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  • From Leakage Detection to Strategic Insights: The Power of Business Assurance in Telecom

    From Leakage Detection to Strategic Insights: The Power of Business Assurance in Telecom

    Introduction

    In the rapidly evolving telecommunications industry, the traditional practice of revenue assurance is undergoing a significant transformation. With declining average revenue per user (ARPU) and the need for real-time insights, telecommunication service providers are embracing a broader approach known as “Business Assurance.” This blog post explores the changing landscape of assurance practices and highlights the four key areas that business assurance encompasses.

    The State of Assurance ‘As-is’

    For telecommunication service providers, revenue assurance has long been a fundamental practice for profitable business operations. Dating back to the early 2000s during the age of 2G and CDMA, revenue assurance practices focused on validating services against billing. While technology has evolved from 2G to 3G and 4G, the mode of operations has remained relatively unchanged, with an increase in the number of source systems. Telecommunication operators now run hundreds or even thousands of revenue assurance controls, validating data from voice, data, SMS, MMS, subscribers, and content from creation to billing.

    What’s Changing?

    The declining ARPU across various geographical locations has brought about a significant shift in the telecommunications industry. As telecommunication services become cheaper, what was once considered a significant revenue leakage is now a fraction of its original value in today’s market. This downward trend in ARPU has prompted telecommunication service providers to invest in systems that provide quicker insights, such as near real-time reconciliations.

    This changing environment has given rise to the concept of “Business Assurance,” which is gradually gaining significance among major telecommunication revenue assurance practices worldwide. Operators are renaming their revenue assurance functions as “Wholesale Assurance” and “Margin Assurance,” signaling the shift towards a broader scope of assurance.

    Business Assurance – Why Your Practice Needs to Adopt It Now?

    Unlike traditional revenue assurance, which primarily focuses on leakage detection, business assurance expands the scope of revenue assurance practice in four key areas:

    1. Single Source of Truth

    Revenue assurance has mastered the art of collecting, enriching, and validating data from various network sources over the past two decades. This practice has created a single source of accurate, clean, and usable data for various upstream process requirements. For example, instead of starting from scratch, revenue assurance systems can provide clean and accurate data for tasks like product profitability prediction. Today, revenue assurance systems are critical contributors to data lakes, blurring the boundaries between revenue assurance and data management.

    2. Revenue Assurance

    While transitioning to business assurance, it is essential to maintain traditional leakage detection practices to ensure process integrity. However, with the emergence of bundled pricing models and fixed-rate plans, conducting record-by-record reconciliations may not make business sense. For instance, many mobile users rarely review their bills as they consistently stay within the bundled limits. Telecommunication operators must evaluate the cost versus benefit of conducting revenue assurance practices on granular or aggregate data.

    3. Active Risk Intelligence

    Business assurance expands the revenue assurance scope to support and enable revenue enhancements, cost reduction, and seamless service enablement. This evolution includes assurance areas such as margin assurance, collections, revenue accounting, partnership dispute management, and order-to-cash processes. By embracing active risk intelligence, revenue assurance practices can go beyond leakage detection and become holistic contributors to the operator’s profit and loss statement.

    4. Decision Boarding

    While data-driven decision making has become the norm in today’s business landscape, the telecommunication industry has been slow to adopt analytical solutions. Telecommunication operators possess vast amounts of data on customers, products, services, markets, and trends, yet their analytical capabilities remain underutilized. Business assurance emphasizes the availability of clean, accurate, and enriched data in near real-time. By leveraging advanced analytics, telecommunication operators can predict risks and opportunities, enabling measurable actions to

    Conclusion

    The future of revenue assurance lies in embracing business assurance, which expands the traditional revenue assurance practice into new territories. Telecommunication service providers must adapt to the changing landscape, leveraging the power of data and analytics to drive revenue enhancements, cost reductions, and informed decision making. By broadening the scope to include single source of truth, revenue assurance, active risk intelligence, and decision boarding, operators can position themselves for success in the ever-evolving telecommunications industry. The time to embrace change is now, as it enables revenue assurance to regain its stature as a vital component of profitable business operations.

    Business Assurance enabled through Active Risk Intelligence

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  • Maximizing Profitability: The Key Role of Business Assurance in Telcos’ Omnichannel Evolution

    Maximizing Profitability: The Key Role of Business Assurance in Telcos’ Omnichannel Evolution

    Introduction

    Telecom operators are recognizing the importance of omnichannel customer experience as digitalization introduces new avenues for customer interaction. To maintain customer engagement across multiple channels, a customer-centric strategy that shares data and provides a holistic view of customer requests is crucial. This blog explores how telecom operators can transform their ecosystem omnichannel experience with the adoption of Business Assurance, overcoming challenges and optimizing business outcomes.

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

    Business Assurance can facilitate the implementation of digital customer journey analytics for Communication Service Providers (CSPs), ensuring an optimal customer experience in terms of efficient response time, easy interaction, educational guidance, collaborative study, and trustful decision-making. This allows for the collection of data-driven insights and informed decision-making based on customer-facing applications across various platforms, such as e-commerce platforms, knowledge management systems, web content management systems, and billing applications.

    However, without customer journey analytics adoption, the process of achieving positive business outcomes remains incomplete, and the expected customer experience is not delivered. This can result in significant operational expenditure (Opex) or capital expenditure (Capex) bottlenecks and process inefficiencies, leading to less profitable growth.

    To meet the evolving needs of customers, CSPs must provide an omnichannel experience that includes effective personalization across channels, the use of chatbots as front-line tools, and centralizing social channels to ensure privacy, security, and trust at every touchpoint. However, such personalization can lead to leakages in various areas, such as customer experience, product profitability, ecosystem, and provisioning/activation.

    To ensure a long-term customer journey, CSPs must analyze customer complaints and contacts to maintain process integrity. This requires identifying mass customer impact and addressing the dynamic nature of customer interactions. CSPs must also obtain a detailed view of all cost components based on usage and cut-off price points to identify profitable products. The value chain for digital services is complex, involving multiple partners, networks, products, and services, and CSPs must ensure effective interaction and integration across the ecosystem. Accurate recording and monitoring of customer orders are crucial to prevent errors and delays in provisioning/activation, which can impact customer behaviour and business profitability.

    Business Assurance with Ecosystem Omnichannel for CSPs

    Business Assurance with Ecosystem Omnichannel can help protect and contribute to digital growth for CSPs in multiple ways. It enables trust and improves customer experience, provides assurance for Blockchain ecosystems, and ensures a trusted environment for new multi-party business models. The system also helps in early detection of obstacles in the customer journey and provides real-time insights to optimize business outcomes.

    Moreover, the integration of AI and analytics-based models with an approach of Business Assurance can effectively build the Ecosystem Omnichannel enablement. This can be achieved by formulating the right approach, including enabling digital experience layers, measuring CX data, and ensuring integration, and designing a roadmap based on key themes of Business Assurance, such as unification, reusability, personalization, and cloud enablement and DevOps.

