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What is AI Solutions?

Artificial Intelligence (AI) is a comprehensive classification that includes cutting-edge concepts such as deep learning. In general, AI solutions are all about adapting aspects of intelligence to machines and having them execute tasks that can be natural and straightforward to humans, but extremely complex to program. Moreover, an AI program can perform such tasks autonomously and efficiently.

AI solutions can be sorted into two sections: general and narrow. General AI (also called powerful AI) is what data scientists seek to extend in the future. It will be developed as a solution to broadly-defined problems in an intelligent method thanks to sophisticated simulated abilities and general learning abilities of its environment. Today that might seem like a science-fiction strategy – but it is becoming a reality sooner than later.

AI-based software and tools are already transforming the world as we know it in the shape of narrow AI, which concentrates on achieving particular tasks with incredible precision, often better than humans. For example, Image tagging software based on narrow AI can be used for tagging images on web platforms. And in the retail enterprise, AI-powered robotic technologies are being used to provide customer assistant services.

What type of data is most suitable for AI solutions, and how do you use them?

Developers who intend to design AI solutions need to train algorithms on a data set of diverse entries– for example, a cluster of images, text, or specific information like discrete transaction data or data from product interaction by the users.

You can however buy structured data, take advantage of public crowdsourcing enterprises (such as the Amazon Mechanical Turk), or – when handling potentially sensitive data – hire personal groups of data science professionals capable of helping you out with data collection, identification, and labelling services.

The dataset employed to train needs to include a good number of both positive and negative examples to help algorithms learn to differentiate them. For instance, if we want our model to identify traffic signals in pictures, we need to expose the model to pictures with signals and without them.

The developer or data scientist may test with different algorithms before sticking with the one with the best fit for the training data. We also require a test set – a dataset used to experiment with a model generated based on the training samples for evaluation, analysis, and improvement.

What is the future of AI and Machine Learning?

Earlier this year Gartner stated, “Artificial Intelligence and Machine Learning have arrived at a crucial tipping point and will from now on continue to augment and expand to every tech-enabled product, service, thing, or application.” They also expect that by 2025, AI will be amongst the top five asset preferences for a minimum of 30% of Chief Information Officers.

Consumers are now getting increasingly used to the usefulness of digital assistants, self-driving cars, robots working in factories, and smart cities. AI has created its impact on most industry sectors and persists to continue spreading to new industries.

Experts anticipate that Machine Learning will grow at an exponential rate. Some people believe that it will inevitably be joining the cloud-based service – so-called Machine Learning-as-a-Service (MLaaS).

Ultimately, Machine Learning will equip our machines to make better sense of data; both in its context and meaning. And these effective and actionable insights will drive AI-based solutions and make them a necessity for data-driven decision making and analysis among executives and project managers of the future.

What are the types of AI solutions?

It is fundamental for companies to look through the lens of business capabilities rather than technologies when it comes to AI. Speaking in terms of use cases, AI can be employed readily in three important business environments: automating business processes, gaining insight through data analysis, and engaging with customers and service professionals.

Process automation

The most common type of AI implementation comes in the form of automation of digital and physical tasks—commonly back-office administrative and financial activities—using robotic process automation technologies. RPA is considerably more sophisticated than earlier business-oriented automation tools because the intelligence aspect of these “robots” function comparably to a human being entering and consuming information from several IT systems. Tasks include:

Reporting and storing data from e-mail and call centre systems into systems of records
Reaching into multiple systems to revise records and manage customer communications
Moderating failures to demand services across billing systems
“Updating” legal and contractual records using natural language processing.

RPA is the least pricey and most effortless to implement of the cognitive technologies in AI, and typically obtains a fast and high return on investment. It is especially well-fitting to operate across multiple back-end systems.

Cognitive insight

This type of project uses algorithms to recognise patterns in massive volumes of data and analyse them to derive cognitive sense. Consider them to be “analytics on steroids.” These machine-learning programs are being used to:

predict what a particular customer behaviour on seller sites
manage credit fraud in real-time and find live insurance fraud
analyze warranty info to determine safety or quality concerns
automate tailored targeting of digital ads
provide intelligent insurance platforms with more-accurate and detailed modelling.

Cognitive insights gained by machine learning programs differ from those available from traditional analytics: They are usually much more data-rich and complex, the models are usually trained on a data set, and the models get more efficient and precise—that is, their capacity to use unexplored data to make predictions or place things into categories enhances over time.

Cognitive engagement

Projects that deal with employees and customers use natural language processing chatbots, intelligent agents, and machine learning as their primary means of AI integration. This category includes:

intelligent representatives that offer 24/7 customer service addressing a wide and expanding array of issues — in the customer’s natural language
internal web pages for answering employee queries on topics including IT, employee benefits, and HR policy
product and service request systems for retailers that strategize to increase personalization, engagement, and sales
health guidance systems help providers create customized care programs

As businesses become more acquainted with cognitive tools, they are experimenting with aspects of AI that combine elements from all three categories to reap the full benefits of AI solutions deployment. A system with such deep-learning technology could engage its employees and customers alike using AI to list and suggest frequently asked questions and answers, previously resolved cases, and documentation to help find solutions to both employees and customer problems.

