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  • Unleashing the Potential: How Generative AI is Revolutionizing Telcos

    Unleashing the Potential: How Generative AI is Revolutionizing Telcos

    In the age of rapid technological advancements, industries around the world are witnessing unprecedented transformations, and the telecom industry is no exception. Communication service providers (CSPs) are at a pivotal juncture. From stagnating revenues to an ever-increasing strain on networks posed by the relentless demands of 5G, to challenges in delivering innovative customer experiences, the telecommunications industry faces an immense pressure to undergo a comprehensive transformation.

    Over the past few years, CSPs around the globe have turned to Artificial Intelligence (AI) to address some of these challenges, but the lion’s share of an operator’s operational expenses is still spent on infrastructure and data management. The integration of artificial intelligence (AI) into telecom operations has led to groundbreaking changes, with generative AI emerging as a particularly revolutionary force. Generative AI, a subset of AI that can create new content, such as text, images, and audio. This technology is having a major impact on the telecommunications industry, and it is changing the way we communicate. It has found its way into various facets of the telecom sector, reshaping the way services are provided, networks are managed, and customer experiences are enhanced.

    Below is the image depicting the results of the survey conducted by the Capgemini Research Institute, showing the percentage of organizations that have established a dedicated team and budget to integrate Generative AI into future product/service development plans, categorized by sector.

    Unleashing the Potential How Generative AI is Revolutionizing Telcos

    According to the survey conducted by Capgemini Research Institute, while 69% of surveyed telecom executives believe the benefits of generative AI outweigh potential risks, only 12% think generative AI will significantly disrupt their industry. Many companies have already started looking into possible applications of generative AI. Among telecom organizations, 36% have a dedicated team and budget. At the same time, 47% said they have started exploring its potential, while another 49% have begun working on pilots. Only 4%, meanwhile, have enabled generative AI capabilities in some functions or locations. Among telecom companies, 36% say they are piloting generative AI applications in call center analytics.  Another application is restoration of old media, with 30% of telecom organizations surveyed conducting pilots. Also 71% of the major telecom companies surveyed are using or planning to use generative AI in the IT sector.

    One of the most important ways that generative AI is changing telecommunications is by improving network performance. Generative AI can be used to optimize networks and make them more efficient. This can lead to faster speeds, better reliability, and lower costs. It also improves customer service by creating virtual assistants that can answer customer questions and provide support. This can free up human customer service representatives to focus on more complex issues. Generative AI is also being used to improve network security that can detect and prevent fraud and other security threats. This can help to protect customers and their data. As generative AI continues to develop, it is likely to have an even greater impact on the telecommunications industry in the years to come.

    Below are some of the examples of how generative AI is being used in the telecommunications industry:

    1. Co-pilots for Fraud Management and Business Assurance

     Generative AI co-pilots in telcos aid Fraud Management and Business Assurance by analyzing data for anomalies, patterns, and compliance. These AI systems work alongside human analysts to assist and swiftly detect fraud and billing errors, adapting to evolving tactics. These AI systems ensure real-time monitoring, issue alerts, and enhance operational efficiency, reducing false positives. Their 24/7 vigilance and collaboration with human experts optimize fraud prevention, regulatory adherence, and service quality, boosting telecom integrity and customer trust.

     2. Enhanced Customer Experiences

     Customer satisfaction is paramount in the telecom industry. Generative AI is transforming customer experiences by personalizing interactions and tailoring services to individual preferences. AI chatbots and virtual assistants ensure 24/7 support with precise, natural language responses, boosting engagement and loyalty through seamless, human-like interactions.

     3. Network Optimization and Management

    Telecommunication networks are becoming increasingly complex, with the proliferation of devices and the demand for high-speed, low-latency connectivity. Generative AI algorithms can analyze vast amounts of data to predict network congestion, optimize routing, and enhance overall network performance. By constantly adapting to changing conditions, these AI systems can ensure seamless connectivity and improve the quality of service for end-users.

    4. Predictive Maintenance

    Maintaining telecom infrastructure is a critical aspect of the industry. Generative AI revolutionizes telecom maintenance by offering predictive capabilities that enable providers to anticipate equipment failures before they occur. It anticipates equipment breakdowns by analyzing historical and real-time data, enabling proactive intervention. This reduces downtime, trims costs, and boosts maintenance efficiency.

    5. Network Security and Fraud Detection

     As telecom networks handle an enormous amount of sensitive data, security is a top concern. Generative AI is vital in spotting and countering security threats, detecting anomalies in network traffic for possible cyberattacks or unauthorized access. It identifies fraud like SIM card cloning and unauthorized billing. By bolstering security measures, generative AI helps maintain the integrity of telecom networks and protects customer data.

    6. Content Generation and Advertising

     Generative AI has also made its mark in content generation and advertising within the telecom industry. Telecom providers can use AI to create personalized marketing content based on user preferences and behaviors. This level of customization enhances the effectiveness of advertising campaigns and boosts customer engagement. AI automates web, social content creation, saving telecom time and resources.

    The benefits of adopting generative AI are clear: more innovation, more efficient services, more productive employees, and ultimately, happier customers. All of these factors contribute to a significant competitive advantage.

    The convergence of generative AI and the telecom industry is reshaping the landscape of connectivity and communication. By optimizing network management, enhancing customer experiences, fortifying security measures, and automating content generation, generative AI is revolutionizing the way telecom services are provided. As the industry continues to evolve, embracing AI-driven innovations will be crucial for telecom providers to remain competitive, efficient, and customer-focused in an increasingly digital world.

    Note: We are conducting a survey on the scope of Generative AI in the Telecom industry. Given your extensive expertise in the industry, I believe your insights would be valuable in understanding how Generative AI can benefit the telecom sector, address challenges, and explore opportunities for its implementation. To participate, please click on the survey link.

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  • The False Positive Fallacy in Revenue Assurance and Fraud Management

    The False Positive Fallacy in Revenue Assurance and Fraud Management

    Alex Gendler published an interesting TED-Ed video on the false-positive paradox. He narrates a hypothetical scenario where a precious metal ‘unobtainium,’ present within only 1% of rocks in a specific quarry, must be mined. Joe, the miner, uses a reading device to identify which rocks contain the precious metal. His device will always detect unobtanium when present. It will also give the correct reading 90% of the time when a rock does not contain unobtanium. Now say Joe’s device beeps when pointed to a rock, and he offers to sell it to you for $200. Is it worth buying this rock from Joe for $200, knowing that you can sell an unobtanium rock for $1000, based on the reading of the detection device?

    To determine the answer to this question, it is imperative to note how rare the presence of unobtanium is as well as the 10% likelihood of false positives from the reading device.

    Any anomaly detection test or model that is not 100% accurate bears the risk of false negatives and false positives. In the revenue assurance and fraud management (RAFM) domain, the presence of false positives can be tricky as it utilizes resources to investigate that the anomaly is, in fact, not a problem.

    Like Gendler’s scenario, one fundamental point to remember is that revenue leakage within revenue assurance and fraud management departments is quite rare. But since nearly 75% of operators report at least 1% leakage of the total revenue, false positives warrant some investigation.

    What would be the successful formula for investing in a system that identifies rare leakages or fraud, especially when false positives abound? 

    Here are some key elements to look out for:

    1.  Calculating ROI 

    Any well-designed revenue assurance and fraud management strategy must articulate short-term as well as long-term benefits. But as time passes, software/controls age, become redundant, or lose their effectiveness. Moreover, it is seen that as operator maturity increases, leakages decrease and, hence, the probability of identifying these leakages.

