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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?

    On-demand Webinar: AI Master Class for Fraud Management

    Watch Now

  • 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.

    Click here to book your session now

  • 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.

    Schedule a meeting!

  • 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

    Request Demo

  • 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.

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  • The AI-First Approach to Fraud Detection and Prevention

    The AI-First Approach to Fraud Detection and Prevention

    As seen in Pipeline Publication

    Fraud is a persistent nuisance for communications service providers (CSPs) across the globe, and unfortunately it just continues to grow in intensity.

    The increasing use of sophisticated technologies by the fraudsters has resulted in a surge in the frequency as well as the intensity of telecom frauds. CSPs’ digital transformation and the ever-increasing pervasiveness of the digital economy have only made it worse. The growing 5G ecosystem now is likely to make CSPs’ ongoing battle with fraud even more important.

    According to Communications Fraud Control Association’s (CFCA) Fraud Loss Survey Report, 2021, the total telecom revenue loss due to fraud is estimated to be 2.22 percent of total revenue, or $39.89 billion. What is more worrying is that there has been a 28 percent increase in fraud losses when compared to 2019. Apart from revenue loss, fraud also results in loss of reputation and potentially even subscribers for CSPs.

    The traditional rule-based fraud management systems are not enough to address the growing sophistication, frequency, and ferocity of telecom frauds, thus making it imperative for CSPs to rethink their fraud management measures and strategy.

    Fraudsters’ tactics have evolved over the years as the traffic on the communications networks continues to grow. The obsolete risk management systems are hardly geared to prevent or detect fraud. As a result, it has become more challenging for CSPs to detect fraud even as losses continue to mount for telcos in all geographies.

    The growing sophistication of the fraudsters means that CSPs, who continue to use older and traditional fraud management systems, are on the back foot and are always trying to catch up with the modern methods used by the fraudsters.

    CSPs can no longer depend on traditional fraud management systems because fraudsters are using innovative technologies to conduct fraud. In a way, fraudsters are taking advantage of the gaps in the weak and redundant traditional fraud management system to conduct frauds blatantly.

    A key issue CSPs face is that the traditional rule-based fraud management systems are reactive in nature. This can be particularly problematic considering that 5G will significantly expand the number of protocols, applications, systems, and endpoints, leading to enhanced risks. It’s imperative that with faster cloud adoption and a growing 5G ecosystem, fraud management needs to transform to be preventative and proactive.

    Increasing technology and process complexity 

    One of the key reasons CSPs continue to struggle to catch up with the growing crimes is the increasing network complexity because of new technologies and the ever-increasing number of services they offer. Telecom service providers are no longer offering just vanilla voice and data connectivity. Mobile money and other fintech products, educational products, and gaming are just some of the new services provided by the telecom service providers. Furthermore, several service providers have started offering 5G-enabled augmented reality (AR), virtual reality (VR), and Industry 4.0 use cases. All this leads to a massive change in how telco business and operations are managed.

    The operations are further complicated by the emergence of new business models, such as partner-led business models, API-led business models, compute-led business models, subscription-led business models and use-case-led business models, making traditional fraud systems deficient. The new use cases and business models will require integration with different types of platforms in which they are not just monitoring or evaluating data but also need to respond in real time.

    In the 5G era, CSPs will need to collate and manage data in real time from several sources to proactively mitigate risks, thus making it critical to revamp their fraud management strategy. In addition, 5G will enable a greater number of deployed devices, which unfortunately provide a larger attack surface for fraudsters using Internet of Things (IoT) devices. According to CFCA Fraud Loss Survey Report 2021, 32 percent of CSPs expect to see an  increase in 5G fraud because of protocols and the number of connected devices. The manual processes are not designed to manage the growing volume, variety, and velocity of data likely to be generated in the 5G era. According to OpenSignal, 5G users consume 2.7 times more data than 4G users.

    As the services offered by telcos grow, the CSP partner ecosystem also records an increase. The growing ecosystem of the Internet of Things (IoT) especially poses a challenge for CSPs, as it brings to the fore different types of devices, partners, and service providers on one platform, thereby providing newer avenues for fraudsters to access the system. One weak link is enough for fraudsters to carry out a massive scam, significantly impacting CSPs’ profitability.

    Growing skill challenge

    Faster adoption of the cloud and the growing 5G ecosystem mean that the risk management teams require new capabilities to deal with risks posed by new technologies and use cases that cut across several products and services.