    These capabilities feed AI and machine learning models that help CSPs invest in the right technology, make the right decisions, and measure actual value. By implementing an effective Business Assurance approach, CSPs can transform into data-driven analytical organizations, personalize offers, and proactively resolve issues for a seamless and cohesive omnichannel experience, which is critical for success in today’s digital ecosystem.

    Conclusion

    Business Assurance is a comprehensive solution that enables businesses to ensure trust, enhance customer experience, and protect against revenue loss. It can be implemented through an API-based architecture, which enables integration of components across multi-vendor environments. The system can provide real-time insights by monitoring data from various components and leveraging analytical processes to generate actionable insights. Business Assurance can also offer a flexible runtime environment, where new services and products can be rapidly designed and onboarded. With a data-driven analytics catalog, the system can capture relevant information and automatically configure new service chains. Security by design is a critical feature, whereby security concerns are continuously weighed against business opportunities in open architectures. By providing agility and feasibility, Business Assurance can help businesses respond effectively to market needs and ensure autonomy, oversight, and consistency across distributed teams, local products, and centralized processes.

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

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  • Open letter to customers from the new CEO of Subex

    Open letter to customers from the new CEO of Subex

    Pioneering innovation that creates value for all our stakeholders, with an undeterred focus on customer delight is at the core of what Subex does. As its new CEO, I wanted to take this opportunity to introduce myself, and communicate our continued commitment towards our vision of enriching lives through AI-led Digital Trust.

    First and foremost, I want to thank all of you for your loyalty and support towards Subex over the years. We are committed to providing you with the highest quality offerings that meet and exceed your expectations at every step possible.

    Serving as an independent non-executive director of Subex since 2015, I fully understand the company’s business, and its customers’ needs. Today, I am excited to step into the CEO role, with over 2 decades years of experience in the technology industry, and a track record of driving innovation and success across organizations of various sizes and dynamics. I am confident that my expertise in the area of Artificial Intelligence will help us significantly amplify our momentum with HyperSense, and other AI initiatives.

    As I take on this new role, I would like to acknowledge and express my gratitude to the outgoing CEO, Mr. Vinod Kumar. His unwavering commitment and dedication to the company is an inspiration to us all, and we will continue to build upon the strong foundations that underpin Subex. Under his leadership, Subex has experienced tremendous innovation, branching out into newer areas of business, while maintaining market leadership in our core areas, all of which enables us to focus on the next chapter for the company.

    As we embark upon this next phase of growth for Subex, my priority will be to work with the talented Subex team to strengthen our offerings and ensure that we are creating immense value to our customers to become their most preferred partner in the areas of Revenue Maximization, AI, Cybersecurity, and Identity Analytics. I truly believe that Subex has enormous potential to grow and innovate, and I look forward to leading the company into a new era of success.

    We will continue to invest in cutting-edge technologies, collaborate with our partners, and listen to your feedback to ensure that we are delivering the solutions that will help you excel in your respective areas of business. I am excited about the opportunity to work with you, and I look forward to meeting with you in person over the coming months. Please feel free to reach out to me or our team with any questions or concerns.

    Taking over the responsibility of leading one of India’s earliest product companies into its next phase of growth is a matter of great honour, and I pledge my unwavering commitment to do everything possible to bring a great degree of success Subex, and all its stakeholders.

    Thank you for your trust in Subex, and I look forward to your continued support.

    Sincerely,
    Nisha Dutt
    CEO, Subex

  • Combating AIT Fraud with AI: Strategies for Success

    Combating AIT Fraud with AI: Strategies for Success

    Introduction  

    The telecom industry is projected to reach $4.16 trillion, with a Compounded Annual Growth Rate (CAGR) of 6.35% (1), in a significantly mature marketplace. A recent survey of telecom losses puts the figure at a whopping 2.2% of the current market size of $1.8 trillion. An increase of 28% from 2019. Among telecom frauds, AIT fraud has been on the rise since many Mobile Network Operators (MNOs) have not invested sufficiently in preventing Artificially Inflated Traffic (AIT) and Artificially Generated Traffic (AGT). AIT (which includes AGT) results in tremendous losses to both operators, and according to Forbes, this figure has grown 600% in the past 10 years to $10.76 billion (2).

    So, who gets affected? Both user and operator. So, who is accountable? Also, both user and operator. So, how do we prevent it?

    What is AIT fraud? What constitutes AIT fraud, how it works, and what effect it has?   

    Before we look at ways to prevent it, let us first look at what AIT is and the different types and causes of AIT fraud. AIT usually begins with the name itself – Artificially Inflated Traffic. The fraud perpetrators start by triggering avoidable and often unnecessary calls, SMS text messages, One-Time-Passwords (OTP), and other MNO services that either cost the customer or the operator.

    How it works is that when the user’s mobile phone makes calls, sends a message, or triggers the receipt of a message, it triggers the accounting workflows between the user’s MNO and the target MNO. The target MNO in this case, is usually one with high termination fees. Since termination fees are revenue streams for MNOs, the fraud perpetrators can benefit from a revenue-sharing agreement with the target MNO who may be completely unaware of the unscrupulous nature of the traffic generated.

    AIT often escalates into other types of fraud, including hacking, phishing scams, and identity theft, where the victim’s mobile phone can be used to create Artificially Inflated Traffic in the form of calls to high-termination MNOs, but also result in fake numbers, and a multitude of possibilities that perpetrators can exploit with ease. Another significant source of AIT fraud is when the Caller Line Identification (CLI) is spoofed to prevent the receiving MNO from identifying the source of the call, thus taking unfair advantage of the difference in termination rates.

    The losses to operators, compounded with churn, regulatory intervention, significant loss of credibility, etc., have serious repercussions which require immediate action.

    Detection& Prevention of AIT fraud

    Having delved into how AIT fraud is perpetrated, there are ways to detect, reduce, mitigate, and prevent AIT fraud, but these are not simple. Preventing and reducing AIT fraud requires the use of the right set of tools and technologies, along with a consistent and conscious approach from both the operator and the user. Let us look at some of the ways to combat this fraud:

    Analytics and AI: Analyzing traffic, identifying patterns, and automating responses based on historical data are crucial to ensuring a progressively increasing number of AIT frauds are eliminated before it enters the network. Some of these methods include:

    1. Pattern Detection: AI can analyze traffic patterns and detect anomalies in data much faster and more accurately than humans. By using machine learning algorithms, AI can detect traffic patterns that are artificially inflated and alert authorities.
    2. Real-Time Monitoring: AI can monitor traffic in real time and detect any suspicious activities. This can help prevent artificially inflated traffic fraud before it happens.
    3. Analytics: AI can use predictive analytics to detect traffic patterns that may indicate artificially inflated traffic. By analyzing historical data, AI can predict when a website may experience artificially inflated traffic and take action to prevent it.
    4. Behavioral Analysis: AI can analyze user behavior to detect whether the traffic is real or artificially inflated. By analyzing factors such as user location, browsing history, and click patterns, AI can identify whether the traffic is from real users or bots.