    Recommendations:

    From a practical perspective, operators need updated solutions to adapt flexibly to changing scenarios such as new revenue streams and broader risk coverage.

    Each solution should encompass well-defined KPIs to measure not only the intended output of revenue assurance and fraud management (like the number of subscribers impacted and revenue at risk) but also its quality (like the effectiveness of a revenue assurance and fraud management control).

    2. Choosing controls

    The process of designing a ‘control’ or a ‘check’ is the key to minimizing the hassle of false positives. Revenue assurance and fraud management teams often struggle to manage the reference data, data specifications, connectivity, latency, etc. All data management parameters can influence false positives and, thus, require timely correction. Since all control systems are not equal, arriving at an effective control design (including the alarm thresholds) is challenging.

    Recommendations: 

    Establish key indicators to measure system performance for business outcomes as well as revenue assurance and fraud management processes.

    Track support processes on overall outcomes to avoid unwanted influences on the quality of revenue assurance and fraud management controls.

    Streamline processes through artificial intelligence (AI) and machine learning (ML) to correlate different outcomes and reduce the number of false positives. To understand the significance of this, even low levels of false positives compromise the ability to detect genuine leakages.

    3. Maintaining accuracy

    The probability of finding a genuine alarm in the revenue assurance and fraud management system indicates how much effort is needed to identify leakages. It also signals the number of alarms that will be closed as ‘non-fraud.’ False positives are usually measured from the system’s perspective, i.e., how many false alarms exist out of the total alarms generated? But most systems do not reflect the actual effort involved in getting the expected benefits of the control.

    Recommendations:

    Identify the potential trade-offs between implementing a control, its benefits, and its costs. The cost is mostly understood as a development effort but should also include maintenance and monitoring effort.

    Consider each control in the broader context of the overarching control framework and risk coverage. This may unearth risks that are over-controlled or under-controlled, thereby suggesting areas that need improvement.

    4. Easy monitoring 

    When the probability of identifying a genuine fraud attempt or revenue leak decreases, the monitoring effort increases because more alarms and controls will need to be set to identify the incident.

    Recommendations: 

    • AI and ML can quickly identify false positives, determine likelihoods, and present these to analysts for immediate action.
    • Revenue assurance managers can save time spent on investigating cases that are not leakages. For fraud management leaders, it helps avoid customer discontent when cases are not fraudulent.

    Dealing with the uncertainty of false positives is a painstaking exercise. By implementing the right controls and tweaking these systematically, revenue assurance and fraud management teams can maximize the return on investments of their monitoring systems.

    (The answer to the unobtainium question is: No. Statistically, the buyer has only a 9% chance of finding unobtainium in the rock, which is pretty poor odds that the $200 investment will pay off!)

    In your experience, what best-practices help deal with false positives?

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  • Unleashing the Power of Asset Assurance: Overcoming Telecom Challenges

    Unleashing the Power of Asset Assurance: Overcoming Telecom Challenges

    Subex proudly stands as a key contributor to the GB1004C Business Assurance Transformation – Asset Assurance v1.1.0

     Introduction

    In today’s rapidly evolving telecommunications industry, effective asset management has become paramount. With the proliferation of new technologies and an array of valuable assets, telecom operators face numerous challenges. However, with the implementation of Asset Assurance, a crucial layer of protection, these challenges can be effectively mitigated. In this blog, we will delve into the challenges faced by telecom operators and explore how Asset Assurance provides robust solutions to optimize operations, reduce risks, and maximize profitability.

    Challenges in Telecom Asset Management:

    1. Understanding Total Cost: One of the foremost challenges in asset management is gaining a comprehensive understanding of the total cost involved. From initial purchase to operations, repairs, maintenance, and eventual resale or depreciation, telecom operators need to analyze the financial aspects of assets. Asset Assurance enables operators to gain clarity on cost structures, aiding in making cost-effective choices and optimizing financial planning.

    2. Increasing Complexity: As the telecom landscape continues to evolve with the introduction of 5G, IoT, and cloud technologies, the complexity of managing network assets grows exponentially. Densification of networks, deployment of virtual environments, and handling diverse workloads require streamlined asset management processes. Asset Assurance provides a structured framework to handle the complexity, ensuring assets are efficiently managed throughout their lifecycle.

    3. Process Gaps: Process gaps within organizations can lead to operational losses and inefficiencies. Inadequate controls and deviations from standardized asset management processes expose telecom operators to risks such as loss, theft, and displacement of equipment. Asset Assurance emphasizes the importance of robust controls, enabling end-to-end tracking and management of assets. By addressing process gaps, operators can enhance efficiency, reduce errors, and minimize operational losses.

    4. Loss of Assets: Loss, theft, and displacement of equipment pose significant challenges for telecom operators. These incidents not only impact the operator’s books but also result in operational losses and compromised customer experiences. Asset Assurance promotes rigorous tracking and management of assets, ensuring their integrity and minimizing the risk of asset loss. By implementing comprehensive asset tracking systems, operators can mitigate losses and enhance their overall asset management practices.

    5. Inadequate Controls: Inefficient controls and a lack of centralized asset tracking mechanisms can hinder effective asset management. Without proper controls in place, telecom operators may struggle to monitor assets, track their movements, and ensure compliance with industry regulations. Asset Assurance emphasizes the need for robust controls, including asset acquisition management, distribution management, and return management. By implementing stringent control mechanisms, operators can improve asset visibility, reduce errors, and optimize their asset management processes.

    Asset Assurance is a solution for Telecom Challenges: Asset Assurance serves as a comprehensive solution to address the challenges faced by telecom operators in asset management. By implementing the following strategies, operators can unlock the full potential of Asset Assurance:

    1. Inventory Management: Effective inventory management is vital for telecom operators. Asset Assurance ensures accurate asset acquisition management, optimal storage and distribution processes, and streamlined return management. By leveraging advanced technologies and analytics, operators can enhance inventory visibility, minimize losses, and improve overall asset utilization.

    2. Cost Optimization: Asset Assurance enables telecom operators to optimize costs associated with asset acquisition, storage, repairs, and maintenance. By leveraging real-time data analytics, operators can identify cost-saving opportunities, negotiate favorable terms with vendors, and make informed decisions regarding asset disposal or resale. By optimizing costs, operators can enhance profitability and financial performance.

    3. Marketing Strategies: Asset Assurance empowers operators to devise effective marketing strategies for devices. Accurate device pricing, bundled service offerings, buy-back programs, and customer upgrade management can all be streamlined with Asset Assurance. By aligning marketing strategies with asset management practices, operators can enhance customer satisfaction, drive revenue growth, and foster customer loyalty.

    4. Customer Satisfaction and Loyalty Management: Asset Assurance plays a pivotal role in customer satisfaction and loyalty management. By offering efficient device pricing, seamless upgrade options, and hassle-free return processes, operators can enhance the overall customer experience. Asset Assurance helps build trust, strengthen customer relationships, and boost long-term customer loyalty.

    Partnering with Subex for Asset Assurance Implementation:

    Subex, a leading provider of telecom analytics solutions, offers expertise and support for telecom operators looking to implement Asset Assurance. With extensive experience in asset management and risk mitigation, Subex assists operators in optimizing their asset lifecycle processes. By leveraging advanced analytics and control calculation capabilities, Subex helps operators quantify and monetize asset leakage, identify risks, and achieve tangible benefits from Asset Assurance implementation. With Subex as your partner, you can streamline your asset management practices, enhance operational efficiency, and maximize profitability.

