote surgery, autonomous vehicles, and Industry 4.0, among others. This means that the usage of 4G and 5G is growing in all geographies at the cost of 3G, making 3G or third-generation networks redundant. In the circumstances, it is unviable for the telcos to continue running 3G networks.

    Further, spectrum is an expensive and limited natural resource, so the MNOs are keen to refarm the available spectrum and use it for 5G. This way, the MNOs needn’t spend more on procuring spectrum for 5G, while at the same time, they would be able to provide the latest use cases to subscribers and enterprises. This is especially relevant because 5G requires a spectrum in several frequency bands.

    Typically, MNOs follow the strategy of adding a new technology layer for every new standard. Shutting down 2G and 3G will also help reduce network complexity and enhance efficiency by making it easier to manage the networks.

    What does 3G Sunset mean for the users? 

    The shutdown of 3G and 2G networks is not without challenges. Apart from cellphones, the 3G network is being used in several other devices, including security cameras, medical devices, cars, and home alarms, among others.

    “While mobile operators have articulated clear timelines and provided multiple delays and postponements, the shutdown of 3G networks will inevitably be challenging for a limited number of 3G mobile and IoT users who have lagged in their upgrades to 4G and 5G,” says Jason Leigh, research manager, 5G and Mobile Services at IDC. “But the finality of 3G in 2022 is a natural part of the cellular networking evolution and a necessary development to allow next-generation 5G connectivity to flourish.”

    Just to put this in perspective, There were more than 80 million active 3G devices just in North America in 2019, according to RCR Wireless News. According to ABI Research, 3G Sunset could impact more than 350,000 Class 8 vehicles and many connected cold-chain trailers.

    Several machine-to-machine (M2M) and Internet of Things (IoT) devices continue to utilize 3G services, and the shutdown of the 3G network will have a crippling effect on them. Several of these devices were never upgraded to 4G because a faster network was not required for the use cases. However, with 3G Sunset on the anvil, several security cameras, medical devices, and car and home alarms will stop functioning.

    Legacy devices that continue to use 2G or 3G networks need to upgrade before network shutdown to ensure that they continue to function. This is also important to avoid a stressful and costly rollback changeover.

    In the circumstances, meticulous planning is required to ensure least or no disruption to the users once 3G networks are shut down. The IoT industry is now looking at next-generation LTE technologies, like CAT-1, CAT-0, and CAT-M1 as 2G, 3G and eventually 4G replacements.

    How to plan for the 3G curtains down? 

    The MNOs need to conduct a thorough audit of all the network elements to ensure that all the 3G devices are on the Public Land Mobile Network, so they will continue to function even when the 3G network is shut. This is crucial to ensure that any existing hardware using these technologies will continue to be operational once the services are switched off.

    While transitioning, the MNOs can ensure that the networks are not just ready for the current

    requirements but are also scalable to meet future needs. Flexible, agile and programmable network architectures, like Open RAN, can be deployed to ensure that the networks are future-ready. In addition, since Open RAN is interoperable and uses the principles of virtualization, it offers better network economics while making it easier to deploy future technologies.

    How can Subex help?

    Subex offers a range of solutions to ensure an easy and seamless transition for 3G users/devices without facing any service disruption. Subex’s Network Asset Management solution uses Machine Learning-based analytics to enable MNOs to meet regulatory and auditory requirements while using automation for better Return on Investment (ROI). It provides an end-to-end view of the network assets, thus allowing service providers to efficiently manage events and workflows.

    On the other hand, Subex’s Capacity Management solution allows service providers to better plan for change by leveraging its ML-based algorithms for accurate capacity planning. 

    Get in touch with us today to find out more about how Subex can help you Sunset 3G without causing any service disruption!

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  • Top 12 features of Augmented Analytics platform

    Augmented analytics is the future of data and analytics, which is the next big wave of uproar in the market of data. In the future, it will signify as a commanding driver in the field of analytics, Power BI, data science, ML platforms, and embedded analytics. Data scientists and analytics experts should equip themselves to embrace augmented analytics.