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Telecommunications, or telecom, is a critical component of our modern lives. It’s a global, interconnected system that facilitates not only communication but also social interaction, business transactions, and more. But with increasing sophistication comes increasing risk – the risk of telecommunications fraud. In this blog post, we delve into the top 10 telecom frauds, provide an in-depth analysis of the list of telecom frauds and telecom fraud prevention, and explore how countries like China and the UK are tackling fraud in the telecom industry.

Telecom Frauds Overview

Before we dive into the top 10 telecom frauds, let’s first understand what telecom fraud entails. Telecom fraud can vary widely, from complex network infiltrations to straightforward scams. Two prevalent forms of fraud currently plaguing the industry are:

IRSF Fraud in Telecom

International Revenue Share Fraud (IRSF) is a pervasive type of fraud in the telecom sector that causes significant financial losses across the globe. This deceptive act involves fraudsters, often part of complex networks, making extensive long-distance calls to high-tariff countries.

The basis of this fraud is the complex international rates set by telecom operators. For some countries, these rates can be exceedingly high due to various factors such as lack of infrastructure or political instability. The fraudsters target these specific countries and establish arrangements with local operators to share the revenue generated from these high-tariff calls.

This fraud works in a quite strategic manner. Fraudsters generate traffic, typically by hijacking or spoofing phone lines, then make numerous calls to high-cost numbers that they control. By doing so, they inflate the phone bill of the unsuspecting telecom operator or individual whose line they’ve hijacked.

Consequently, the telecom operator is liable to pay for the fraudulent calls based on international telecom regulations. The revenue share, obtained from these calls, is then divided between the fraudsters and the local operators who are part of the scheme. Unfortunately, the unsuspecting telecom operator, who has no clue about this arrangement, suffers significant financial losses.

Bypass Fraud Telecom

Bypass fraud, also known as interconnect bypass fraud, is another rampant issue in the telecommunications sector. In essence, this type of fraud occurs when international calls are rerouted via illegal or unlicensed channels, skillfully bypassing the official network gateways that levy call termination charges.

Telecom operators usually have official agreements to handle calls originating from foreign networks. These agreements include termination fees, which are charges imposed for handling and terminating calls on their network. Fraudsters, however, find innovative ways to sidestep these charges.

In bypass fraud, the trick is to make international calls appear as local calls, thereby evading the termination fees associated with international calls. Fraudsters employ illegal SIM boxes, devices filled with several prepaid SIM cards, to convert international calls into local calls. This unlawful practice effectively bypasses the official network pathways and dodges the termination fees.

As a result, telecom operators suffer from revenue loss due to the unpaid termination fees. Moreover, it leads to inaccurate traffic measurements and network performance data, which can seriously impair a telecom operator’s ability to plan and manage their network effectively.

Top 10 Telecom Frauds

Now, let’s enumerate and look into the top 10 telecom frauds in 2023, which include IRSF and Bypass fraud discussed above:

International Revenue Share Fraud (IRSF)
Bypass (Interconnect Bypass) Fraud
Subscription (Identity) Fraud: This happens when fraudsters use false identity information to gain telecom services.
Premium Rate Service (PRS) Fraud: Scammers trick victims into calling premium-rate numbers, which incur high charges.
Traffic Pumping Fraud: Also known as access stimulation, fraudsters increase traffic to high-cost numbers to receive a portion of the termination fee.
Refiling Fraud: This involves changing the characteristics of a call to lower charges or disguise its origin.
False Answer Supervision Fraud: The fraudster causes calls to be charged as answered, even if they weren’t.
Stolen Equipment Fraud: Telecom equipment is stolen and used to generate illegal revenue.
Phishing: Scammers trick victims into revealing personal or financial information via calls or text messages.
Wangiri Fraud: This fraud involves missed calls from international numbers, enticing the victim to call back and incur high charges.

Spotlight on Fraud in Different Countries

To fully grasp the global impact of telecom fraud, let’s look at specific cases from China and the UK:

China Telecom Fraud

In China, telecom fraud is widespread. Fraudsters often impersonate authority figures or relatives, leading victims to disclose personal information or make financial transactions. Chinese authorities are taking proactive measures, such as regulatory reforms and public awareness campaigns, to combat this escalating problem.

British Telecom Scams

In the UK, a typical scam involves fraudsters posing as British Telecom (BT) employees. They often claim to have detected issues with the victim’s internet connection or threaten to cut off services unless a payment is made. British Telecom has issued warnings and guidance to its customers to help them recognize and avoid these scams.