    Conclusion:

    Asset Assurance has emerged as a critical component in addressing the challenges faced by telecom operators in asset management. By implementing Asset Assurance, operators can optimize costs, streamline processes, mitigate risks, and enhance customer satisfaction. With the support and expertise of Subex, operators can unlock the full potential of Asset Assurance, enabling them to thrive in the dynamic telecommunications industry. Embrace Asset Assurance today and experience the transformative power it brings to your telecom operations.

    Subex, in collaboration with TM Forum and leading telecoms and vendors, has played a pivotal role in creating the TM Forum Asset Assurance Guidebook. This comprehensive guidebook offers valuable insights and best practices to help telecom operators overcome the challenges associated with asset management. It is the result of extensive industry collaboration and expertise, providing operators with a trusted resource to enhance their asset management practices. If you are a member of the TMF Forum community, download this essential guidebook and unlock the power of Asset Assurance in your telecom operations, click [here]. Don’t miss out on this opportunity to optimize your asset management and drive operational excellence.

    Book a free session today with Harish Patnala, a key contributor and subject matter expert who played a lead role in creating this TM Forum Asset Assurance Guidebook.

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  • In a conversation with GO Malta about Fighting CLI Spoofing

    In a conversation with GO Malta about Fighting CLI Spoofing

    Voice over Internet Protocol (VoIP) networks are extremely popular for their ability to save costs, support innovations like 5G, and roll out features such as video calling. With the workforce becoming increasingly distributed,businesses and customers want affordable international calling plans that support mobility. Some estimate that the VoIP market will reach US $30 billion by 2025.

    However, IP networks have vulnerabilities that expose them to risk.

    Arvind Rao, Director of Business Solutions Consulting at Subex Limited, and Charmaine Galea Triganze, Fraud Prevention Officer at GO (Malta), recently presented at the CFCA Educational event, where they discussed the problem of CLI Spoofing at GO. According to Arvind, “VoIP-based attacks constitute roughly 45% of all fraud security events today, as per the recent CFCA report. In fact, out of the top 10 fraud methods recorded by CFCA, 6 of them are related to VoIP-based attacks.”

    (In case you missed the event, the conversation below offers the main highlights.)

    To provide a context of what was happening at GO, Charmaine recalls, “We were getting reports from broadband customers, internal employees, retail customers, and business customers that something wasn’t right. Someone would receive a call that originated from Italy, an EU country, but it would be their relative based out of Canada or Australia. As these reports increased, we started investigating the CDRs in our existing fraud management solution, and the data was conflicting, showing the number was from Italy or any other EU country. That was when we called Subex to do a proof of concept. What we found was quite alarming.”

    Since the EU works on an origin-based rating, rogue carriers were exploiting a loophole to pay lower terminating charges. As the investigation deepened, more concerns surfaced. Caller ID spoofing was wreaking havoc: Scam calls had increased during the pandemic. Scamsters began spoofing numbers of well-known businesses in Malta. Customers, trusting of EU numbers, were prone to pick up these calls. Apart from being a nuisance to retail customers, it created immense ecosystem challenges.

    GO collaborates with law enforcement to aid criminal investigations by providing information about a suspect’s call records or location. However, in many cases, the data passed on from CDRs turned out to be incorrect. Charmaine states, “The police would complain, stating they asked for a specific person’s records but based on the data we shared, it was implausible that the suspect made these calls from the number provided.” It was compromising the operator’s reliability.

    One of the most telling stories Charmaine narrates was how spoofed numbers were disrupting Malta’s emergency services. “People would call emergency services, request assistance, and hang up. On tracing the call, emergency services would be dispatched to the location only to find confused people wondering why the police were at their door! You can understand that the emergency department was not happy. They repeatedly asked us to stop the spoofed calls or at least sieve out the genuine ones from spoofed numbers so they would know which to ignore and which to respond to. But the existing system was not able to give us this information.”

    Modern problems need modern solutions

    Arvind explains why, “Traditional fraud management systems which are reactive, transaction-based and rely entirely on call detailed records, are not best suited for addressing these VoIP based frauds in real-time.”

    To visualize this, let’s consider the standard OSI stack starting from Layer 1 to Layer 7 and walk through what happens during a CLI spoofing attack.

    CLI spoofing attacks typically begin with a port scan at Layer 3, wherein a program looks to identify what services are running on the network. Once discovered, probing or brute force attacks ensue, after which the attack moves to the application layer or layer 7. “This is where we start seeing breaches,” says Arvind. “However, many lower-level attacks and failed attacks are not monitored, as no records or transactions are generated. Thus, traditional fraud management won’t kick in. Moreover, such systems do not track these failed attempts.”

    “Here is where Signaling Risk Intelligence systems make a difference,” adds Arvind. ” A robust signaling-based detection and prevention system monitors signaling information across the layers and look for malicious behavior. From a technical aspect, they observe network packet captures, usually using port mirroring in a non-intrusive manner, without causing any network lag. Using shallow and/or deep packet inspections to identify markers or threat signatures like SIPvicious software as the user-agent. Apply other detection measures such as heuristics-based detection and look for a relationship between A and B numbers, including looking for calls made or from unallocated numbers.”

    The issue of unallocated numbers is an important marker of fraud as this highlights a behavior wherein the random dialer/program showcases malicious behavior.

    Put into the mix of using an AI/ML-based approach for further identifying hidden correlations and patterns, a robust solution can be deployed. In fact, such a trifecta approach was enabled by Subex at Go Malta to address the CLI spoofing fraud.

    A host of benefits

    Charmaine is glad because since implementing Subex’s signaling security solution, GO can stop calls directed to unallocated numbers. “We started charging the highest terminating rates for calls to unallocated numbers from partner carriers, and soon, this practice reduced drastically, saving us revenue.”

    The solution also performs several other tasks, such as analyzing error codes and using advanced AI/ML to analyze dialing patterns and identify spikes in traffic using anomaly detection. “We didn’t know much about monitoring signaling traffic, but Subex provided us with the right training to understand SIP signaling,” she declares.

    Subex’s extensive (the world’s largest) honeypot network is an added advantage. It provides threat intelligence from external databases and has a repository of over 55,000 different malicious signatures and more than 8,000 malicious IP addresses to give operators an edge in addressing technical frauds.

    Insights from the solution are also helping Go Malta do other things apart from improving assistance to law enforcement agencies and protecting its customer from spoofed calls. Charmaine notes, “Our business clients were using international platforms, so we found a way to offer them local solutions, which gave us more revenue while helping them save costs.” Customer satisfaction, a foundational lever at GO, has also risen because customers are duly informed when a number is spoofed.

    Follow the money

    Can a signaling-based defense mechanism give operators a leg up on dealing with new-age frauds?

    “Absolutely!” asserts Arvind. “It ticks off the three main goals for fraud management, i.e., to widen fraud coverage, reduce fraud run-time, and improve the fraud hit ratio.”

    Malta is already leveraging the much-needed boost. Recently, the commissioner of police set up a task force between three major operators in Malta (including GO), banks, and postal services. Charmaine opines, “There is no single way to fight fraud. CLI spoofing affects all of us because people are lured into disclosing financial information, affecting banks too. Only through ecosystem cooperation can we make sure that fraudsters don’t make a financial profit.”

    Warmly referring to GO’s anti-fraud solutions as ‘her toys,’ Charmaine quips, “As I say, ‘Follow the money. The more toys we have, the better we can track the fraudsters, and the stronger our defense!”

    Meet our experts to discover the features of Subex’s Signaling Security Solution.

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  • Machine Learning in Financial Markets: Applications, Effectiveness, and Limitations

    Machine Learning in Financial Markets: Applications, Effectiveness, and Limitations

    Introduction

    Welcome to our comprehensive guide on machine learning in financial markets. In this blog post, we will delve into the applications, effectiveness, and limitations of machine learning in the finance industry. Whether you’re a data scientist, investor, or simply interested in the intersection of technology and finance, this article will provide valuable insights into the role of machine learning in predicting asset prices and its impact on trading strategies.