    Monitoring: Whether at the operator level or the user, monitoring of calls, messages, and other network activities at the network level and the user’s device is crucial to identify potential AIT fraud instances. From simply blocking typical calls and messages originating from fake numbers to reporting spam, regular anti-malware scans can help users prevent losses to themselves as well as the network operator. Likewise, operators can use network scanning and monitoring tools to identify the AIT and devise approaches to mitigate AIT fraud.

    Vigilance: While the technology component – firewalls, monitoring tools, and AI can significantly improve the efficiency of AIT fraud reduction, the users also have to assume responsibility for the traffic that they experience on their devices. Refraining from installing apps without verifying their authenticity, and avoiding circumventing content restrictions, as these are significantly large sources of AIT in the form of spam calls and text messages, and fraudulent apps, which may, in turn, result in spam, as well as OTP fraud, phishing, and other potential causes for losses both for the user and the operator.

    Conclusion:  

    As much as our technology solutions improve in their ability to detect and prevent AIT fraud, they cannot all be eliminated in one fell swoop. And until it becomes untenable for the perpetrators, it is up to users and operators alike to remain cautious and vigilant and keep updating their understanding of how, why, and what perpetrators of AIT do to achieve their goals.

    References: 

    1. https://www.skyquestt.com/report/telecommunication-market
    2. https://www.telecomreviewasia.com/index.php/news/featured-articles/3124-a2p-sms-fraud-a-rising-threat

    Explore the Use Cases and Strategies for Combating Frauds with Advanced Analytics and Machine Learning

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  • Telcos must do more than just shake off SMS fraud

    Telcos must do more than just shake off SMS fraud

    A telco we recently spoke to narrated how scam SMSes were creating trouble for its subscribers, but its risk team was able to ‘shake it off for now. Interesting choice of words: ‘shake it off, a phrase popularized by Grammy Award Winner, Taylor Swift, who has been busy shaking off other things like a record deal that turned sour last year, driving her to re-record her first 6 albums.

    Unfortunately, unlike the pain of past relationships, which is what the singer croons about in her chart-topping hit, SMS fraud isn’t something that can be easily shaken off by telcos.

    Sending the right message

    A text message from a friend is a person-to-person or P2P message. A message from your bank sharing an OTP is an application-to-person or A2P message. A2P SMS finds use in areas like two-factor authentication, sharing details about bookings, reminders for appointments and travel, updates about deliveries, notifications about discounts, sales, and promotions, etc., making it an indispensable information delivery channel. Since the pandemic, the number of A2P SMSes traveling across telecom networks has shot up. It is estimated that 3.5 trillion A2P messages will be sent in 2023, marking a 40% increase over 5 years. On the other hand, the flourishing of OTT apps providing free messages has put a dent in P2P messaging revenues. Nevertheless, subscribers consider SMS a safe channel of communication. A study on marketing channels reveals that the average open rate of a marketing SMS is 99% compared to 28-33% for marketing emails.

    What goes on behind that SMS

    Convenience is perhaps the best thing about sending a text message. It’s quick and does what it is supposed to. But there is a whole machinery at play to ensure that every ‘send’ button clicked on an SMS creates revenue for telecom operators and many, many intermediaries.

    The SMS ecosystem consists of numerous players supporting the business of P2P and A2P SMS. There are SMS resellers, SMS hubs, Rich Communication Service (RCS) providers, and SMS aggregators, to name a few. The infrastructure also comprises several components like SMS centers as well as software such as SMS gateways and SMS APIs. Each of these plays a role within authorized routes, allowing messages to be delivered from a business through an MNO to the right customer.

    SMS resellers provide software that allows quick broadcasting of business SMSes based on an agenda with pre-built templates. SMS hubs streamline the flow of international SMS through interoperable systems between telecom operators, enabling wider reach at a lower cost without complex agreements. SMS aggregators are niche telcos that act as intermediaries between many MNOs to send and receive SMS connecting brands to their customers. SMS gateway is a website that allows businesses to send bulk SMSes to their customers via telecom networks and supports international SMS. And finally, SMS API, a new addition to the market, is a piece of code that dispatches SMS via an SMS gateway and also supports text message communication between different web applications.

    Understanding ‘The Blank Space’ of fraud 

    Interactions between these nodes and players are complex and governed by numerous and verbose contracts outlining cost, frequency, carriers involved, interruptions, disputes, privacy, and much more. But fraudsters commit much time to find loopholes within networks and agreements that they can exploit, such as weaknesses within SS7 signaling protocols and grey routes. According to CFCA Fraud Loss Survey 2021, SMS fraud accounts for US $3.65 billion in losses.

    Here are some well-known fraud types:

    • SMS Spoofing – The location and identity of the sender are spoofed to mimic a known business
    • SMS Faking – Signaling parameters are manipulated to fake the operator’s details, causing customers to receive unsolicited SMS
    • SMS Spamming – SMS is embedded with a callback premium rate number, incurring high charges
    • SMS Malware – Hackers breach MNO systems to steal sensitive user information
    • SMS Bypass – Traffic is routed through alternate networks and grey routes, leading to a loss of revenue for telco
    • SIM Farms – A collection of SIM cards are used to issue business SMS to avoid paying enterprise SMS rates

    SMS fraud creates much harm. It leads to identity theft, financial theft, and network manipulation. It significantly erodes customer trust in the primary communication channel. For example, nearly 64% of customers worldwide are concerned that mobile messages could be from impersonators trying to steal their data, money, or identity.

    And they are right to be concerned. Today, a majority of SMS fraud remains undetected. It affects operators through the leakage of SMS revenue and negative brand image. Businesses cannot monetize their services as people become distrustful of promotions and sales discounts.

    Re-orienting fraud and security solutions for the new age 

    Telcos need a diverse ecosystem of SMS players to forge international connections between people and businesses. However, they ought to focus on transparency and weeding out misaligned players if they want to secure their SMS ecosystems and sustain revenues from A2P and P2P SMS.

    SMS firewalls are among the most popular approaches, but their efficacy is waning in light of emerging SMS threats due to new signaling protocols. Techniques such as real-time signaling analytics, heuristics, and advanced ML techniques give all parties – businesses, operators, and users – visibility into SMS interactions so operators can identify any SMS fraud. As operators re-orient their security systems to fight SMS fraud, it fosters customer confidence, secures access from businesses to their consumers, and creates a thriving ecosystem for genuine players.