    CSPs’ risk management teams need to be agile to continuously handle changes in the business. The teams should also have the ability to perform different data operations such as statistical analysis, behavioral analysis, protocol analysis, predictive analysis, and so on to capture risk-based insights from the data.

    There is a greater need to upskill and increase data literacy within CSPs’ risk management teams. This is crucial to gain the ability to deal with newer forms of data sets to address the evolving type of fraud—and also to ensure the successful implementation of risk mechanisms.

    A key challenge faced by risk management teams, however, is the skills shortage. According to the Risk & Assurance Group 2021 Digital Trust Survey, finding skilled people is one of the critical challenges faced by CSPs in addressing growing fraud. This is likely to further grow with the 5G ecosystem, leading to an increase in the demand for these skills.

    CSPs can partly address these challenges by using AI-powered automation. It is critical for the risk management teams to quickly scale up, enhance coverage and bring down the dependency on manual labor. This will also increase operational efficiency while freeing up resources for more strategic work.

    Addressing new-age fraud with an AI-first approach

    Amid growing fraud losses and the emergence of new challenges, CSPs stand to benefit by leveraging the capabilities of artificial intelligence (AI) and machine learning (ML) based systems to gain the required efficiencies to address new-age fraud. AI and ML hold tremendous potential for CSPs to not only bring down fraud-related losses but also to enhance the trust of their subscribers in their infrastructure.

    An AI-powered fraud management system comes with capabilities to quickly identify and respond to suspicious activity. It can combine data, both structured and unstructured, from several data streams and make sense of it in real time to help telcos efficiently stop fraud before it negatively impacts their revenue and reputation.

    In addition, AI and ML will be key proponents for handling changes in business scenarios and enabling risk management teams to be more agile. Another key advantage of an AI-based system is that it can collate both structured and unstructured data from multiple sources such as Kafka, pub-sub, APIs, and more, leading to faster detection of fraud and minimizing fraud run time.

    Also, CSPs can further leverage AI and ML capabilities to make informed decisions if it is explainable, meaning that it provides complete clarity and visibility on how decisions are taken. Therefore, a fraud management system based on Explainable AI eliminates AI bias completely and provides transparency on how decisions are made.

    Despite the potential, however, adoption of AI/ML-based fraud systems continues to be low. As per the CFCA Fraud Loss Survey Report 2021, 30 percent of the respondents are still using manual processes, while 28 percent use rules-based fraud management systems, and only 13 percent are leveraging AI and ML-based fraud management systems. The increasing sophistication of the frauds committed underlines that the methods being used by CSPs are not sufficient. Therefore, CSPs must adopt an AI-based approach to bring down fraud-related revenue loss.

    The growing fraud losses of telcos coupled with the increasing level of sophistication of fraudsters means that CSPs must reexamine their fraud management strategy. It is time to adopt AI-first fraud management systems that use the latest technologies for both prevention and quick detection of fraud, thereby taking a more proactive—rather than reactive—approach to risk mitigation.

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  • Build an efficient input data pipeline for deep learning

    Problem

    Developing the input pipeline in a machine learning project is usually time-consuming and uncomfortable, and it might take longer than building the model itself. When dealing with massive datasets including thousands/millions of files, the input data pipeline can either be a game changer or a bottleneck depending on the architecture.

    When datasets are too large to fit in RAM, a python generator-based technique can represent a significant barrier in training complicated GPU compute-intensive models. GPUs that are idle while waiting for data significantly slow down model training.

    This is when TensorFlow TFRecord comes in handy.

    We will learn how to utilise TensorFlow’s Dataset modules tf.data and tf.record to create efficient pipelines for photos and text in this lesson.

    TFRecord

    To record, data is stored as a binary record in a succession of protocol buffers. TFRecords files are incredibly read-efficient and take up little hard drive space. TFRecord is a method for storing data samples in sequential order. Each sample contains a number of characteristics.

    We specify our features as a dictionary within the ‘write record’ method. In this scenario, we have picture data, the location of the item on the image, and the colour of the object, and we also want to keep the image data’s form.

    Certain datatypes can be specified for a feature in TFRecord. For our features, we use a byte list and a float list.

    Now we enter our real data into the features: we begin with data in digits, which is quite handy. We flatten them (‘.ravel()’) and provide them to the appropriate feature constructor. You might be wondering why we store picture data as floats. This is a design choice (oops! — see on for the consequences of this design choice), so we already save the picture data with colour values in the 0=val=1 range, so we can feed this immediately to the training. You’ll see that there are a few of spots suited for data conversions – assuming you stored it as uINT8 here, you can later convert it in the feeding pipeline.