Preventing Telecom Fraud

Prevention is our most potent weapon against telecom fraud. Here are some vital strategies for telecom fraud prevention:

Secure Network Infrastructure: Telecom providers should ensure their networks are secure and frequently updated to prevent fraudsters from exploiting vulnerabilities.
Fraud Management Systems: Using advanced fraud management systems can detect unusual activity in real-time and halt fraudulent transactions.
Educate Customers: Regularly informing customers about common scams and how to avoid them can help shield them from fraud.
Collaboration: By collaborating, telecom providers can share information about new fraud techniques and work together to devise preventive measures.

Wrapping Up

The world of telecom is in constant flux, and with these changes come new types of fraud. By understanding these various types of fraud and implementing effective prevention strategies, we can work together to create a safer telecom environment for everyone. Stay vigilant, stay educated.

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AI-First Approach for Combating Telecom frauds are a serious concern for businesses and customers alike. From Wangiri and SIM Box to CLI Spoofing and IRSF, there are various types of telecom frauds that can lead to significant financial losses and reputational damage. Fortunately, an AI-first approach can help businesses combat telecom frauds with greater efficiency, accuracy, and speed. Let’s take a closer look at some of the most common types of telecom frauds and how AI can help combat them:

Wangiri Fraud: Wangiri fraud is a type of fraud in which fraudsters use automated dialing technology to call random numbers and then disconnect the call after one ring. The objective of this fraud is to encourage the call recipient to call back, typically to an expensive premium rate number. With AI-powered analytics, businesses can quickly detect and block calls from known Wangiri numbers, reducing the risk of fraudulent activities.

SIM Box Fraud: SIM Box fraud is a type of fraud in which fraudsters use a device called a SIM Box to bypass international calling rates and avoid paying fees. The device routes international calls through local phone numbers to avoid detection. With AI-powered algorithms, businesses can detect SIM Box fraud by identifying unusual traffic patterns and monitoring call metadata for anomalies.

CLI Spoofing Fraud: CLI Spoofing is a type of fraud in which fraudsters manipulate caller ID information to display a different phone number. This type of fraud is often used to trick individuals or businesses into answering calls or messages from unknown or suspicious sources. With AI-powered analysis of call patterns and metadata, businesses can quickly detect, and block spoofed calls, reducing the risk of fraudulent activities.

IRSF Fraud: IRSF fraud is a type of fraud in which fraudsters exploit international revenue sharing agreements to generate revenue by placing fraudulent calls. With AI-powered algorithms, businesses can quickly detect and block calls from fraudulent numbers, reducing the risk of fraudulent activities.

Data Fraud: Data frauds are becoming increasingly common in the telecom industry, with fraudsters using a variety of methods to steal data or personal information. With AI-powered analytics, businesses can identify unusual data patterns and quickly detect potential data breaches, reducing the risk of fraudulent activities.

Subscription Fraud: Subscription fraud is a type of fraud in which fraudsters use stolen or fake identities to subscribe to telecom services. With AI-powered analysis of customer behavior and account data, businesses can quickly detect and block suspicious activities, reducing the risk of fraudulent subscriptions.

AI-powered fraud detection and prevention tools can also help businesses proactively identify and prevent frauds before they occur. By using real-time data analysis, machine learning, and advanced algorithms, businesses can detect and block suspicious activities in real-time, reducing the risk of financial losses and reputational damage. In addition to detecting and preventing frauds, AI-powered investigations can also help businesses quickly and efficiently investigate and resolve fraud incidents. By automating investigative processes and using advanced analytics tools, businesses can reduce investigation times, streamline processes, and maximize the ROI of fraud management efforts. In conclusion, an AI-first approach to telecom fraud management is essential for businesses looking to combat frauds with greater efficiency, accuracy, and speed. With the right tools and technologies, businesses can proactively identify and prevent frauds, while also efficiently investigating and resolving incidents. By embracing an AI-first strategy, businesses can protect their customers, their finances, and their reputation in today’s increasingly complex telecom landscape.

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

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Introduction:

The telecom industry is a highly competitive market, which makes it difficult for operators to differentiate themselves from their competitors. The rising level of complexity in this competitive space requires an advanced toolset that can help to identify and prevent new types of risks to safeguard the revenues and their customers.

Telecom analytics for fraud management and consumer protection

Fraud is a growing problem. According to the World Economic Forum, it’s estimated that the cost of fraud globally is $3 trillion annually. This figure doesn’t include any data breaches or other types of cybercrime; rather, it only accounts for telecommunications fraud alone.

Although the traditional approaches exist, they fall short in many parameters. An AI-based fraud management system is better than traditional approaches for several reasons. First and foremost, AI-based systems can process vast amounts of data and identify patterns and anomalies in real time, which is beyond the capabilities of human analysts.

AI algorithms can also learn from past data and adapt to new fraud schemes, whereas traditional approaches are typically rule-based and cannot keep up with the constantly evolving nature of fraud. Additionally, AI-based systems can provide more accurate and consistent results compared to traditional approaches, which rely on human judgment and can be influenced by biases or errors.

Furthermore, AI-based fraud management systems can reduce false positives, which is a common issue in traditional approaches, by analyzing multiple data sources and using advanced techniques such as machine learning and deep learning. This, in turn, can improve the overall customer experience by minimizing unnecessary inconvenience caused by false alarms.