    In this era, a huge amount of data is generated daily from multiple sources. To analyze and process the raw data manually is typically a lengthy process that involves complex steps. The first step would be to understand the data as well as requirements. Once the clarity of the strategies in question has been defined, the next task is to build an algorithm or model and at last, evaluate the model.

    Augmented Analytics makes this entire process easier by automating the process of understanding, analyzing data and generating useful insights. It recognizes patterns and displays clear visualizations and trends. This automation is conducted by using Artificial Intelligence, Machine Learning and Natural Language Processing. It is deduced into three sub-categories, i.e., Augmented Data Preparation, Augmented Data Discovery, and Augmented Data Science and Machine Learning.

    • Augmented Data Preparation – It compiles the data preparation process like imputing missing value, data cataloging, time series feature extraction, etc. employing AI and machine learning techniques to the process.
    • Augmented Data Science and ML – It automates the crucial features of analytic modelling, which helps decrease the involvement of experts that generate, operationalize, and manage AI models.
    • Augmented Data Discovery – Augmented Data Discovery employs machine learning to allow findings and visualization of insights as well as results without manual implementation of models or algorithms.
    What are the benefits of Augmented Analytics?

    The straightforward concept behind augmented intelligence is to support human information, speed-up repetitive tasks, and enable businesses to function faster. Let us list a few advantages of augmented analytics

    • Augmented analytics with AI – Currently, augmented analytics with the inclusion of human intelligence and machine learning and AI-enabled data analytics can help to make excellent decisions.
    • Augmented analytics optimizes productivity – It is tedious to do repetitive and time-consuming tasks that require very little attention. With the implementation of AI, those repetitive tasks can be automated thus increasing human productivity.
    • Augmented intelligence can deliver more value – Rather than working hard to do analytics, it is better to develop such an automated system that can do such tasks as data preparation, implementation of ML algorithms, insights monitoring, etc that helps businesses at all levels.

    Here are a few features to look out for in an Augmented Analytics software:

    Feature #1: Augmented Data Preparation

    This feature leverages Machine Learning (ML) automation to augment data profiling and data quality, recognition, modelling, manipulation, enrichment, metadata development, and cataloguing. It includes abilities like automated matching, joining, profiling, tagging and annotating data before data preparation, sensitive attribute recognition, automating repetitive transformations and integrations, data quality and enrichment recommendation.

    Feature #2: Autogenerated and Analyzed Segments or Clusters

    This feature leverages Machine Learning (ML) to find new segments or clusters in a dataset automatically to better process them and help with performance.

    Feature #3: Autogenerated Forecasts or Predictions

    This feature leverages Machine Learning (ML) to create a forecast or prediction automatically providing essential insights into pre-planned strategies and conduction methods in a business.

    Feature #4: Automated Algorithm Selection and Model Tuning

    This feature leverages Machine Learning (ML) to automate selecting the appropriate algorithm to fit a defined use case. It also automatically adjusts the parameters of code to improve accuracy and optimize the predictive model performance.

    Feature #5: Automated Anomaly Alerting

    This feature leverages Machine Learning (ML) to support automated alerting, notification, or proactive collection of anomalies based on modifications of data or detections of black-listed patterns or activities.

    Feature #6: Automated Descriptive Insights

    This feature leverages Machine Learning (ML) to automatically detect and deliver basic insights such as variances, associations, correlations, or trends from a column or dataset. These metrics are typically displayed as concise natural language depictions or sample visualizations.

    Feature #7: Automated Feature Generation or Selection

    This feature leverages Machine Learning (ML) to automatically determine the best types of data or variables to be assigned as part of the predictive model building process.

    Feature #8: Automated Model Monitoring

    This feature leverages Machine Learning (ML) to automate inspecting the performance of models in use to ensure the relationships are still valid, and that the model is functioning well.