    Perhaps it is time for telcos to move from merely ‘shaking off fraud’ to ‘knowing that it’s trouble’ and adopt a long-term view to combat it. They ought to upgrade their fraud management system to mitigate any form of risk proactively.

    Subex’s AI-first Fraud Management Solution provides a data analytics platform that helps CSPs engineer ML features, leverage a global honeypot network to spot anomalies faster, identify malware attacks in SMS, and ensure real-time SMS threat monitoring.

    To see how our signaling to fraud management solution telcos from SMS fraud

    Connect with a Subex expert now!

  • Explaining a Machine Learning Model using XAI Methods

    Understanding the Factors Behind Airline Passenger Satisfaction through XAI Approaches

    Explaining a Machine Learning Model using XAI Methods

    Introduction

    Explainable Artificial Intelligence (XAI) aims to provide understandable explanations of AI models and their predictions to individuals without a strong background in AI. In recent years, XAI has become a highly sought-after area of research due to the growing demand for transparency in AI systems. The three key principles of XAI are: transparency, which refers to making the inner workings of a model easily accessible; interpretability, which involves the ability to comprehend the model’s decisions; and explainability, which pertains to the provision of clear, human-understandable explanations of the model’s outputs.

    Interpretable machine learning can be achieved through two approaches. One approach involves designing a predictive model that inherently provides interpretable results, such as linear regression or decision trees. The other option involves using a black-box model and applying a post-training explanation method, referred to as agnostic methods. I will outline some of these methods and provide illustrations using a binary classification model as a context.

    In this article, we explore the application of XAI methods to enhance the understanding of a machine learning model designed to predict airline passenger satisfaction. Through the use of XAI techniques, we aim to uncover the key factors that contribute to passenger satisfaction and provide a clear, human-understandable explanation of the model’s predictions.

    Airline Passenger Satisfaction Dataset

    This dataset contains an airline passenger satisfaction survey. Here, the goal is to predict passenger satisfaction. The data was made public by Klein TJ in Kaggle, and the columns are:

    • Gender (male or female).
    • Customer type (loyal or disloyal customer).
    • Age.
    • Type of travel (personal or business travel).
    • Flight class (business, eco, or eco plus).
    • Flight distance.
    • Arrival delay in minutes.
    • Airline satisfaction level (satisfaction, or neutral or dissatisfaction).
    • Satisfaction level among the following services, rated from 0 to 5, 0=not applicable, 5=most satisfaction:
    Inflight wifi service Departure/arrival time Ease online booking Gate location
    Food and drink Online boarding Seat comfort Inflight entertainment
    On-board service Leg room service Baggage handling Check-in service
    Inflight service Cleanliness

     

    Global Model-Agnostic Methods

    In XAI, we refer to global methods to algorithms that give a comprehensive explanation of the entire data set.

    Permutation Feature Importance

    • Permutation feature importance measures the increase in the prediction error–or decrease in model score–after permutating the feature values.
    • The permutation breaks the relationship between feature and target. Increase in prediction error is an indicative of the model dependence on the feature.
    • We obtain the importance of the features with the following expression.

    Explaining a Machine Learning Model using XAI Methods

    ij = importance of feature j
    s = fitted model score on training or validation dataset
    K = number of different permutations
    skj = model score on permutated dataset.

    Example

    We observe, in Figure 1, that after randomly shuffling the features Personal travel and Inflight wifi service there is a decrease in the recall by 0.193 and 0.189. The decrease in the score means that the ML model depends heavily on these features to predict passenger satisfaction.

    Figure 1. Permutation Feature Importance Plot.

    Explaining a Machine Learning Model using XAI Methods
    Figure 1. Permutation Feature Importance Plot.

     

    Partial Dependence Plot

    • The partial dependence plot (PDP) shows the marginal effect of a set of features on the outcome.
    • This helps to discover the nature of the relationship between the features and the target (e.g., linear, non-linear).
    • For regression, the partial dependence function is defined by:

    Explaining a Machine Learning Model using XAI Methods

    where,
    S= set of features of interest
    C= set of other features
    xS= features of interest
    xC= other features
    f ̂s= partial function
    f ̂= ml model

    • In practice, we estimate the function using the following expression:
      Explaining a Machine Learning Model using XAI Methods
    • Given values of the features in , the partial function shows:
      • The average marginal prediction effect, for regression.
      • The average target class probability, for classification.

    Disadvantages

    • The PDP assumes that the features in and  are not
    • A correlation between features can bias the estimated effect due to unlikely data points generated in the computation of the PDP.
    • PDP also hidden heterogeneous effects – since it is the mean of change in marginal effects.

    Example

    On average, the passenger satisfaction probability when it is a business travel is 0.54. For inflight wifi service, no service and 5 rating reach equal or more than 0.70. Loyal customers have 0.48 probability of satisfaction (Fig. 2).

    Explaining a Machine Learning Model using XAI Methods
    Figure 2. PDP for most important features (first row).

     

    We can see some strong correlations between features in the training data (Figure 3). For example, the feature of interest  inflight wifi service plotted above is strongly correlated with ease of online booking. In this case we should trust more the ALE (Accumulated Local Effect) plots, which are not affected by strong correlations.

    Explaining a Machine Learning Model using XAI Methods
    Figure 3. Strong correlations in training data.

     

    Accumulated Local Effect (ALE) Plot

    Intuition

    • ALE plots describe how the features influence the predictions, on average.
    • ALE plots calculate differences in predictions in small windows around the feature value.

    Estimation

    1. Divide the feature in intervals.
    2. Compute differences in predictions for each instance inside the intervals.
    3. Average the difference in predictions for each interval.
    4. Accumulate average across all intervals.
      Explaining a Machine Learning Model using XAI Methods
      Nj(k): neighborhood defined by the k-th interval of feature xj
      nj(k): size of neighborhood (number of instances)
      kj(x): number of intervals of feature xj
      xj(i): i-th instance of j-th column
      zkj: grid value
    5. Center the effect so the mean is zero.
      Explaining a Machine Learning Model using XAI Methods

    Interpretation

    • The value of the ALE can be interpreted as the main effect that a feature has at certain value compared to the average prediction of the data.
    • Example: = -2 (  = 3) then the prediction is lower by 2 compared to the average prediction.
    • The grid intervals can be specified with the feature quantiles.

    Advantages

    • Works when features are correlated.
    • Easy interpretation.

    Example

    We see that a passenger with no service of inflight wifi service has 0.55 more probability of satisfaction that the average passenger. The personal travel plot shows that a passenger on a personal travel has 0.21 less probability of satisfaction than the average passenger, while passengers on a business travel, has 0.21 more probability (Figure 4).

    Explaining a Machine Learning Model using XAI Methods
    Figure 4. ALE plots

     

    Feature Interaction

    • When features interact with others, the sum of the independent feature effects does not fully express the prediction, since the feature effect depends on values of other features.
    • One method to measure the effect between features is the Partial Dependence Variance method.
    • The intuition is that weak interaction effect between two features and  on the response Y suggest that the importance has little variance when one of the features varies and the other is left constant.