Finally, AI-based systems can help organizations detect fraud earlier and more efficiently, which can result in significant cost savings and protect the company’s reputation. Overall, AI-based fraud management systems offer a more efficient, accurate, and adaptable solution compared to traditional approaches.

In a nutshell, here are some of the advantages of using AI for telecom fraud detection and prevention:

Real-time detection: AI-based systems can analyze large amounts of data in real time, enabling operators to detect and prevent fraud as soon as it occurs. This helps to minimize revenue loss and protect customers from fraud.
Increased accuracy: AI algorithms can analyze patterns in data and identify anomalies that may indicate fraud. These algorithms can learn from historical data to improve their detection accuracy over time, resulting in fewer false positives and false negatives.
Detection of new and evolving fraud types: AI systems can detect new and evolving types of fraud that traditional rule-based systems may miss. This helps operators stay ahead of fraudsters who are constantly developing new techniques to evade detection.
Multi-layered analysis: AI systems can analyze multiple layers of network data, including signaling traffic, voice, and data traffic, and user behavior. This allows operators to identify complex fraud schemes that involve multiple types of traffic and behavior.
Scalability: AI-based systems can handle large volumes of data, making them well-suited for the high-volume, high-velocity telecom environment.
Cost savings: AI-based fraud detection and prevention systems can help operators reduce fraud losses, which can result in significant cost savings.

Overall, the use of AI for telecom fraud detection and prevention can improve detection accuracy, reduce fraud losses, and enhance the customer experience.

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[vc_row][vc_column][vc_column_text]Revenue Assurance can be defined as the use of data quality and process improvement techniques to boost profitability, revenues, and cash flows through the proper collection of revenue for all delivered services. The majority of Revenue Assurance methods include rectifying data before it enters the invoicing system and identifying and repairing leakage areas in the network and customer-facing systems.

It is now more important than ever to achieve revenue excellence in the fiercely competitive business climate of today. To achieve revenue excellence, Revenue Assurance, a crucial element of revenue management, must be in place. We’ll give an overview of Revenue Assurance in this guide, go through its significance and challenges, and offer practical advice on how to use Revenue Assurance solutions to their full potential.

What is Revenue Assurance?

Revenue Assurance is the process of making sure the business issues accurate invoices in accordance with the contracts it has with its clients. If a contract’s terms are complicated, billing mistakes may result in clients being undercharged. Even minor mistakes can accumulate over time and result in “revenue leakage,” the loss of money.

Revenue Assurance may assist in preventing revenue leakage and maximising both cash flow and revenue by routinely looking for anomalies between contract wording and billing history. Manual inspections by a company’s sales or account management teams, recurring audits by specialised Revenue Assurance teams, or automated procedures carried out by AI software platforms to detect errors are all examples of Revenue Assurance.

Why is Revenue Assurance important?

Robust Revenue Assurance procedures ensure that clients receive correct invoices in accordance with their contracts, preventing money leakage. Moreover, it guarantees that any errors are rapidly found. A consumer is more inclined to pay the difference rather than challenge the price if the error is discovered quickly.

When problems do arise, being able to resolve them quickly increases the possibility of recovering money leakage, enhances customer satisfaction, and safeguards the reputation of your business. Although billing errors are never ideal, it’s better for your customers’ experiences and brand reputation to catch them early than to find yourself in the unpleasant position of telling them that they owe a lot of money due to persistent, unrelated billing issues. Using Revenue Assurance solutions enables them to be more proactive and attentive with customers and makes it simple to get answers to inquiries about contracts and items bought, leading to a better customer experience.

A few significant areas where Revenue Assurance strategies may add the most value are listed below.

Ensures Accurate Records

Revenue Assurance ensures that anomalies are swiftly discovered and handled, helps organisations maintain accurate records of all their financial activities, and helps prevent errors in reporting financial data.

Also, it offers a thorough understanding of how much profit is produced by each division or sector, which may assist firms in making better choices regarding their next investments.

Avoids Losses

Revenue Assurance can assist in locating possible loss sources, such as fraud or money-laundering schemes. Businesses may have greater control over their funds and decrease the likelihood of theft or customer or staff misuse by monitoring all incoming and outgoing payments.

Improved Efficiency

By automating various operations including invoicing, billing, and collection procedures, Revenue Assurance methods help firms manage their finances more effectively.

Staff members may concentrate on other activities because this saves them time and effort. Automatic recordkeeping systems also lessen human error, making it simpler to identify mistakes and respond appropriately when needed.

Boosts Profits

Businesses may greatly increase their bottom line by simplifying revenue procedures and assuring accuracy in all financial transactions. In order to maximise revenues, it also aids businesses in optimising their pricing methods.

Adherence to regulations

Revenue Assurance not only guards against possible losses brought on by mistakes or fraud, but it also aids businesses in staying in compliance with all relevant laws and rules.