    The last thing we need is a means to shut down the writer and ensure that everything is written to disc. We introduced a close writer function to do this (as an aside, you might modify this to operate in combination with the Python ‘with’ statement).

    That’s all. We will return to this point later: we presently do not write validation data separately from training data. One may expect a simple’split dataset’ method that we can use afterwards, however there is none with Datasets. This is acceptable given that tf.data is designed to operate with billions of records, and splitting billions of information in a certain way is difficult.

    To record eliminates the need to read each sample file from the disc for each epoch.

    The effectiveness of utilising TFRecord comes at a cost, in the form of the need to write a lot of complicated code to produce and read the records files.

    We provide a solution to this problem by using Datum to handle TFRecords files.

    Datum

    Datum is a TensorFlow-based framework for creating an efficient, quick input pipeline. Datum is intended to create/manage TFRecord databases nearly entirely without the use of complicated programming.

    Datum is intended to read and publish TFRecord datasets while also constructing a fast input pipeline that can be used for single GPU or distributed training with only a few lines of code.

    Datum constructs a fast input pipeline using tf.data and tf.record.

    TFData

    The Dataset API enables you to create an asynchronous, highly efficient data pipeline to prevent data exhaustion on your GPU. It reads data from disc (pictures or text), performs optimal transformations, generates batches, and sends them to the GPU. Previously, data pipelines forced the GPU to wait for the CPU to load the data, resulting in performance concerns.

    Installation

    Datum may be installed with the command pypi

    pip instal datum.

    TFRecord export

    If you don’t use datum, writing/exporting data to tfrecord format might get quite complicated.

    Datum facilitates the export of datasets to tfrecord format. Datum has a few preset issue kinds that allow you to generate a dataset with a few lines of code without having to go into the inner workings of tfrecord and serialisation.

    Import TFRWriteConfigs to define datum configuration for writing/exporting data to tfrecord

    from datum.configs import TFRWriteConfigs

    Define the splits information in the configs, splits names are important for a datum to automatically identify the splits data.

    write_configs = TFRWriteConfigs()
    write_configs.splits = {
    "train": {
    "num_examples": <num of train examples in the dataset>
    },
    "val": {
    "num_examples": <num of validation examples in the dataset>
    },
    }

    To transform the datasets, import the export API and problem type.

    Different datasets serve different functions. An image classification dataset with merely a class label, for example, cannot be utilised for picture segmentation or detection. To facilitate conversion, datum distinguishes between issue categories for classification, detection, and segmentation tasks.

    from datum.export.export import export_to_tfrecord
    from datum.problem.types import IMAGE_CLF

    Suppose we want to build a tfrecord dataset for an image classification task, the type for that is IMAGE_CLF

    Convert the dataset to tfrecord format

    export_to_tfrecord(input_path, output_path, types.IMAGE_CLF, write_configs)

    Datum will transform and store the result dataset. In the output directory, there are tfrecord files and dataset metadata files. The exported tfrecord files may be imported as tf.data with ease. Datum load API was used to load the dataset.

    Load the data as tf.data.Dataset.

    Import the loading API as tf.data to load the tfrecord dataset.

    Import dataset from datum.reader

    To load the dataset, simply give the output path from the previous export state.

    dataset = load(<path to tfreord files folder>)
    train_dataset = dataset.train_fn('train', shuffle=True)
    val_dataset = dataset.val_fn('val', shuffle=False)

    Examples/cases in the dataset can be augmented before feeding into the model. It’s easy to preprocess and post-process samples in the dataset using pre_batching_callbackand post_batching_callback .

    pre_batching_callback : Using this callback example can be processed before batching.

    post_batching_callback: Using this callback example can be processed after batching. Examples are processed as a batch.

    Suppose we want to augment the dataset, which can be achieved using the following pre_batching_callback

    def augment_image(example):
    image = tf.image.resize(example["image"], IMG_SIZE)
    image = tf.image.random_flip_left_right(image)
    image = tf.image.random_flip_up_down(image)
    example.update({"image": image})
    return exampledataset_configs = dataset.dataset_configs
    datset_configs.pre_batching_callback = lambda example: augment_image(example)
    train_dataset = dataset.train_fn('train', shuffle=True)

    Put Datum to the test.

    The usage of datum for transfer learning an EfficientNet-B0 model for an image classification project is demonstrated in this notebook.