     

    Understanding the Role of Machine Learning in Financial Markets

    The Growing Importance of Machine Learning in Finance

    In recent years, machine learning has emerged as a powerful tool in the finance industry. With its ability to analyze vast amounts of data and uncover hidden patterns, machine learning algorithms have found applications in various areas, including trading, risk management, fraud detection, and portfolio optimization. Let’s take a closer look at why machine learning is gaining traction in financial markets.

     

    The Applications of Machine Learning in Trading
    1. High-frequency trading and predictive analytics

    High-frequency trading (HFT) involves executing trades at lightning-fast speeds to take advantage of market inefficiencies. Machine learning algorithms are well-suited for HFT strategies, as they can quickly analyze large volumes of data, including order book data and historical trade data, to identify patterns and make informed trading decisions.

    • Predictive analytics in trading involves using machine learning models to forecast price movements and market trends. By analyzing historical data, market indicators, and other relevant factors, these models can generate predictions that guide trading strategies.
    1. Forward price prediction and order book analysis

    Machine learning algorithms can be used to predict future asset prices based on historical data. By training models on historical price patterns and market conditions, these algorithms can identify trends and make predictions about future price movements.

    Order book analysis involves examining the supply and demand dynamics in a market by analyzing the order book data. Machine learning models can analyze order book data in real time to identify patterns, liquidity levels, and potential market imbalances, which can inform trading strategies.

    1. Price discovery for illiquid securities

    Machine learning algorithms can aid in determining the appropriate prices for illiquid securities. Traditional valuation methods may struggle to accurately price these securities due to limited trading activity and data availability. Machine learning models can leverage historical trade data and related liquid assets to estimate fair values for illiquid securities.

    1. Counterparty behaviour prediction and risk assessment

    Machine learning models can help predict counterparty behaviour and assess associated risks in financial transactions. By analyzing historical data on successful and unsuccessful trades, these models can identify patterns and indicators of counterparty reliability. This information can be valuable for risk management and decision-making processes.

    Machine learning algorithms have demonstrated their versatility and effectiveness in various trading applications. From high-frequency trading and predictive analytics to price discovery for illiquid securities and counterparty behaviour prediction, these algorithms offer powerful tools for improving trading strategies and risk management.

     

    Effectiveness of Machine Learning in Financial Market Predictions

    Short-Term vs. Long-Term Predictions

    When it comes to predicting asset prices in financial markets, the effectiveness of machine learning models can vary based on the prediction time horizon. In this section, we’ll explore the factors that influence the success of machine learning models in both short-term and long-term predictions.

     

    Short-Term Predictions: Leveraging High-Frequency Trading
    • Short prediction horizons

    Machine learning models excel in short-term predictions due to their ability to analyze real-time data and quickly adapt to changing market conditions. Short prediction horizons typically range from a few seconds to minutes, allowing these models to capture and react to market dynamics effectively.

    • Availability of real-time order book data

    Real-time order book data provides valuable information about market liquidity, supply, and demand. Machine learning models can leverage this data to identify patterns and make informed trading decisions within short timeframes.

    • Acknowledging the probabilistic nature of trading

    Machine learning models in trading recognize the probabilistic nature of predicting asset prices. Even models with modest accuracy, around 55% to 60%, can provide valuable insights and contribute to profitable trading strategies. By understanding the inherent uncertainty in financial markets, machine learning models can generate probabilistic predictions that guide trading decisions.

    Short-term predictions in financial markets heavily rely on high-frequency trading strategies and real-time data analysis. Machine learning models equipped with short prediction horizons and access to real-time order book data can exploit market inefficiencies and generate profitable trading opportunities.

     

    Long-Term Predictions: Challenges and Limitations

    Rapidly changing market regimes

    Long-term predictions in financial markets face challenges due to rapidly changing market regimes, behaviours, and drivers. Key model drivers that prove useful in one period may become irrelevant in the next, making it difficult to rely solely on historical data for accurate long-term predictions.

    Infrequent and sparse data.

    Long-term predictions often suffer from data scarcity, particularly in markets with infrequent trading or limited data availability. Sparse data poses challenges for machine learning models, as they require a sufficient amount of historical data to identify reliable patterns and make accurate predictions.

    Lack of predictors and historical patterns.

    Long-term predictions may encounter limitations when there is a lack of predictors or historical patterns. Some markets or assets may have limited data on past behaviour, making it challenging for machine learning models to capture the drivers and dynamics that influence long-term price movements.

    Long-term predictions in financial markets present unique challenges for machine learning models. The dynamic nature of markets, scarcity of data, and the absence of reliable predictors can limit the effectiveness of machine learning algorithms in making accurate long-term predictions.

     

    Limitations and Considerations for Machine Learning in Financial Markets

    Overcoming Limitations: The Role of Domain Knowledge and Human Expertise

    While machine learning algorithms have demonstrated their value in financial markets, it’s crucial to recognize the role of human expertise and domain knowledge. In this section, we’ll explore how human involvement can help overcome limitations and ensure the successful implementation of machine learning in finance.

     

    Strategies for Addressing Rapidly Changing Regimes and Behaviours
    • Regular model retraining and redeployment

    Machine learning models should be regularly retrained and redeployed to adapt to rapidly changing market regimes and behaviours. This ensures that the models incorporate the most recent data and reflect the current dynamics of the market.

    • Incorporating adaptive algorithms

    Adaptive algorithms allow machine learning models to adjust and learn from new patterns and market conditions. By continuously updating the model’s parameters based on real-time data, adaptive algorithms enable the models to remain effective in evolving market environments.

    • Monitoring and adjusting model inputs.

    Regularly monitoring and adjusting the inputs and features used in machine learning models is essential to ensure accurate predictions. Factors that influence market behaviour may change over time, and by monitoring and updating the model inputs, practitioners can ensure the models capture the most relevant information.

    Addressing the challenges of rapidly changing market regimes and behaviours requires a proactive approach. By retraining and redeploying models, incorporating adaptive algorithms, and carefully monitoring and adjusting model inputs, practitioners can mitigate the limitations posed by dynamic market conditions.

     

    Mitigating Data Challenges and Sparse Information
    • Data augmentation techniques

    Data augmentation techniques can enhance the quality and quantity of available data. By using techniques such as data synthesis, feature engineering, and imputation, practitioners can augment the existing data to provide more comprehensive insights for machine learning models.

    • Leveraging alternative data sources

    To overcome data scarcity, leveraging alternative data sources can be beneficial. Social media sentiment, news analytics, and other non-traditional data sources can provide valuable insights that complement the existing data and improve the accuracy of machine learning models.

    • Collaborating with industry experts

    Collaborating with industry experts and incorporating their domain knowledge can help fill data gaps and improve the effectiveness of machine learning models. Experts can provide valuable insights into market dynamics, identify relevant predictors, and guide the feature selection process.

    Data challenges, including data scarcity and limited historical patterns, can be addressed through data augmentation techniques, leveraging alternative data sources, and seeking collaboration with industry experts. These strategies expand the data available for machine learning models and enhance their effectiveness in making predictions.

     

    Conclusion:

    Machine learning continues to reshape the landscape of financial markets, offering new opportunities and challenges. By understanding its applications, effectiveness, and limitations, market participants can make informed decisions and leverage machine learning for improved trading strategies and risk management. While machine learning algorithms hold immense potential, they are most effective when combined with human expertise and domain knowledge.