Entities can avoid costly fines for noncompliance while appropriately accounting for revenue by maintaining up-to-date with the constantly changing laws and regulations relating to revenue collection

Which companies need Revenue Assurance departments?

Every business that bills a lot of clients based on intricate contracts might profit from having a specialised Revenue Assurance department. Companies are especially vulnerable to revenue leakage if the amount they bill consumers fluctuates from month to month or if their contracts contain terms like minimum spend requirements, discount structures, break clauses, or price hikes over the contract period.

Business-to-business contracts typically require the most Revenue Assurance methods since they are negotiated separately and businesses may have hundreds of distinct contracts, each with a particular set of provisions. Contracts from worldwide companies may be written in several different languages, which makes it considerably harder for teams to keep track of. The property industry often benefits the most from a specialised Revenue Assurance department for companies handling commercial leases or multi-use complexes with a number of various contract forms. Even companies with more straightforward contract conditions, however, could still profit from routine audits to make sure that invoices are being issued properly.

Revenue Assurance in the Telecom Industry

Although a variety of businesses might benefit from Revenue Assurance, the telecom industry finds it to be particularly advantageous. The environment of what it means to be a telecommunications operator is quickly changing as a result of the proliferation of connected devices. Innovation and cutting-edge technology are influencing consumer behaviour and will continue to fuel demand for new services. The billing scenario becomes more complex due to new relationships with partners who can offer such services as well as new business models and related revenue streams.

Complex pricing schemes are created by these new services, including revenue sharing, sponsored and zero-rated services, elastic pricing, loyalty points, prorated services, and others. In the typical telecom revenue chain, there are several intricately linked technologies, systems, goods, and procedures.

What are the challenges of Revenue Assurance?

Revenue Assurance may be a time-consuming manual procedure that requires teams to go through contracts clause-by-clause and compare them to billing data in order to find invoicing errors made by hand. This is made more difficult by the fact that Revenue Assurance is a time-sensitive operation; if errors aren’t discovered right away, contract clauses may prevent the re-issuing of un-invoiced charges, requiring businesses to write off the lost money.

This laborious procedure is frequently made more challenging by a company’s lack of strong version control or a single repository for all of its contracts. The process of discovering income leakage can be made more difficult by the fact that long-standing clients may go through numerous iterations of contracts with specifics altering at renewal periods. When things expand and get more intricate, there is a considerable risk of income leakage.

Revenue Assurance in the telecom industry entails a number of controls and procedures that work together to guarantee that all supplied goods and services are invoiced for and paid for in line with the contractual agreement with users. Audit and control management, Revenue Assurance process reviews, customised dashboards, and reporting are all parts of the Revenue Assurance role. Annual losses may be between 2% and 4% of revenue, depending on the environment for Revenue Assurance of the operator.

What is the process of Revenue Assurance?

A system-based approach to financial management known as “Revenue Assurance” aims to make sure that all income from transactions and goods is accurately recorded, billed, and collected in accordance with the company’s regulations.

Techniques for Revenue Assurance may be used by businesses in a variety of company operations, including budgeting, buying, billing, accounts receivable management, and customer service.

In order for the process to be effective, it must be carried out regularly in order to take note of any modifications to the company’s business practises or processes that could have an effect on the company’s revenue sources or cash flow.

Moreover, it must entail the analysis of data from several sources, including transaction logs, billing systems, and client information. To find any problems or hazards related to income leakage, this data should then be compared to predicted outcomes.

Corrective actions can be performed when possible issues have been detected through this study, such as changing billing policies or adding more controls over incoming money.

Also, companies must consistently check their internal procedures to make sure they adhere to all applicable laws, rules, and industry standards.

They must also think about the data that must be logged for effective audit trails and the safeguards that will best guarantee correct reporting of transactions.

Overall, Revenue Assurance offers a thorough system-level perspective of how money moves across divisions of a business while taking into account current internal controls and external limitations.

It enables businesses to better understand which sectors are most vulnerable to leakage and what proactive measures they can take to safeguard their income over the long term.

How can you unlock value with Revenue Assurance solutions?

The core issue with business and Revenue Assurance is an oversight. You can take action to stop income leakages if you can pinpoint where and how they happen. It’s a straightforward idea, but in today’s data-rich environment, comprehension is hard.

Operating in a data-rich telecommunications environment, communication service providers (CSPs) face an especially challenging situation. Such data offers enormous opportunities to improve corporate operations and procedures, but it also generates a tremendous amount of data that has to be recorded and comprehended.

How has Artificial Intelligence (AI) changed Revenue Assurance?

By analysing contract papers and converting them into structured data, artificial intelligence (AI) technology may speed up and simplify the Revenue Assurance process. Software for AI-based Revenue Assurance maintains a single repository for all contract information. It then reads contract clauses in a number of different forms using sophisticated algorithms trained on hundreds of thousands of contracts in 25 languages, extracting clauses into a uniform format, making it much simpler for your team to examine.