    Feature #9: Automated Model Packaging or Deployment

    This feature leverages Machine Learning (ML) to elevate the ease and speed with which the user can transfer models from a development phase to a production phase or embed them into a business model directly. It automates the process of creating APIs or containers that are used for faster deployment.

    Feature #10: Contextualized or Relevant Insights

    This feature leverages Machine Learning (ML) to automate insight generation that merges explicit and/or implicit usage and user feedback data to display the most relevant data required at specific moments.

    Feature #11: Key Driver Analysis

    This feature leverages Machine Learning (ML) to automatically recognise vital key drivers or attributes of a specific metric in a dataset.

    Feature #12: Voice-based Natural Language Search

    This feature offers a voice-based interface to search through the data using natural-language statements in a dataset.

    Business users and executives get excellent value from augmented analytics because of data refinement and deep analysis of strategies, plans, and more. With intelligent operations from augmented analytics integrated into the system without the need for great technical skills or expertise, a quick study of data has never been easier. Augmented analytics helps business users and executives find precise metrics more easily, make relevant inquiries, and instantly uncover insights in the context of their business. While augmented analytics benefits those without deep analytical expertise, it also boosts performance for data prep tasks and more thorough analysis.

    Do you agree with the points mentioned in the blog? Feel free to share your comments if we’ve missed any important points.

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  • Why enterprises need AI-driven platforms

    Introduction: A New Technology Stack

    Over the last four decades, the information technology market has grown exponentially to more than $2 trillion. During this time, the IT industry has experienced the transition from mainframe computing to minicomputers, to personal computing right at our homes, to internet computing, and handheld computing. The software industry has transitioned from custom applications based on mainframe standards to applications developed on a relational database to enterprise application software, to mobile apps, and now to the AI-enabled enterprise.

    Today it is unimaginable that any major corporation would close its intelligent forum without an enterprise resource planning system. The IT industry is now undergoing another major shift, where its business can’t solely be based on mainframe computers. A new era of 21st-century technologies – including flexible cloud computing, the internet of things, and artificial intelligence – is driving digital execution across enterprise, commerce, and management globally. Online transformation presents several special requirements that create the void for an entirely new software technology deployment. The requirements are numerous.

    This article describes the requirements of the new digital transformation software with Prescriptive Insight and the current approach to Simulation Transparent AI Model Execution – i.e., using Model Performance to build applications by batch Deployment, Real-time Deployment, Model Monitoring, Model Management components and cloud services.

    Finally, to describe how the C3 AI Suite, AI Fairness, AI Trust and AI Ethics with its unique model-driven architecture fully addresses the requirements for the digital movement, providing a low-code/no-code AI and IoT platform that accelerates software expansion and reduces cost and risk, delivering future-proof applications.

    Artificial intelligence is redefining the very importance of intelligent modelling and execution in an enterprise. The rapidly progressing Machine learning capability is on its way to revolutionizing every aspect of an enterprise. The ability to access and process data online has levelled the playing field and brought every enterprise a unique opportunity for progress. Here’s what enterprise AI can do to help Model performance and provide Data insights.

    Purpose of Enterprise AI

    Enterprises across the world are experiencing a shift in the relative adoption of AI. These applications will present individual enterprises as many opportunities as it does challenges when the transfer of data towards the transition to AI hosting is to be accomplished. While access to AI, data monitoring, and Prescriptive Insight is common to all enterprises, what is not common is how each enterprise utilises that knowledge—and on what grounds. It is crucial to understand the levels that will define their individual and collective success in putting AI to good use.

    There are many complexities in each enterprise system that will determine whether an enterprise will be able to quickly use the data and information from its existing talent to develop AI, automate, and deploy to succeed.