    Play with the Transfer Learning with Datum notebook to speed up your input flow.

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  • The Challenges Faced by Telecom Companies in FAR Reconciliation

    The Challenges Faced by Telecom Companies in FAR Reconciliation

    The telecommunications world is constantly changing, and CSPs, are continuously adapting to meet the increasing demands of the market. In this ever-evolving landscape, having effective processes for reconciling fixed assets, known as the Fixed Asset Registry (FAR), is crucial for CSPs to manage their assets accurately, ensure correct billing, and collect revenue.

    The Importance of FAR Reconciliation

    FAR reconciliation ensures that the records of fixed assets accurately represent the physical assets deployed in a telecom operator’s network. In today’s business environment, where achieving a positive return on investment (ROI) and maintaining financial integrity are crucial, inadequate FAR reconciliation processes can lead to revenue loss, with an average annual loss of 2-5%. Since a significant amount of capital expenditure is dedicated to network assets, it is critical for CSPs to manage their assets accurately to achieve ROI.

    The Challenges Faced by CSPs in FAR Reconciliation

    CSPs encounter several challenges when it comes to FAR reconciliation. Let’s take a closer look at these challenges:

    1. Complex and Diverse Asset Ecosystem: CSPs must reconcile and integrate diverse assets from different vendors, operating on various technologies, and requiring specialized management approaches. Managing this complexity and ensuring accurate records is a challenge for CSPs.

    2. Rapid Asset Deployment and Scalability: The telecom industry demands fast asset deployment and scalability to meet the growing requirements of data-intensive applications and increased connectivity. CSPs need to reconcile assets quickly and accurately to optimize their utilization. However, the speed of deployment and asset expansion can create bottlenecks, resulting in delays, errors, and incomplete records.

    3. Real-time Asset Visibility and Control: CSPs require real-time visibility and control over their assets. However, there is often a gap between the financial teams and the network teams when it comes to tracking assets. Factors like depreciation and taxation are not always considered in asset tracking and reconciliation processes, leading to discrepancies between the asset registry and financial records.

    4. Data Complexity and Volume: The telecom industry generates vast amounts of data from various sources, such as network elements, provisioning systems, billing systems, and operational support systems. Managing and reconciling this complex and voluminous data is a challenge for CSPs. They need advanced data management techniques to process, validate, and derive actionable insights from this data.

    5. Lack of Standardization: The absence of standardized FAR reconciliation processes, data formats, and industry-wide practices poses a challenge for CSPs. Inconsistencies in reconciliation approaches across CSPs and ecosystem partners hinder interoperability, data exchange, and collaboration. CSPs require a standardized framework to streamline reconciliation processes and facilitate information sharing among stakeholders.

    The Five Key Principles of FAR Reconciliation

    To overcome these challenges and achieve successful FAR reconciliation, CSPs must adopt five crucial capabilities. These capabilities empower CSPs to enhance their enterprise asset management processes, improve financial performance, and establish a strong foundation for the efficient operation of their networks.

    To learn more about these five principles and how they can empower CSPs in the modern era, you can download our white paper titled “The Five Key Principles of FAR Reconciliation: Empowering Success in the Modern Era.” This white paper provides valuable insights, strategies, and best practices for CSPs to optimize their FAR reconciliation processes and thrive in the ever-changing telecom landscape.

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  • The Future is Now: Why Your Business Needs to Embrace (and Implement) AI Adoption

    Today AI’s impact and potential value are undebatable. Many businesses have already evaluated and experimented with AI in various forms. To the extent that applications of AI are actively investigated in healthcare, recruitment, law, and government. However, the adoption rate varies across industries and companies, but AI is becoming increasingly prevalent in the business world. However, AI boost and bust cycles are seen over time, which makes potential adopters wary. On one side, there are greater success stories of AI implementation, especially within the technology and healthcare domain. There are other traditional businesses where AI adoption is much slower than expected.

    Technology adoption is an ongoing process of operational use of technology, according to Neumann, Guirguis & Steiner. I prefer this definition because it clearly defines adoption as not being achieved at a single point in time. It is also emphasized for the operational success of AI, especially in the business context of AI adoption. Any technology adoption depends on not just technological advancement but also external factors such as social, economic, and environmental influences. Technological advancement, for instance, is the ease of use, efficiency, value drivers, etc.; external factors are social, economic, and environmental factors. All these factors are unequivocally applicable to AI today, evidently enforcing their effects on us today, creating an environment that demands either adoption or failure.