Checking contracts against billing data takes much less time when you have a single source of consistently-structured data to deal with. It also makes it much simpler for your Revenue Assurance staff to spot problems. There is a complete audit trail and complete transparency thanks to the structured data fields that instantly link back to the original contract papers. The Revenue Assurance team will be able to focus on discrepancies rather than having to go through each contract thanks to the possibility to automate the data comparison with the billing data.

Single source of truth for Revenue Assurance teams globally

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[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][vc_toggle title=”What are the components of Revenue Assurance?”]The components of Revenue Assurance are Individual source trends, multi-source reconciliations, profile and service assurance, balance movement checks, invoice reconciliation, setup support, and billing assurance.[/vc_toggle][vc_toggle title=”What does a Revenue Assurance officer do?”]The Revenue Assurance officer will work as a member of the Revenue Assurance team and be in charge of implementing established procedures to audit internal controls throughout the entire revenue cycle in order to ensure that revenue risks and any potential revenue leakage are promptly identified and addressed.[/vc_toggle][vc_toggle title=”How do you ensure Revenue Assurance?”]To ensure Revenue Assurance for your business, it is essential to pinpoint the areas that can yield profit or cost savings. These five methods can aid in achieving enhanced Revenue Assurance:

Streamlined Processes.
Adaptability.
Adherence to Standards.
Robust Security.
Upgrading Outdated Systems.

[/vc_toggle][vc_toggle title=”What is the role of Revenue Assurance in telecom?”]Revenue Assurance refers to the ability of a telecom business to bill for goods and services in accordance with the commercial contract it has with the client while preventing revenue leakage. It guarantees the correctness and integrity of all systems involved in rating, billing, and invoicing.[/vc_toggle][vc_toggle title=”What is the scope of Revenue Assurance?”]Revenue Assurance may include any and all areas that management designates as those that fall under their purview. Depending on the known or undiscovered revenue leakage points that Revenue Assurance solutions may assist identify, some businesses define Revenue Assurance’s scope to encompass a very small range of areas, while others define it to include a vast range of areas.[/vc_toggle][vc_toggle title=”What is revenue leakage?”]The loss of money from your company, which sometimes goes unreported, is referred to as revenue leakage. Although it is possible to stop revenue leaks, many companies fail to implement the necessary safeguards since they frequently go undiscovered.[/vc_toggle][/vc_column][/vc_row]

We are thrilled to announce that Subex has once again been mentioned in the Gartner report, Market Guide for AI Offerings in CSP Customer and Business Operations*, published on February 20, 2023. This is the second consecutive time that Subex has been recognized in this report, and it highlights our continued commitment to providing advanced AI solutions to our customers.

Subex mentioned as a representative vendor in Market Guide for AI Offerings in CSP Customer and Business Operations

Market Guide Overview

Gartner’s Market Guide for AI Offerings in CSP Customer and Business Operations provides an overview of the commercial off-the-shelf (COTS) offerings available for communications service providers (CSPs) to enhance their customer and business operations using artificial intelligence (AI). The report, published on February 20, 2023, is authored by analysts Pulkit Pandey, Amresh Nandan, and Peter Liu.

Subex Mentioned in the Report

The Gartner Market Guide for AI Offerings in CSP Customer and Business Operations covers Subex’s key offerings for AI in CSP customer and business operations, including HyperSense Business Assurance, HyperSense Fraud Management, and HyperSense Partner Ecosystem Management. Subex also provides the HyperSense AI Platform, allowing customers to create their own AI solutions. Along these lines, the report highlights Subex’s capabilities in areas such as revenue assurance, fraud management, partner management, and more. The report also showcases our product strategy to include providing plug-and-play, preconfigured, and customizable solutions and self-service controls to build custom use cases within the application.

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About the Market Guide

Market Definition and Description

Gartner defines AI in CSP customer and business operations as COTS offerings with capabilities to utilize customer and business operation data, and develop and evolve algorithms. AI offerings in CSP customer and business operations also enable CSPs to focus on their customer channels and customer journey, sales and marketing operations, and revenue and fraud management. The report classifies the AI market for the CSP customer and business operations into three categories: customer interaction operations, marketing and sales operations, and fraud management and revenue assurance operations.

Findings on CSPs and AI Adoption

The report identifies the top business priority for CSPs exploring AI adoption as using AI for customer experience (CX) enhancement. However, according to the 2022 Gartner Data and Analytics for Digital Transformation Survey, CSPs are struggling to meet their CX goals today. The top three KPIs that CSPs are tracking to measure their performance are customer retention, CX, and cost optimization. Challenges faced by CSPs when adopting and implementing AI include data source integration, skills-related challenges, leadership and organization related challenges, and value generation.

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*Published 20 February 2023. Report by Pulkit Pandey, Amresh Nandan, Peter Liu

Simplifying operations by working towards the right tools and technologies is a fundamental priority for all business owners. Choosing the right asset maintenance software is a critical aspect of this endeavor: a robust asset management system can help you maximize the life of assets through preventive care, improve asset utilization and reduce costs.