     Estimation

    1. Construct the PD (Partial Dependence) function
    2. Compute the feature importance of while  is constant, for all values of .
    3. Take the standard deviation of the resulting importance scores across all values of .
    4. Similarly, we compute the same standard deviation across all values of
    5. Compute the feature interaction averaging the two results.

    There are some interactions detected, such as disloyal customer and personal travel; personal travel and inflight wifi service; or disloyal customer and inflight wifi service (Figure 5).

    Explaining a Machine Learning Model using XAI Methods
    Figure 5. Feature interaction plot.

     

    Local Model-Agnostic Methods

    Local model-agnostic methods aim to explain individual predictions.

    Individual Conditional Expectation

    • Individual conditional expectation (ICE) plots are the PDP equivalent for individual data instances.
    • An ICE plot shows the prediction dependence of all instances, while PDP averages them.
    • The average relationship between feature and the predicted value – PDP output – works when there is a weak interaction between set S and set C.
    • ICE plots provide more insights when there are interactions.

    Example

    We see (Figure 6) that the ICE for type of travel gives us additional information. ICE lines for disloyal customers are flat while loyal customers show a decrease of dependence when it is personal travel. We observe similar patterns for the interaction between inflight wifi service and personal travel or disloyal customers: personal travels and disloyal customers have a low probability for values 1 to 4, while if it is a business travel or a loyal customer, the probability is higher and, in some cases, remains flat at 80%.

    Explaining a Machine Learning Model using XAI Methods
    Figure 6. ICE plots of a sample of randomly selected observations from the training data. It is also displayed the mean prediction at each value of the x-axis. Lines are colored by the interaction feature value. The first part of each title corresponds to the feature mapped by the PD function, while the second part of each title is the one mapped to add color. From left to right: a) blue are loyal customers, b) blue are business travel, and in c) blue are also loyal customers.

     

    Counterfactual Explanations

    • Counterfactual explanations express a causal situation in the form: “if X (causes) hadn’t occurred, then Y (event) wouldn’t have occurred.”
    • In the ML context, Y is the model prediction and X are the feature values.
    • Counterfactual thinking requires imagining a hypothetical situation that contradicts the observed facts.
    • The goal of counterfactuals Is to provide actionable guidance, in the form of steps that a consumer might take to achieve a different output in the future.

     Example

    Here I found three counterfactual explanations for a dissatisfied random passenger. The XGB model predicts dissatisfaction with a probability of 18%. The first counterfactual explanation says that by receiving a better inflight wifi service the passenger is predicted to be satisfied with 71% probability. Similarly, by the second counterfactual, the passenger would have been satisfied if the cleanliness service were a bit better.

    Feature Values
    Gender Female – – –
    Customer type Loyal – – –
    Type of travel Business travel – – –
    Class Business – – –
    Age 33 – – –
    Flight Distance 325 – – –
    Inflight wifi service 2 5 – 3
    Departure/Arrival time 5 – – –
    Ease of Online booking 5 – – –
    Gate location 5 – – –
    Food and Drink 1 – – –
    Online Boarding 3 – – 5
    Seat comfort 4 – – –
    Inflight entertainment 2 – – –
    On-board service 2 – – –
    Leg room service 2 – – –
    Baggage handling 2 – – –
    Check-in service 3 – – –
    Inflight service 2 – – –
    Cleanliness 4 – 5 –
    Arrive Delay in Minutes 7 – – –
    Satisfied 0 1 1 1
    Probability 0.18 0.71 0.53 0.64
    Table 1. Counterfactual Explanations, only changes on features are displayed.

     

    Conclusion

    In this article, we present various agnostic methods, both global and local, to enhance our understanding of the XGBoost model used for binary classification in the context of airline passenger satisfaction. These XAI techniques provide a way to fulfill the right to explanation of machine learning models and provide insights into the key factors that influence passenger satisfaction. Through the application of these methods, we aim to provide a clear, human-understandable explanation of the XGBoost model’s predictions and contribute to the field of Explainable Artificial Intelligence.

    If you found this article on using XAI methods to explain a machine learning model informative, it’s time to take the next step with HyperSense. As a leader in the AI and machine learning space, HyperSense AI provides a comprehensive platform for building, deploying, and explaining models. With HyperSense AI, you can leverage cutting-edge XAI techniques to gain a deeper understanding of your models and make data-driven decisions with confidence. So why wait?

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    References

    1. Molnar, C. (2022). Interpretable Machine Learning: A Guide for Making Black Box Models Explainable (2nd). christophm.github.io/interpretable-ml-book/
    2. L, Breiman, “Random Forests”, Machine Learning, 45(1), 5-32, 2001.
    3. Goldstein, A. Kapelner, J. Bleich, and E. Pitkin, “Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation” Journal of Computational and Graphical Statistics, 24(1): 44-65, Springer, 2015.
    4. Ramavirind K. Mothilal, Amit Sharma, and Chenhao Tan (2020). Explaining machine learning classifiers through diverse counterfactual explanations. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency.
  • Overcoming Challenges in IoT and M2M Settlements in a B2B2X Scenario

    Overcoming Challenges in IoT and M2M Settlements in a B2B2X Scenario

    The growth of IoT and M2M technology has brought about significant opportunities for businesses across industries. However, the complexity of managing the data and transactions involved in this technology has made it challenging for companies to settle accounts in a B2B2X scenario. In this blog post, we will discuss the key challenges that telecom companies encounter in settling transactions related to Internet of Things (IoT) and Machine to Machine (M2M) technology. We’ll also explore strategies to overcome these obstacles and help companies achieve their goals.

    Challenges in IoT and M2M Settlements

    One of the primary challenges that telecom companies face in IoT and M2M settlements is the complexity of the settlement process. Unlike traditional telecom services, which involve a single service provider and customer, IoT and M2M services involve multiple providers and customers. This makes it difficult to manage the transactions and data involved in settling accounts.

    However, settling B2B2X transactions can be challenging due to several factors. One of the significant obstacles is the intricate nature of the ecosystem, which entails the involvement of multiple parties with distinct roles and responsibilities in the settlement process. Unfortunately, this complexity can lead to a lack of transparency that makes it challenging to track and reconcile transactions.

    On top of that, the lack of standardization in the industry poses another challenge that companies must overcome. Different operators and vendors may have their own unique settlement systems, which can make it difficult to integrate and standardize settlement processes across the industry. These challenges can cause setbacks and prolong the settlement process, leading to inefficiencies and delays.

    Moreover, to ensure the integrity of transactions and safeguard against fraud, robust security measures are crucial. With more parties involved in the settlement process, the risk of fraudulent activities and security breaches heightens. These risks can ultimately result in financial losses and tarnish the reputation of the parties involved.