    In this comprehensive guide, we’ve explored the applications of machine learning in financial markets, discussed its effectiveness in short-term and long-term predictions, and highlighted the limitations it faces. As technology continues to advance and new data sources emerge, the role of machine learning in finance will undoubtedly evolve. By staying informed and embracing a hybrid approach that combines machine learning with human intelligence, market participants can navigate the ever-changing landscape of financial markets more effectively.

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  • From Prototype to Production: Best Practices for Scaling Machine Learning Models

    From Prototype to Production: Best Practices for Scaling Machine Learning Models

    Introduction:

    Scaling machine learning (ML) models is a critical step in achieving efficient and reliable performance when working with large datasets. However, it can be a complex process that poses various challenges. In this comprehensive blog post, we will explore a range of best practices to help organizations successfully scale their ML models. By implementing these strategies, you can unlock the full potential of your ML initiatives, enabling seamless handling of vast amounts of data and optimizing model performance.

     

    1. Efficient Feature Management for Scalability

    a) Centralized Feature Store

    Efficient feature management is crucial for scaling ML models. Implementing a centralized feature store allows organizations to store, share, and reuse curated features easily. This centralized repository facilitates collaboration across teams and ensures consistent feature engineering practices.

    b) Offline and Online Feature Storage

    A robust feature store should support both offline and online feature storage. Offline features enable batch processing and offline model training, while online features provide low latency serving for real-time inferencing. By maintaining a centralized repository for both types of features, organizations can achieve versatility in their ML workflows.

    c) Historical Data Maintenance

    Retaining historical data is essential for effective feature management. Historical data enables the comparison of feature values over time and facilitates efficient model monitoring. By keeping track of changes in feature values, organizations can identify anomalies and assess model performance accurately.

     

    2. Leveraging the Right Programming Languages and Processors

    a) Choose High-Performance Languages

    When dealing with large-scale ML models, selecting high-performance programming languages like C/C++ or Java can significantly impact scalability. These languages excel in handling complex computations and large datasets, ensuring efficient processing of ML tasks.

    b) Consider GPU and TPU Acceleration

    Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs) offer substantial performance advantages over CPUs. These specialized processors excel in parallel processing and complex matrix operations, making them ideal for scaling ML models. Leveraging GPUs or TPUs can accelerate training and inference, enabling faster time-to-insights.

    c) Harness the Power of Distributed Computing

    Distributed machine learning techniques can effectively handle large data volumes and complex algorithms. Frameworks like Apache Hadoop and Apache Spark provide powerful distributed computing capabilities. By leveraging data parallelism and algorithm parallelism, organizations can distribute computational work among multiple nodes, achieving faster training and inference times.

     

    3. Distributed Machine Learning for Large Data and Complex Algorithms

    a) Apache Hadoop for Distributed Computing

    Apache Hadoop is a popular framework for processing large-scale datasets in a distributed environment. It offers a reliable and scalable infrastructure for distributed ML. By dividing data processing tasks into smaller, manageable chunks, Hadoop distributes the workload across multiple nodes, enabling efficient and parallel execution of ML tasks.

    b) Apache Spark for Parallel Processing

    Apache Spark is another widely used framework that provides efficient parallel processing capabilities. With its in-memory computing capabilities, Spark allows for faster data processing and iterative model training. Spark’s support for various ML libraries simplifies the implementation of distributed ML algorithms, facilitating scalability and performance optimization.

    c) Addressing Synchronization Challenges

    In distributed ML systems, synchronization challenges can arise when aggregating model updates from different nodes. Ensuring proper synchronization is crucial to maintain model accuracy. Organizations should carefully design synchronization mechanisms and perform thorough validation after synchronization to guarantee consistent and reliable results.

     

    4. Containerization for Consistent Deployment

    a) Managing Package Versions and Dependencies

    Managing package versions and dependencies is crucial for consistent deployment of ML models. Incompatibilities or version mismatches can lead to unexpected issues in production. By carefully managing package versions and documenting dependencies, organizations can ensure reproducibility and minimize deployment-related obstacles.

    b) Utilizing Docker Containers for Reproducibility

    Docker containers provide an effective solution for packaging ML models and their dependencies together. Containers encapsulate the entire runtime environment, including the model, dependencies, and system configurations. This approach ensures consistency across different deployment environments, eliminating inconsistencies that may arise due to varying system configurations.

    c) Performance Optimization with Docker Images

    To further enhance scalability, organizations can leverage performance-optimized Docker images specifically designed for ML workloads. Platforms like Google’s Tensorflow images provide pre-configured environments with optimized libraries and dependencies. Using such images helps optimize resource utilization and streamlines the deployment process.

     

    5. Efficient Model Retraining and Hyperparameter Tuning Strategies

    a) Automatic Model Retraining

    To maintain model effectiveness over time, implementing automatic model retraining is crucial. ML models should be continuously monitored for data drift and updated accordingly. By periodically retraining models with fresh data, organizations ensure their models stay relevant and perform optimally in dynamic environments.

    b) Continuous Learning and Data Drift Management

    Continuous learning techniques, such as online learning or incremental training, enable ML models to adapt to changing patterns in real-time. By monitoring data drift and adjusting models accordingly, organizations can ensure accurate predictions and prevent performance degradation over time.

    c) Optimizing Hyperparameter Tuning

    Hyperparameter tuning plays a vital role in optimizing ML model performance. Exhaustively searching the entire hyperparameter space can be computationally expensive. Efficient techniques like Bayesian optimization can help identify optimal hyperparameter configurations without exhaustive search, striking a balance between compute time, cost, and model quality.

     

    Conclusion:

    Scaling machine learning models is a critical step in achieving optimal performance when dealing with large datasets and complex algorithms. By adopting the best practices outlined in this comprehensive blog post, organizations can overcome challenges associated with scaling ML models and ensure efficient workflows. From efficient feature management to leveraging the right programming languages and processors, implementing distributed ML techniques, utilizing containerization for consistent deployment, and adopting efficient model retraining and hyperparameter tuning strategies, organizations can unlock the full potential of their ML initiatives. Embracing these best practices will enable organizations to scale their ML models effectively and drive impactful results in today’s data-driven world.

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  • Demystifying MLOps: The Art of Operationalizing Machine Learning

    Demystifying MLOps: The Art of Operationalizing Machine Learning

    Introduction:

    Operationalizing machine learning is a crucial step in deploying and running an ML model in a production environment. It involves transitioning from the development and training stages, which occur in controlled environments using clean data, to the deployment and management stages, where the model operates in real-world scenarios with messy data. In this blog, we will delve into the significance of operationalizing machine learning, explore the challenges associated with it, and highlight the benefits of using a machine learning platform for this purpose. Let’s dive in and uncover the secrets to unlocking the true potential of machine learning in your business.

     

    What is Operationalizing Machine Learning?

    Operationalizing machine learning is the process of deploying and integrating machine learning models into a production environment to make them fully functional and effective in real-world scenarios. It involves transitioning from the development and training stages, which occur in controlled environments using clean data, to the deployment and management stages, where the models operate with messy, real-world data. Operationalizing machine learning ensures that the models are ready to be used in practical applications, providing valuable insights, predictions, and decision-making capabilities. By operationalizing machine learning, businesses can unlock the true potential of these models and leverage their power to drive better business outcomes, improve operational efficiency, and gain a competitive advantage.

     

    Why Operationalize Machine Learning?

    Operationalizing machine learning is essential for businesses to fully leverage the value that ML models bring. It enables the seamless integration of ML models into production environments, allowing organizations to utilize these models to make informed decisions, improve operational efficiency, enhance customer experiences, and gain a competitive edge. By operationalizing ML models, businesses can transform theoretical concepts into practical solutions that drive tangible business outcomes.