    As the conditions of AI deployment accelerates, it is difficult to capture that staying competitive means being more intelligent in day-to-day tasks as well as noteworthy decisions for an enterprise’s survival. It is overlooked how enterprises across nations are expected to face tremendous challenges and changes in the coming years, with automation compelled growth as the only workforce to lead during those changes. As a result, it is essential to understand what AI-driven growth means for enterprises.

    Emerging Trends

    The emerging movements in AI-driven automation mirror momentous shifts of players and actions in the AI ecosystem that reveal the realisation of ideas, interests, influence, and investments in the AI discipline of enterprise adoption and transformation. Enterprises have started to understand the overall effects of the automated statistical learning-driven platforms far beyond narrow artificial intelligence, crossing economic, commerce, education, governance, and trade supply chains. While the relationship between enterprises and automation is complex, and at times weary, the energy and pace of AI-driven automation change along with anticipated challenges and opportunities for its: products, services, processes, operations, and supply chains all come with huge benefits. From what it appears, the AI applications of the future will be composed of hybrid systems with several components and reliant on many different data sets, methodologies, and models.

    The ever-growing cyberspace is connecting humans and machines across the world. It is not only the human users and interface-based applications that are getting connected but the growing number of internet of things (IoT) devices are also getting activated and operational with the rollout of new smart technologies. Individually and collectively, the ever-complex connectivity of man and machines is producing enormous amounts of data and is driving the rapid evolution of AI across enterprises. However, there never seemed to be enough power and speed behind AI for enterprises to implement ideal techniques and strategies. While AI-driven automation emerged many years ago, it is only now evolving as real-time computing, and as massively parallel processing systems advance AI performance even further. As a result, AI brought automation is now moving further as a fundamental trend.

    Many functional parts of enterprises are already benefiting from the AI movement. From R&D tasks, customer assistance, finance, accounting, and IT, there are rapid transformations from experimental to settled AI technology across enterprises. There is no doubt all enterprises will benefit from intelligent decision making to simplified supply chains, customer relations to recruitment techniques.

    As the Enterprise AI market rises, so does the demand for AI-as-a-service. Moreover, AI-driven automation, Transparent AI, and low-code platforms are merging as the competitive landscape. New organizational capabilities are becoming critical, and so is the necessity for effective management of the growing security risks of AI.

    Now, common sense tasks have become more comprehensible for computers to process, AI-driven intelligent applications and robots will become extremely useful in business operations and supply chains. Without a complete understanding of use cases — the problems can be solved using AI, where to apply AI, what data sets to use, how to get credible data and skilled resources — still slows down AI adoption, company culture also plays an important role in AI adoption strategies and has seemingly been a barrier to AI adoption.

    Enterprise Digital Data Infrastructure

    While enterprises are taking advantage of global, regional, local explanation and decision analytics AI brings, web platforms are beginning to employ these technologies and benefits. The AI is shaped by several variables and external factors, many of which are influenced by data choices made at the enterprises. So, how will availability, affordability, accessibility, and security of data impact potential AI growth for enterprises?

    As seen, many enterprises lack the required digital data infrastructure. The lack of digital support, in turn, makes it harder for opportunities and innovations in AI, making it challenging to be equipped to the enterprise needs adequately — leaving the company with outdated data, information, and ecosystem. Moreover, the trustworthiness of the data sets also is an emerging concern. That directs us to these important questions: how are enterprises handling digital data infrastructure challenges? What are the various data classifications that are vital for enterprises?

    While enterprises have already employed AI in analytics, many meaningful data partnerships are emerging. The emerging integrated structured data and text, when available to train AI systems, will bring much-needed progress in enterprise AI. It will be interesting to see how this new data-driven world brings each enterprise across industries, both opportunities, and risks.

    What Next?

    The possibility of Enterprise AI to transform the enterprise ecosystem in many ways. From decision making to supply chain intelligence and tracking capabilities to the automation of business processes, AI can change the entire enterprise ecosystem across spaces.

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