    Overcoming Challenges in IoT and M2M Settlements

    To tackle these hurdles, telecom operators and vendors are exploring advanced settlement solutions. These cutting-edge solutions rely on groundbreaking technologies like artificial intelligence (AI) and machine learning (ML) to enhance the accuracy and efficiency of the settlement procedures.

    AI and ML can help automate settlement processes, reducing the need for manual intervention and improving the speed and accuracy of settlement. Furthermore, these technologies can aid in the detection and flagging of questionable transactions. By doing so, they can assist in mitigating the risks of fraudulent activities and security breaches.

    In addition to AI/ML, industry-wide standardization efforts are underway to improve the efficiency and transparency of settlement processes. The GSMA, for example, has launched a B2B IoT Roaming Guidelines initiative to help standardize settlement processes for IoT roaming.

    Overall, settling B2B2X transactions in the telecom industry can be complex and challenging. However, with the help of advanced settlement solutions and industry-wide standardization efforts, telecom operators and vendors can improve the efficiency and accuracy of settlement processes while reducing the risk of fraud and security breaches.

    If you’re keen to know more about B2B2X settlement in the telecom industry, we’ve got you covered! You can catch our recent webinar on the topic and gain valuable insights. During the webinar, we dive deep into the challenges and solutions surrounding B2B2X settlement, as well as real-world examples and case studies. To access the webinar recording, simply click here.

    In conclusion, settling B2B2X transactions in the telecom industry requires careful consideration of the challenges involved, including the complexity of the ecosystem, the lack of standardization, and the need for robust security measures. By leveraging advanced settlement solutions and making standardization efforts across the industry, telecom operators and vendors can enhance the efficiency and accuracy of settlement processes. This can lead to better business outcomes for everyone involved, helping to streamline operations and reduce errors.

    Explore real-world use cases and get a comprehensive understanding of IoT/M2M solutions!

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  • How AI becomes mission critical in Enterprise Asset Management

    How AI becomes mission critical in Enterprise Asset Management

    The efficient and successful management of the physical assets required to deliver telecommunications services is known as Enterprise Asset Management (EAM) in the telecoms sector. These resources consist of fiber-optic cables, cell towers, and other voice and data transmission infrastructure. It is not surprising that EAM is being used in the telecommunications business given the growing usage of artificial intelligence (AI) in many other industries.

    Automating repetitive operations is one of the key advantages of employing AI in EAM. This covers activities like keeping track of and maintaining assets, spotting possible problems, and planning maintenance. AI-powered solutions, for instance, may be used to evaluate data from asset sensors to spot possible issues before they arise. This might lessen downtime and increase the asset lifespan. AI may also be used to improve maintenance scheduling, guaranteeing that assets are maintained at the ideal time and minimizing the impact on operations.

    Enhancing decision-making is another advantage of employing AI in EAM. AI-powered systems can uncover insights via the analysis of vast volumes of data that would be challenging or impossible for humans to find. AI can, for instance, be used to spot trends in data that point to a certain asset’s propensity to fail. Using this data, maintenance and repair tasks may be prioritised so that the most important assets are taken care of first. AI may also be used to optimise the placement of resources, such as choosing the ideal site for a new cell tower.

    AI has the potential to increase EAM’s effectiveness. AI-powered solutions can free up time for human personnel to concentrate on more complicated and strategic duties by automating repetitive operations and offering insights. By reducing downtime and extending the lifespan of assets, AI may also assist in lowering the cost of EAM.

    Predictive maintenance is one particular use of AI in EAM in the telecoms sector. Utilizing data and analytics to anticipate when equipment is likely to malfunction and plan maintenance appropriately is known as predictive maintenance. This method can assist in reducing unplanned downtime and extending the life of equipment. By using sensors on equipment to collect data and AI algorithms to evaluate the data and spot possible issues, predictive maintenance may be accomplished.

    Network optimization is a particular area where AI in EAM is being used in the telecommunications sector. In order to boost performance and cut expenses, the network setup must be optimised using AI. For instance, AI may be used to analyse network data to spot bottlenecks and improve data routing to cut down on delays. In order to assure the highest coverage and capacity, AI may also be used to deploy assets like cell towers in the best possible locations.

    In conclusion, EAM in the telecoms sector has the potential to become much more effective and efficient thanks to AI. AI-powered solutions may assist in lowering downtime, extending the lifespan of assets, and enhancing network performance by automating repetitive operations, enhancing decision-making, and optimising the deployment of assets. Future uses of AI in EAM in the telecoms sector are probably going to increase as technology develops further.

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  • 7 Reasons to Attend the ‘IoT and M2M Settlement in a B2B2X Scenario’ Webinar

    7 Reasons to Attend the ‘IoT and M2M Settlement in a B2B2X Scenario’ Webinar

    The development of machine-to-machine (M2M) and Internet of Things (IoT) technology is changing how organisations run and complete transactions. New difficulties in concluding these transactions do, however, arise as a result of this transition. Due to this, organisations should attend the IoT and M2M Settlement in a B2B2X Scenario webinar on February 22, 2023, now more than ever.

    this blog article, we’ll look at the reasons why companies need to handle these settlement issues if they want to keep up with the quickly evolving IoT and M2M technology ecosystem.

    • New Challenges in Settlement Techniques: The Internet of Things (IoT) and M2M technologies are significantly changing how organisations settle transactions, but this influence is not without difficulties. In order to ensure seamless and effective transactions, these new technologies are posing enterprises with new and difficult settlement challenges. Learn about these difficulties in this webinar, along with strategies for dealing with them.
    • Streamline and Enhance Settlements: According to a research by ABI Research, the use of IoT and M2M technology may help firms cut operating expenses by up to 40%. Businesses must deal with the difficulties in the settlement processes in order to gain these savings, though. By utilising IoT and M2M technologies, you will discover how to simplify and enhance settlements in this webinar.
    • Keep Ahead of the Curve: According to a poll conducted by Business Insider Intelligence, 60% of organisations either now use or want to use IoT technology in the next five years. Businesses must deal with the difficulties in settling IoT and M2M transactions if they want to stay ahead of the market. You will get knowledge of the main factors influencing the growth of IoT/M2M solutions as well as how to navigate the market in this webinar.
    • Real-World Use Cases: Hearing about the potential advantages of IoT and M2M technologies is one thing; seeing them in action is quite another. In this webinar, you’ll examine actual use cases and see how businesses are resolving settlement issues and streamlining their processes.
    • Significant Market Growth Potential: According to a MarketsandMarkets analysis, the IoT in the B2B market is predicted to increase from USD 99.2 billion in 2018 to USD 194.4 billion by 2023, at a CAGR of 14.5% over the forecast period. This enormous market expansion underlines how crucial it is to handle settlement issues if you want to keep up with the fast-evolving IoT and M2M technology ecosystem.
    • Better Business Results: According to Gartner research, companies that have integrated IoT solutions have witnessed a 15% boost in their business results. Businesses must handle the IoT and M2M settlement difficulties if they want to attain these better results. You’ll discover how to address these issues and enhance your company outcomes in this webinar.
    • Expert Speakers: Paulo Zanotto, Director of Business Consulting at Subex, and Subrat Saurabh, Head of Partner Ecosystem Management at Subex, will serve as the webinar’s expert presenters. These seasoned experts will share a wealth of information and views on IoT and M2M technology as well as the difficulties in concluding agreements.