     

    What are the Challenges of Operationalizing ML Models?

    The challenges of Operationalizing ML Models are as follows:

    • Different Approaches and Skill Sets:

    Data science and operations teams have distinct roles and responsibilities. Data scientists focus on developing and training ML models, while operations teams are responsible for deploying and managing them in production environments. Bridging the gap between these teams can be challenging, as they often use different tools, concepts, and tech stacks. Effective collaboration and coordination between these teams are crucial to successfully operationalize ML models.

    • Real-World Constraints and Considerations:

    ML models developed in training environments may not account for real-world constraints, such as legal, compliance, IT ops, or data architecture restrictions. These models need to be adapted to work within the operational context without compromising their effectiveness. Addressing these constraints requires close collaboration between data science, operations, and other relevant stakeholders to ensure that the deployed models align with business requirements and adhere to regulatory and compliance standards.

     

    What is the process of Operationalizing Machine Learning models?

    Operationalizing machine learning involves a series of critical tasks that transform ML models into practical solutions within the operational context. Here are the key steps:

    1. Selecting a Use Case:

    Identify a specific use case that aligns with your business objectives and requirements. Understanding the problem, you want to solve or the opportunity you want to capitalize on is crucial for effective model operationalization.

    1. Determining Prediction Probability:

    Define the acceptable range of probability for making predictions based on the ML model’s output. This helps establish confidence thresholds and decision boundaries for actionable insights.

    1. Calculating Compute Power:

    Assess the amount of compute power the model will require in the real world to ensure efficient performance. This includes considerations such as hardware infrastructure, scalability, and resource allocation.

    1. Addressing Explainability:

    ML models often deliver high accuracy but may lack explainability, making it challenging to interpret and communicate their decisions. Finding ways to enhance model explainability without sacrificing accuracy is vital for gaining stakeholder trust and compliance with regulations.

    1. Establishing a Data Pipeline:

    Set up a comprehensive data pipeline that ensures a seamless flow of data from various sources to serve the ML model. This involves data acquisition, preprocessing, transformation, and integration to create a reliable data infrastructure.

    1. Hyperparameter Configuration:

    Optimize hyperparameters to improve the performance and generalizability of the ML model. Experiment with different configurations, evaluate their impact on model performance, and select the best settings for your specific use case.

    1. Building a Model Scoring Engine:

    Develop a model scoring engine that can evaluate the performance of the operationalized model based on predefined metrics. This allows for continuous monitoring and assessment of the model’s accuracy and performance over time.

    1. Deploying the Model:

    Ensure the correct deployment of the ML model in the chosen business application context. This includes considerations such as API integration, containerization, cloud deployment, or on-premises deployment, depending on the specific requirements of the use case.

    1. Monitoring and Evaluation:

    Continuously monitor the model’s performance in the production environment. Assess how well it adapts to real-world data, detect any degradation in performance, and proactively identify opportunities for improvement.

    1. Performance Analysis and Retraining:

    Analyze the model’s results and errors to gain insights into its behavior and identify areas for enhancement. If necessary, retrain the model using new data or modified algorithms to ensure its continued accuracy and relevance.

     

    The Benefits of a Machine Learning Platform for Operationalizing ML

    • Overcoming Silos:

    A machine learning platform facilitates end-to-end machine learning pipelines, breaking down silos between data science, operations, and other departments. It enables seamless collaboration and streamlines the operationalization process.

    • Automated Data Pipelines:

    Machine learning platforms offer automated workflows for data gathering, preprocessing, feature engineering, and model training. This automation saves time and reduces manual effort, allowing teams to focus on higher-value tasks.

    • Improved Collaboration:

    By centralizing the operationalization process on a single platform, machine learning platforms foster better communication, coordination, and collaboration between teams. This enhances cross-functional collaboration, ensuring that all stakeholders are aligned and working towards a common goal.

    • Generating Business Value:

    Operationalized ML models have the potential to drive significant business value. By leveraging these models, organizations can make data-driven decisions, optimize processes, enhance customer experiences, detect anomalies, and identify valuable insights that lead to improved business outcomes.

     

    Conclusion:

    Operationalizing machine learning is a crucial step for businesses to harness the true potential of ML models. By addressing the challenges and leveraging the benefits of a machine learning platform, organizations can streamline their operationalization processes, foster collaboration, and unlock substantial business value. Embrace the power of operationalized ML models today and position your business at the forefront of data-driven innovation.

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  • Mastering Raw and Aggregated Joins in Spark Structured Streaming

    Mastering Raw and Aggregated Joins in Spark Structured Streaming

    Introduction to Streaming

    What is Stream Processing?

    Stream processing refers to a method of handling and analyzing data in real-time as it is continuously generated or received. It involves the processing of data streams in a sequential and continuous manner, rather than storing and processing data in batches or as static datasets.

    Stream processing systems are designed to handle high-volume, high-velocity data streams from various sources such as sensors, social media feeds, logs, financial transactions, and more. These systems allow for the immediate processing, transformation, and analysis of the data as it flows, enabling real-time insights and decision-making.

    Stream processing involves several key components, including data ingestion, data processing, and data output. Data is ingested from the stream source, processed using algorithms, filters, aggregations, or other operations, and then delivered to one or more destinations, such as databases, dashboards, or other applications.

    Why Streaming Processing ?

    Processing streaming data offers several advantages and opportunities compared to traditional batch processing. Here are some reasons why processing streaming data is beneficial:

    Real-time insights: Streaming data processing enables real-time or near-real-time analysis of data as it is generated. This allows organizations to gain immediate insights and make timely decisions based on the most up-to-date information.

    Rapid detection and response: Streaming data processing facilitates the quick identification of critical events or anomalies. By continuously analyzing incoming data in real-time, organizations can promptly detect and respond to issues, such as fraud, security breaches, system failures, or any other events that require immediate attention. This proactive approach can help mitigate risks and minimize the impact of potential problems.

    Improved scalability: Stream processing systems can be designed to scale horizontally, meaning they can handle increasing data volumes by distributing the workload across multiple processing units. This scalability allows organizations to accommodate growing data streams and handle high data throughput effectively.

    Enhanced situational awareness: By processing streaming data in real-time, organizations can gain a better understanding of current situations and trends. They can monitor and analyze data as it unfolds, allowing for more accurate and timely decision-making. This situational awareness is valuable in various domains, such as finance, logistics, cybersecurity, IoT, and many others.

    Personalized and real-time experiences: Streaming data processing enables organizations to deliver personalized and dynamic experiences to users in real-time. For example, in e-commerce or digital advertising, real-time analysis of user behavior and preferences can be used to deliver personalized recommendations or targeted advertisements instantly.

    Overall, processing streaming data offers the advantage of real-time insights, rapid detection and response, continuous processing, scalability, enhanced situational awareness, and the ability to provide personalized and real-time experiences. These benefits make stream processing invaluable in numerous use cases across industries.

    Streaming Use Cases:

    • Fraud detection
    • Real-time stock trades
    • Marketing, sales, and business analytics
    • Customer/user activity
    • Log Monitoring: Troubleshooting systems, servers, devices, and more
    • Ride share matching

    Real Life Example

    When a passenger calls Lyft, real-time streams of data join to create a seamless user experience. Through this data, the application pieces together real-time location tracking, traffic stats, pricing, and real-time traffic data to simultaneously match the rider with the best possible driver, calculate pricing, and estimate time to destination based on both real-time and historical data.