No matter the asset types, monitoring asset health and maintenance is vital to improving productivity. However, companies today have an overwhelming variety of asset maintenance options to choose from.

Choosing the right asset management process & tool for your business can be exhausting, especially if you are unaware of what to look for. In this blog, we’ll run you through some important points you should consider before purchasing asset management software.

Know your business requirements regarding user-level and technical requirements. Business requirements include the volume of assets there are in your business, their efficiency and performance, and why do you want to track assets. In selecting asset management software, it is implied that you look for innovative usability improvements that will streamline the adoption of the software in the company and offer enhanced decision and managerial support in presenting which maintenance tasks are most urgent at a given time. This improves the decision-making process significantly.
The perspective of user-level requirements involves knowing minute detail and task that is contained in your workflow process. Technical prerequisites include how you plan to host the software, and what security and backup procedures you want. Also, by incorporating both your technical requirements and user-level requirements you can achieve more than the expected benefits. Analyze the standing and experience of developing the asset software of the vendor. A well-known asset tracking and management software provider is more likely to be able to offer continuing support.
Go for a Detailed Demo: A good asset management system does not just sort and track assets, it puts them in context. Metadata is additional details you provide for each asset. It usually contains keywords, dates, copyright, product line, approval status, contract status, usage and any other information useful to your organization. Make sure that your asset management solution provides automated metadata management, you can book a demo or a trial to check the capabilities of the tool to organize files accurately and save you from large-scale data entry projects. An in-depth demo session usually includes people who are familiar with the software and know the overall process portals, helping you understand the features that could correspond to your business needs. You could also look for ways in which the software would help to improve the existing workflow process. Plus, understanding the risks associated with the implementation of the software is also a crucial step.
Reporting and analytics: Concentrating all your assets in one location is not just about efficiency. It also provides you with a better summary of your asset library. Robust reporting and analytics offer insights into how your assets are being used. If you are obtaining a lot more return out of certain assets, you might want to create more like them. You can track if valuable assets are being underutilized or spot other patterns to refine your asset management strategy.
Choose the One Which is Easy to Use: You should find a fine balance between ease of use and the requirement of expertise needed. Decreasing the need for training and the application of easy-to-use software would save costs in operation. Your software provider should also readily provide the required training for your staff. They should also provide the support and quick interpretation of problems and solutions needed once the software is implemented.

Asset management software provides a holistic view of an asset’s lifecycle—from its procurement and implementation through its renewal and disposal. While a business could use a simple spreadsheet to track its assets, using Asset Lifecycle Management (ALM) enables it to analyze data specific to each asset, enabling it to make more informed decisions about managing its assets.

To understand more about Subex’s Network Asset Management solution

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As networks become more and more complex, intertwined with user experiences, ever-connected devices, and critical applications—each demanding their share of the network resources—the power to dynamically adapt is crucial for maintaining and enhancing the quality of service. Using network analytics, providers can predict and take action before service disruptions occur and penalties start piling up. This kind of intelligence—anticipating a disturbance in a function—not only elevates the quality of service, an important metric by itself, but it also helps gain higher net promoter scores, which in return helps providers understand their customers’ loyalty.

Some standard advantages of network analytics are :

Optimized usage of network resources: Analytics can assist you to understand your network, and balance and utilize resources in the best way possible so you can optimize network performance and lower cost structures.
New revenue streams: Analytics plays an integral role in mining insights that can open up new revenue streams and build data-driven business possibilities for fast action.
Accelerated time to market: Analytics can support making capacity planning easier for new services. As a result, the resources needed for a new or expanded service can be estimated and then provisioned well in advance of going live, ensuring products get to market on time.

Most companies seek to get the maximum worth out of their network resources while avoiding overbuilding, overspending or overextending their network environments. An acceptable balance and the proper tools are needed to get value out of these resources.

Having a full picture of the network environment is essential when extracting the most value from resources. Let’s see how network analytics solutions help extract valuable insights from network resources.

Precise metrics and complete access to the entire network are key to making the most of existing network resources. Often business networks ignore or forget underused components until they break. This approach can pose risks for the network in worst-case scenarios. These underused elements may also be forgotten until they are scheduled for an upgrade because of age or some other incidental variable. Until that moment, these resources will be wasted and may continue to be wasted or under-utilized, even after an upgrade.

A network analytics solution can provide complete and continuous visibility of how all the components and resources are working, ensuring businesses are consistently using the most cost-effective use of all networking resources. This also includes devices, bandwidth, and even cloud services. Assuring these previously under-utilized resources are being properly integrated and used by the system, lowers the risk to both infrastructure and business while simultaneously creating efficiencies and cost savings.

Network analytics solutions also provide precise data logs along with intelligent analysis of the full network. This forces greater accuracy when managing the network, along with a comprehensive view of network conditions on an ongoing basis.

These analytical data help optimise the process of gathering data from a variety of sources, including network devices and distributed agents. The data is also sorted and organised through various methods like network polls and flow/packet telemetry, ensuring all components and resources throughout the network are accounted for, monitored, and interpreted.