    Finally, in order to stay ahead in the fast-changing market, organisations must handle the settlement difficulties in IoT and M2M technologies. Attending the IoT and M2M Settlement in a B2B2X Scenario webinar on February 22nd, 2023, is a wonderful way to learn about the problems and solutions in settling IoT and M2M transactions, examine real-world use cases, and network with other experts in the area. We look forward to seeing you there!

    Explore real-world use cases and get a comprehensive understanding of IoT/M2M solutions!

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  • What is an AI platform?

    Any type of action-conscious automation needs a method that can use artificial intelligence to assist digitalization and provide outcomes that are at least as good as those that would have been produced by humans. Recently, there has been an increase in demand for this skill, which is where AI platforms come in.

    The term “AI platform” refers to a hardware architecture or software framework (including application frameworks) that enables software to address key issues in artificial intelligence (AI), such as reasoning, planning, learning, natural language processing, perception, and object manipulation. Platforms are used to imitate cognitive processes carried out by human minds, like reasoning, social intelligence, and general intelligence.

    Statistical techniques, artificial intelligence, soft computing, and conventional symbolic algebra are all methods. Al uses a variety of tools, including iterations of search and mathematical optimization, reasoning, and techniques based on chance and economics.

    What is the definition of Artificial Intelligence Platforms?

    AI platforms may be categorised as either strong AI, also known as artificial general intelligence, which can solve problems for novel tasks, or weak AI, also known as narrow AI, which is often intended for a specific job.

    Artificial intelligence is said to include machine learning. You need trustworthy and high-quality data for it to operate. To get started, simply decide what you want to do, locate the data that is already accessible, and then sit back and let machine learning handle the rest. Without explicit instructions, machine learning uses algorithms and statistical models to carry out a specified task, depending on patterns and inference.

    Automation

    If you want to fully profit from your Al, you must have this feature. Making software that can carry out tasks automatically and without human involvement is called automation. One can save time and resources by automating laborious procedures, freeing up staff time for tasks that demand human interaction. The automation platform you choose should be a simple solution that can handle various automation processes with ease and doesn’t require any special knowledge. You may easily automate tasks with the correct system.

    Natural language processing and natural language

    For your Al solution to be fully optimised, these two qualities are essential. This is due to the requirement for a system capable of supporting complete speech recognition and interaction. With speech recognition, natural language comprehension, and natural language production, this aids in the processing and analysis of significant volumes of natural language data.

    Cloud infrastructure: This capability gives you the resources and scalability to deploy even the most complicated artificial intelligence and machine learning systems. For you to truly profit from Al and cloud, you must integrate the two. When introducing Al solutions, it’s critical to make use of the platform as a service (PaaS) and software as a service (SaaS) to guarantee that all resources are always available.

    Understanding the basics of AI platforms and their role in business

    Whether you’ve just started evaluating AI platforms or have already made investments in AI technologies like machine learning, it’s crucial to be aware of what’s available in the artificial intelligence platform market right now in 2023. A company’s success may be greatly benefited from learning useful insights on how to choose enterprise AI software and devices and how to integrate such AI tools into your current IT infrastructure. This may be a necessary step as we progress toward the next era of intelligent technologies.

    Big Data usage has continued to develop and flourish, with some firms experiencing significant benefits. Platforms for artificial intelligence (AI) have lately brought the processing of big data to a new degree of progress. The next ten years are expected to see considerable effects (and disruptions) from AI systems. Business intelligence and analytics will benefit from hitherto unrealized advancements as a result of the usage of AI to handle large datasets, among many other technologies.

    The main forces behind raising an AI platform’s quality are machine learning and data training. Algorithms are used in machine learning to assess data, learn from it, and then create predictions. Decision tree learning, clustering, reinforcement learning, and inductive logic programming are examples of algorithmic techniques. Deep Learning creates artificial intelligence by using algorithms and “artificial neural networks.”

    What are AI Platform tools?

    A framework called an AI Platform is made to work more intelligently and effectively than conventional frameworks. When properly developed, it enables enterprises to collaborate with data scientists and personnel more quickly, effectively, and efficiently. It may save expenses in a variety of ways, including avoiding duplication of effort, automating basic processes, and getting rid of some high-cost activities like copying or data extraction. Additionally, an AI platform may offer data governance, ensuring that a group of AI scientists and ML programmers uses best practices. Additionally, it can help to make sure that work is finished more swiftly and more equally.

    What is AI Platforms’ Role for Businesses?

    Big Data analysis with artificial intelligence can offer a greater comprehension of a company’s internal and external dynamics. Artificial intelligence is supported by using the most up-to-date Big Data architecture and machine learning techniques. In 2023, the following will serve as the foundation for a successful AI platform that is both cutting-edge and modern:

    • AI with full access to all information
    • AI that can study the past behaviour of customers or prospects
    • Conscious AI can use self-reference to display strategies that have previously succeeded by using experience from prior, comparable customers.
    • AI continuously observes and learns, spotting patterns that humans might overlook
    • The AI can quickly pick up new information and respond in real-time while adapting to it.
    • AI that uses machine learning to forecast and offer insights based on changes in the data

    There are a few standard needs to meet in order to optimise the outcomes provided by state-of-the-art artificial intelligence.

    Framework for analysis

    Methodologies called analytical frameworks were created throughout time to address certain business issues (often complex). Supporting the system’s artificial intelligence and machine learning capabilities requires the use of an analytical framework.

    Context is also a necessity. 

    At the moment, machine learning and artificial intelligence are terrible at understanding context. AI is capable of seeing patterns and figuring out what is occurring in the data, but it lacks the ability to go beyond trend insights and identify actions that staff members should do. It is envisaged that AIs will eventually be able to understand context, however, this is not yet a reality. Currently, a human must choose the context and add it to the model.

    Appropriate technology is the third requirement. 

    An AI-supported platform must be scalable for the AI to learn and develop solutions, unlike traditional analytical systems. While an AI would offer suggestions in real time, a standard analytical system would provide insights into the data.

    In order to scale databases up to very large volumes while simultaneously pushing ever-faster transaction rates per second, a variety of different techniques are utilised. The bulk of database management systems employs the strategy of partitioning tables with a lot of data.