    Challenges in Stream Processing:

    • Scalability
    • Ordering
    • Consistency and Durability
    • Fault Tolerance & Data Guarantees

     

    Spark Structured Stream

    Structured Streaming is built on top of the Spark SQL engine, which is a distributed SQL query engine that allows you to query large datasets using SQL syntax. Structured Streaming extends the Spark SQL engine to support real-time streaming data processing by providing a unified API for batch and streaming data processing.

    Spark Structured Streaming works by dividing the data stream into micro-batches and processing each batch using Spark’s batch processing engine. This allows Structured Streaming to process data streams in near real-time without having to write complex streaming code.

    Why Spark Structured Streaming?

    • Ease of use :Developers can use SQL-like queries to process real-time data streams
    • Scalability : Spark Structured Streaming is built on top of the Spark SQL engine, which is a distributed SQL query engine that can scale to handle large datasets
    • Fault-tolerant : Spark Structured Streaming is fault-tolerant, which means that it can handle failures without losing data.
    • Integration : Spark Structured Streaming integrates with a wide range

     

    Join Raw and Aggregated Streams

    Raw Stream : Input Data Stream

    Aggregated Stream : Window Based Aggregation on Input Stream.

    Why join Raw and Aggregated Streams?

    When using Spark Structured Streaming or similar stream processing frameworks, it is common to aggregate data based on a look-back window or sliding window interval. This aggregation helps in summarizing or condensing the data to derive useful statistics or metrics. However, the aggregated data alone may lack context or details about individual transactions or records.

    By joining the raw data stream with the aggregated data stream, we can combine the summarized information with the corresponding original data. This allows us to obtain transaction or record-level results that provide a comprehensive understanding of the data.

    Therefore, the relationship between the raw and aggregated streams is crucial for obtaining transaction-level details and gaining a more comprehensive understanding of the data.

    Example Use Case:

    Let’s consider a scenario where we have a raw stream of sales transactions and an aggregated stream that provides the total sales amount per product category for a specific time window. If we only had the aggregated data, we would know the total sales amount for each category, but we wouldn’t have insights into which individual transactions contributed to those totals. By joining the raw and aggregated streams, we can determine which specific sales transactions contributed to the aggregated amounts, enabling us to analyze the performance of individual products or identify patterns in customer behavior.

    Way to join input and agregated stream in Spark Strucutred Streaming join

    Input and Aggregated stream can be joined in spark structured streaming in append mode.

    Understanding the modes :

    Spark Structured Stream provides ways to process the input stream.

    Complete : Entire result will be outputted after every trigger.

    Append : Only the new rows will be outputted after every trigger.

    Update : Only the updated result since the last trigger will be outputted.

    Shortcoming of Append Output Mode:

    Joining two stream is only available for append mode. But it has certain limitations.

    In append mode the output for a records comes only once when spark is sure that the lifetime of the record is completed and it is not going to be updated in future. This time will be sliding interval plus the watermark duration — so there will be a delay of this duration in outputting the actual data.

     

    Our Approach:

    Terminologies –

    Foreach Batch: The foreachbatch operations allow you to apply arbitrary operations and writing logic on the output of a streaming query. This allows you to define a function that is executed on the output data of every micro-batch of a streaming query.

    Check Point: Checkpoint helps build fault-tolerant and resilient Spark applications. In Spark Structured Streaming, it maintains intermediate state on HDFS compatible file systems to recover from failures.

    Checkpoint Directory Folder Structure :

    1. Commits — It maintains batch wise commits.
    2. Offsets — The offset files contain information about the position of the last processed record in each partition of the input data source. This information is used to resume processing from the last checkpoint in case of a failure or a restart. Applicable in case of Kafka
    3. Source — The offset files contain information about the position of the last processed record in each partition of the input data source. This information is used to resume processing from the last checkpoint in case of a failure or a restart. Applicable in case of file store
    4. State — The state files contain the state of the streaming job, such as the state of the aggregations or the state of the window operations.
    5. Metadata — The metadata files contain information about the data sources, schema, and other metadata related to the streaming job.

    Architecture Diagram:

    Mastering Raw and Aggregated Joins in Spark Structured Streaming

    Flow:

    After Aggregating the values, we call the foreach batch function with output mode as update, Inside the foreach batch function we do the following things:

    1. Get the batch number from the foreach batch function. When we call the foreach batch function by default it passes two arguments — Batch Dataframeand Batch Number.
    2. Based on the batch number we will get the list of files that needs to be read again. From the checkpoint folder we can the list of files read in the current batch either from source or offset folder depending on the data source.
      Sample File :
      {“path”:”s3a://simulated-simbox/simulated-simbox/part-13037.parquet”,”timestamp”:1670486999000,”batchId”:665}{“path”:”s3a://simulated-simbox/simulated-simbox/part-13038.parquet”,”timestamp”:1670487000000,”batchId”:665}
    3. Parse the file list and then read the data from the source as a batch dataframe inside the foreach batch function.
    4. Apply the transformations again that was applied on streaming data before the aggregations.
    5. Join the raw data frame with the aggregated streaming data frame and drop the duplicates.
    6. Apply transformations on the joined data frame and write it to sink.

    Using Hypersense AI to do stream operations:

    HyperSense AI, a powerful AI platform, offers advanced functionality specifically designed for processing streaming data and joining streams, revolutionizing data-driven decision-making. With its comprehensive set of features, Hypersense AI enables businesses to effortlessly incorporate real-time insights into their decision-making processes. By seamlessly integrating Hypersense AI into the flow of processing streaming data, organizations can efficiently aggregate, transform, and analyze data streams in real-time. Moreover, Hypersense AI provides robust capabilities for joining streams, allowing businesses to combine multiple data sources, perform complex join operations, and extract valuable insights from the unified view of the data. This seamless integration empowers organizations to gain immediate insights, make timely decisions, and uncover actionable intelligence from streaming data. With Hypersense AI’s powerful streaming data processing and joining capabilities, businesses can unlock new opportunities and gain a competitive advantage in the dynamic landscape of data-driven decision-making.

    About Hypersense AI:

    HyperSense AI is a cutting-edge AI Orchestration platform from Subex that provides the following benefits to our customers

    • Orchestrate E2E machine learning model building, deployment and ML Ops, at least 3X faster than traditional methods
    • No Code platform requiring no knowledge of any programming languages to build ML models
    • Fully automated Auto ML capability for even business users to do simple AI/prediction tasks, thereby democratising AI adoption within enterprises
    • Explainability, What-if analysis, counterfactual analysis to ensure model transparency and remove bias ensuring that you can operationalize AI models with full confidence
    • Can be deployed on any Hyperscaler cloud platform, or on-prem. Built using distributed computing architecture and micro services for high scalability and support for huge volumes of batch and streaming data processing and analytics.
    • Subex professional services with deep telco knowledge to solve complex problems using AI/ML

    Derive maximum ROI for your business from AI

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  • inwi extends partnership with Subex for AI-based Fraud Management and Business Assurance on HyperSense

    inwi extends partnership with Subex for AI-based Fraud Management and Business Assurance on HyperSense

    22nd June 2023, BENGALURU, INDIA – Subex, a telecom analytics solution provider, has been awarded a five-year contract with inwi, a major telecom operator in Morocco, to transform their existing Revenue Assurance and Fraud Management (RAFM) system to HyperSense Business Assurance and Fraud Management, including Managed Services support.

    inwi aims to lead in innovation, customer experience, and digitization of services. In 2019, inwi achieved a noteworthy milestone by becoming the first telecom operator in Africa to launch a 100% digital brand “win by inwi”. To meet new demands and ensure customer satisfaction, the company was searching for a solution that could be scaled to accommodate new services and business lines while minimizing potential risks. Based on the success of their longstanding partnership with Subex spanning over a decade, inwi was confident in HyperSense’s AI/ML capabilities to augment their existing rule-based approach. This integration would provide robust protection against new types of telecom risks and enable inwi to achieve greater growth and profitability.