    Given the potential impact and value of AI, enterprise AI practitioners and managers need to approach adoption in a thoughtful and strategic manner. This may involve carefully evaluating business needs, identifying appropriate use cases, and considering the ethical and social implications of AI adoption.

    By focusing on operational success and understanding the broader contextual factors influencing AI adoption, businesses can navigate the potential challenges and maximize the benefits of this transformative technology.

    There are several frameworks and tools that exist to support it. There are a few popular frameworks to which one can refer.

    1. Gartner’s AI Maturity Model: This framework provides a structured approach for businesses to assess their current AI capabilities and chart a path for AI adoption at the organizational level.
    2. Microsoft’s AI Business School: This framework is designed to help businesses understand AI concepts and provide guidance for AI adoption through a series of case studies, videos, and tutorials.
    3. IBM’s AI Ladder: This framework helps businesses to understand the key stages of AI adoption, starting with defining the AI strategy and ending with scaling AI across the organization.
    4. Deloitte’s AI in a Box: This framework provides a modular approach to AI adoption, allowing businesses to choose the components that best suit their needs.
    5. McKinsey’s AI Enablement Framework: This framework helps businesses to develop a strategy for AI adoption, define use cases, build the necessary infrastructure, and scale AI across the organization.

    The main challenge with AI adoption lies in its execution, as it has remained theoretical and difficult to implement in practical situations. The primary issue is the inability to simplify complex concepts to a level that can be effectively executed.

    In most AI adoption models or frameworks, best practices for organizational readiness are suggested for three key areas: people, process, and technology. It is important to note that technology readiness also includes data readiness. These three areas are often referred to as the “golden triangle” of AI adoption.

    Why Your Business Needs to Embrace (and Implement) AI Adoption

    The golden triangle of people, process, and technology is widely recognized as a useful framework for analyzing socio-technical systems. Each component of the triangle – such as the development of people’s skills, the importance of processes, and the fit of technology to business needs – is equally critical to the success of AI adoption. These components are intertwined and can impact each other in numerous ways. For instance, in the AI technology context, the technology selected by a project team may influence the nature of AI tasks and, consequently, the processes to which these tasks contribute, which in turn can impact the people involved, including project stakeholders. Understanding the interplay between these three components is essential for successful AI adoption.

    The data is an extension of technology in this golden triangle. The importance of data, its availability, quality, and governance are pre-requisite for the concept of a data-driven culture in an organization. The successful adoption of AI requires data to be first understood, available, and managed. The importance of data can be better illustrated by separating Data and Technology in the golden triangle as below.

    Why Your Business Needs to Embrace (and Implement) AI Adoption

    The people who drive processes can decide the relevance of data. Data also determines the output of AI technology and drives the direction of AI development in an organization. To reflect all this importance of data, let us extend the triangle to a square, including data at the apex.

     

    Stages of AI Maturity

    AI Adoption within an organization depends on mechanisms that vary based on the current level of AI maturity. There are three stages of AI project maturity such as AI Experimentation, AI Adoption, and AI Production.

    • AI Experimentation:

    An early stage of AI, where use cases need to be identified by business stakeholders, the success criteria need to be defined for each use case and overall. Then, a strategy to execute these use cases in terms of people, process, and technology needs to be evaluated.

    • AI Adoption:

    A transitioning stage from research to deployment. This is the hardest phase to deploy an AI model in an operational setting. There could be several operational challenges to overcome at this stage, such as ensuring operational data is readily available, quality controls are in place, etc. Many organizations also need to gain all the required skills to operationalize AI models.

    • AI Production:

    This is a production phase where proven AI models are integrated with production applications.

    Let us understand how interaction of these PPDT model influence AI project maturity.

    Why Your Business Needs to Embrace (and Implement) AI Adoption

    At the experimentation stage, it is required to match the technology to the kinds of data available to address the business problem that stands out (model testing and selection). During the adoption phase, the link to people is emphasized, as the data which captures expert judgment of the quality of AI outputs is needed to prove the technology’s value. Once the technology is mature, the link between data and processes must be built into the organization’s MIS infrastructure to maintain the supply of operational data.

    Conclusion:

    New age AI platforms have significantly reduced the challenges to AI adoption in an enterprise allowing them to be confident about adopting AI in an organization based on their AI maturity. Subex’s cutting edge platform (Such as HyperSense AI) enables enterprise customers to make faster, better decisions by leveraging AI across the data value chain. The platform 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.

    8 Keys to AI Success in an Enterprise

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