    A database may expand out over clusters of different database servers using this strategy. Additionally, multi-threaded systems that may significantly scale up transaction processing capacity can now be supported by multi-core CPUs, huge SMP multiprocessors, and 64-bit microprocessors.

    What is an Enterprise AI Platform?

    An integrated collection of technologies known as an enterprise AI platform enables businesses to design, create, deploy, and manage enterprise AI applications at scale. A new subcategory of corporate software is represented by enterprise AI applications. In comparison to earlier generations of business software, creating and deploying this kind of application at scale includes substantially more difficulties and calls for a new technological stack.

    What are the capabilities Enterprise AI platforms enable for Businesses?

    A corporate AI platform has to include the following 10 key characteristics in order to provide a full solution:

    Data gathering throughout the organisation, a federated data picture that is unified

    • Data persistence and multi-cloud computing
    • Edge processing
    • Platform services and data virtualization are already available for accessing data.
    • Business semantic model
    • business microservices
    • Enterprise data security and governance
    • utilising AI and dynamic optimization methods for system simulation
    • open ground
    • Common platform for software engineers and data scientists to work together on projects

    The AI hype cycle is reaching its height, and numerous technologies are marketed as “business AI platforms.” But a solution cannot be referred to as an enterprise AI platform if any one of these 10 essential qualities is lacking.

    Why is an Enterprise AI Platform Important?

    The main catalyst for digital transformation is enterprise AI. Nearly all corporate software applications will be AI-enabled in the upcoming years. Organizations won’t be able to successfully function and compete without the usage of corporate AI capabilities, just as they would not be able to do business without a CRM or ERP system today. Therefore, it is increasingly necessary for businesses to have the skills necessary to design, implement, and run corporate AI systems at scale.

    In order to handle several high-value use cases throughout an organization’s full value chain, enterprise AI apps – dozens or hundreds of them – must be deployed at scale. A corporate AI platform offers the tools and capabilities necessary for businesses to successfully design and run these applications while spending the least amount of time, money, and resources possible.

    What do AI Platforms offer Telecommunications?

    • AI enabling intelligent networks

    The transformation that alters sectors and businesses becomes a reality as 5G, IoT, and Edge gain pace. The coexistence of cutting-edge and antiquated technology, hybrid networks, a wide range of frequency bands and spectrums, and a proliferation of connected devices all add to the complexity of network operations. In addition, the network has to be further optimised and optimised for performance due to increasing requirements from IoT and industrial use cases. In order to reduce complexity, solve the needs of new technologies and use cases, improve network performance, and allow network automation, the AI Platform is working in this environment.

    • AI and automation

    AI platform solutions can assist in achieving a high level of practical autonomous operation by combining a variety of currently accessible and well-understood AI approaches inside a flexible framework. This will usher in a period of intelligent, autonomous networks that require almost no human interaction. The true usefulness of AI will eventually be seen in telecom networks, not only in applications that link to the network.

    • Trustworthy AI

    The development, deployment, and usage of AI must be based on trust if their full potential is to be realised. In order to handle issues ranging from explainability and human supervision to security and built-in safety features, it is crucial that we use AI platforms. Trustworthiness is a requirement for AI, and AI platforms assist in designing it into the system.

    • Remote robotics and 5G

    Zero-touch operations and an environment where the network would automatically change and respond based on the demands of the robots are being made possible by AI.

    The adoption of AI is crucial for effective network management and operations in 5G. The complexity of 5G networks may be addressed with the aid of AI and automation, which can also increase productivity, enhance customer satisfaction, and create new income streams.

    • Network Optimization

    By 2025, 20% of all connections will be made using 5G networks, which are anticipated to launch in 2019. By that time, there will be more than 1.7 billion users worldwide.

    AI is needed to create self-optimizing networks (SONs) for CSPs to facilitate this expansion.

    These give network administrators the ability to automatically enhance network quality based on traffic information relevant to particular regions and time zones.

    In the telecom industry of AI, sophisticated algorithms are employed to look for patterns in data, enabling telecoms to both diagnose and foresee network problems.

    CSPs can prevent problems from negatively affecting customers by using AI in the telecom industry to proactively handle problems.

    • Predictive Maintenance

    Telecoms are being helped to provide better services by AI-driven predictive analytics, which makes use of data, sophisticated algorithms, and machine learning techniques to make predictions about the future based on the past. This suggests that equipment operators can track its health and predict failure based on patterns utilising data-driven insights. CSPs can proactively address issues with communication hardware, such as set-top boxes in consumers’ homes, cell towers, power lines, and data centre servers, by using AI in the telecoms industry. The automation and intelligence of the network will enhance short-term root cause analysis and issue forecasting. In the long run, these technologies will help with broader strategic goals like creating fresh consumer experiences and successfully addressing shifting business needs.

    • Virtual Assistants for Customer Support

    Another application of AI in communications is conversational AI systems. They are also referred to as virtual assistants and have perfected the art of efficiently automating and scaling one-on-one conversations. The deployment of AI in the telecom sector aids in managing the vast volume of setup, configuration, debugging, and maintenance support requests that occasionally overburden customer care centres. Using AI, operators can provide self-service capabilities that show customers how to set up and utilise their own devices.

    • Robotic Process Automation (RPA) for Telecoms

    Human error is possible in each of the daily millions of transactions that the sizable client bases of CSPs take part in. A type of AI-based business process automation technology is robotic process automation (RPA). RPA may boost the effectiveness of telecom processes by allowing telcos to more easily manage their back-office operations and significant amounts of repetitive and rules-based actions. RPA frees up CSP employees for higher value-added duties by automating the completion of difficult, labor-intensive, and time-consuming processes including billing, data entry, workforce management, and order fulfilment. The RPA market is anticipated to reach a value of USD 13 billion by 2030 and almost all businesses would have adopted RPA by then. Cognitive computing is expected to “substantially change,” according to businesses in the telecom, media, and IT sectors.

    • Fraud Prevention

    To combat fraud, telecom companies are turning to AI’s powerful analytical capabilities. Telecom-related fraud, such as false profiles and unauthorised network access, has been greatly reduced thanks to real-time anomaly detection capabilities offered by AI and machine learning algorithms. The system may immediately restrict access to the fraudster after detecting suspicious behaviour, lessening the harm. Given that industry estimates indicate that 90% of operators are routinely targeted by fraudsters, incurring in billions in losses annually, this AI application is very essential for CSPs.

    • Revenue Growth

    AI can combine and make sense of a wide range of data, including data from devices, networks, mobile apps, geolocation, in-depth client profiles, service use, and billing. Using AI-driven data analysis, telcos may increase their average revenue per user (ARPU) and subscriber growth rate through intelligent upselling and cross-selling of their services. Telecoms may anticipate customer requests using real-time context to deliver the relevant offer on the right channel at the right time.

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