    Through the deployment of the HyperSense platform, inwi will benefit from a more advanced and sophisticated system that has greater automation and best-in-class AI/ML capabilities. These capabilities will enable inwi to identify and prevent revenue leakages and fraud more efficiently.

    The upgrade to Business Assurance and Fraud Management on HyperSense will bring several business benefits for inwi, including:

    • Boost business agility and innovation to create new use cases in an autonomous manner, using HyperSense’s DIY capabilities.
    • Maximize cost-efficiency and improve financial performance with optimized OPEX and CAPEX budget, reduced footprint, and best-in-class TCO through rolling upgrades.
    • Accelerate time-to-revenue and gain competitive advantage with an agile deployment approach allowing for quick benefit realization and faster market penetration.
    • Leverage AI to amplify risk detection and enable faster mean time to detect and resolve false positives.
    • Future proof against next-gen telecom risks with advent of services in 5G, digital services and complex partner ecosystems.
    • Make quick data-driven decisions with advanced visualization and dashboarding capabilities

    “We are delighted to continue our partnership with inwi, one of the fastest-growing digital services providers in Morocco. We are very excited to support them consolidate their position as a leader in innovation and customer experience with our HyperSense Business Assurance and Fraud Management solution. This solution will give inwi access to advanced AI/ML technology that can help them detect and prevent revenue leaks and fraud more effectively. With this upgrade, inwi will be better equipped to meet customer demands, provide an excellent customer experience, and improve their financial performance. This partnership demonstrates our expertise, competence, and strong customer relationships, and we are eager to collaborate with inwi to address their challenges and support their growth.” – Shankar Roddam, Chief Operating Officer, Subex.

    “inwi is committed to providing the best-in-class customer experience while protecting our revenues and combating fraud. We have been working with Subex for more than a decade and have witnessed their commitment to delivering quality services. We are now looking forward to unlock the full potential of HyperSense, and leveraging it to achieve our growth objectives.” said Mr. Mohammed Temsamani, Head of Revenue Assurance and Fraud Management, inwi.

    About Subex

    Subex is a pioneer in enabling Digital Trust for businesses across the globe.

    Founded in 1994, Subex helps its customers maximize their revenues and profitability. With a legacy of having served the market through world-class solutions for business optimization and analytics, Subex is now leading the way by enabling all-round Digital Trust in the business ecosystems of its customers. Focusing on risk mitigation, security, predictability, and intelligence, Subex helps businesses embrace disruptive changes and succeed with confidence in creating a secure digital world for their customers.

    Through HyperSense AI, Subex empowers communications service providers and enterprise customers to make faster, better decisions by leveraging Artificial Intelligence (AI) analytics across the data value chain. The solution allows users without coding knowledge to easily aggregate data from disparate sources, turn data into insights by building, interpreting and tuning AI models, and effortlessly share their findings across the organization, all on a no-code platform.

    Subex has more than 300 installations across 90+ countries.

    Press Contact:
    Rakshit Raviprakash
    rakshit.raviprakash@subex.com

  • How CSPs can monitor and monetise flash calls

    How CSPs can monitor and monetise flash calls

    As seen in Vanilla Plus

    When accessing personal information or conducting an online transaction, a One Time Password (OTP) is typically all that’s required to verify your identity. Although OTPs provide an extra layer of security, issues do arise.

    Bad actors are using phishing scams to gain access to OTPs at an increasing rate. And, with OTP interception services available on the dark web, even novice fraudsters are successfully circumventing the added security. In addition to serious security concerns, SMS OTP two-factor authentication (2FA) process falls short in other areas, like customer experience. For instance, once a user enters their phone number, they must wait for the code to arrive. Sometimes it never arrives. These issues, along with the rising costs for businesses to send 2FA SMS messages, have accelerated the need for an alternative authentication method.

    Will OTP vanish in a flash?

    For businesses and communications service providers (CSPs), ‘flash call’ verification is rapidly becoming the preferred alternative to OTP 2FA. Juniper Research defines flash calling as” an authentication process that leverages mobile networks to authenticate users or actions. “

    Juniper Research predicts that the number of flash calls used for authentication will increase from 60 million in 2021 to 5 billion in 2022. A longer view forecast, also by Juniper Research, estimates that flash calls will reach an incredible 128 billion calls by 2026, which equates to a compound annual growth rate (CAGR) of 128%.

    This unprecedented growth can, in part, be attributed to its processing advantages. Basically, the system places a call to a number provided by the user. The user’s profile is then verified by authenticating the last few digits (usually 4 – 6 digits) of their phone number. While in most cases, the user isn’t required to do anything, sometimes flash calling requires the user’s interaction, such as selecting an icon or entering a password or passcode.

    From the perspective of a business, flash calling is swift, cost-effective, provides additional security, and has fast application programming interface (API) integration. From a user’s perspective, it delivers a nearly seamless customer experience. However, with the exponential adoption of flash calling, CSPs need to look at the monetisation opportunities it can provide.

    How will flash calling impact CSPs?

    This year alone, it is expected that authentication-based messaging will generate $39 billion in revenue for mobile operators. However, as an increasing number of companies begin to migrate authentication traffic to voice, flash calls have the potential to wreak havoc on operators’ SMS revenues.

    Another foreseeable headwind that service providers need to prepare for is increased competition from Over the Top (OTT) messaging app companies that provide alternative mobile network operator (MNO) messaging services. Historically, CSPs have lagged OTT players when it comes to implementing new services. On the other hand, OTT players have made their mark by being quick to identify market trends and pivoting in quickly to cater to changing consumer demands. For instance, OTT player WhatsApp has already announced its flash calling intentions and is working on integrating it within their applications, giving them a head start in meeting market demand.

    OTT players entering the flash market also face challenges. There have been reports of users losing access to their accounts, such as what happened to WhatsApp users when they shared their verification codes with someone claiming to be from the company.

    How can flash calls give CSPs a new revenue stream?

    Service Providers will need to move quickly to begin monetising flash calls. However, in most cases, they do not currently have the technology needed, which results in their inability to monetise the service. For operators to capture this revenue stream, they will need to implement cutting-edge technology that will enable them to detect and validate flash traffic, as well as detect and protect against suspicious behavior.

    For service providers to protect their Application to Person (A2P) revenue stream, solutions such as real-time fraud management systems, complex machine learning (ML) algorithms, and pattern mining tools will be essential. End-to-end fraud management solutions that provide 360-degree protection will also be essential. These solutions typically leverage artificial intelligence (AI) at every step of the process to effectively combat fraud and security risks. When capabilities such as statistical analysis, feature algorithms, auto ML, and data preparation are included, operators gain superior fraud protection over their OTT competitors.

    Data pattern mining tools are another crucial component needed to detect evolving fraud patterns. This is made possible with enhanced rule modeling capabilities that can be configured to incorporate threshold, geographic, pattern, hotlist, spam detection, intrusion detection, and smart pattern rules.

    From a revenue perspective, Juniper Research recommends that operators look at mirroring the business models of established A2P SMS market leaders. Their monetisation models almost exclusively rely on charging on a per-traffic basis. Adopting this model will enable operators to increase flash calling adoption, as well as revenues. While monetising flash calls comes with its challenges, the potential financial benefits far outweigh those challenges. But to overcome the head start of OTT players, service providers will need to move quickly.

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

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