By applying automated and intelligent analysis by artificial intelligence to all data, as well as each component and resource throughout the network, businesses can benefit from several key functions. These include:

Trend analysis
Anomaly detection
Advanced correlation
Threshold management

The data and insights collected may be utilized to compel increased accuracy for managing the entire network while also enhancing the end-user experience. This positively affects not only internal users but also customers and external partners.

One of the primary advantages of network management and analytics solution is that it depends on data-driven thresholds instead of arbitrary measurements to allow businesses to balance network integrity and investment. This is a significant aspect for any business striving to extract maximum value from its network without building out, overextending and spending more money on network components and resources.

A requirement-based approach to managing network resources can also be applied sufficiently to all systems and services across the network, including public and private infrastructure. Too often traditional network performance management solutions offer independent data from arbitrary resources and components throughout the network. This data may or may not be purposeful for identifying trends, anomalies and threats. It is unhelpful when it comes to the utilization of network resources.

A new-generation network analytics solution provides this visibility, analysis, and insights about all network performance data. The level of visibility of operational facilities and capital savings can help process and produce productivity improvements and increased end-user satisfaction. The outcome is a streamlined, efficient network that uses all resources and components.

Telecom and Datacenter network managers are applying network analytics to optimize their infrastructure. Administrators are better equipped – with deep understandings and meaningful insights – to plan capacity and dimension for their complex networks.

Network Analytics can help transform many industry verticals. It can play a crucial role in extending network efficiency and lowering operational expenditure. It encloses a wide range of use cases in capacity planning and optimization, service assurance, capex utilization and many more.

To know more about Subex’s suite of Network Analytics solutions

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“La Guerra del mañana” (‘The Tomorrow War’) de Chris Pratt fue un éxito de taquilla. Apasionante, aterrador y tremendamente redentor al final. Sin spoilers, pero una cosa que me llamó la atención fue la gran cantidad de amenazas alienígenas que constituían la principal amenaza de la película. ¡Un solo monstruo alienígena que genera tantos ‘Whitespikes’ letales, cada uno de los cuales amenaza la supervivencia de la especie humana!

Dramático y fantástico: exactamente como me gustan mis películas. Pero de alguna manera recordó un escenario actual muy real: la batalla en la actualidad contra una amenaza inminente en el mundo de las telecomunicaciones: la suplantación de identidad de llamadas (CLI). Claro, es muy divertido ver videos de transeúntes que reciben llamadas de amigos que fingen ser estrellas de cine o incluso estrellas de cine que fingen ser amigos (¡mira la divertida broma de Bourne de Matt Damon)! Pero a mayor escala, la falsificación de identificadores de llamadas es responsable de generar tantos tipos paralelos de fraude, cada uno igualmente amenazante y capaz de causar mucho daño, amenazando la supervivencia de los actores de las telecomunicaciones.

Arañando la superficie de CLI Spoofing

La falsificación de identificadores de llamadas es una práctica mediante la cual los operadores de voz y los carriers falsifican intencionalmente la información del identificador de llamadas para obtener una ventaja ilícita. También genera diferentes tipos de fraude, como Wangiri (llamadas cortas o falsas llamadas perdidas, generadas para dejar una notificación en la pantalla de para que les devuelvan la llamada), llamadas fraudulentas (personas que afirman ser de una empresa confiable para obtener información personal o financiera) e incluso llamadas automáticas (donde los estafadores usan un marcador automático que puede transmitir millones de llamadas en cuestión de horas).

Hay más: suplantación de identidad OBC (OBC Spoofing), VM Brute Force, Bombardeo de llamadas (call Boombing), fraude de bypass. Todos estos son derivados de la suplantación de identidad de llamadas (ID Spoofing). No es una amenaza tan inocente, después de todo.

Para obtener más información sobre las diferentes facetas de CLI Spoofing, lea esto.

La escala del problema

Sin embargo, a diferencia de la ciencia ficción, CLI Spoofing es un gran problema. Los proveedores de servicios de comunicación han estado luchando con la suplantación de CLI durante años. En la mayoría de los casos, los clientes que son víctimas de este tipo de ataques manifiestan una insatisfacción extrema con sus proveedores de telecomunicaciones. Los estudios muestran que los clientes han perdido millones de dólares a través de llamadas engañosas. De hecho, la suplantación de identidad representó una pérdida de $2900 millones, según la Encuesta de pérdidas por fraude de CFCA de 2021.

Este es un gran revés para los CSP debido a las implicaciones de costos. Los principales proveedores de servicios de comunicación informan una disminución constante del 20% al 30% por año en las tasas de captura de llamadas. Las llamadas no contestadas impactan directamente en los ingresos y márgenes de los servicios de comunicación de voz y mensajería nacionales e internacionales. La desconfianza de los consumidores por las ofertas de los proveedores de servicios tradicionales los obliga a cambiar a otras alternativas.

Una puntada a tiempo ahorra nueve

Un informe de 2020 de i3 Forum titulado ‘Caller ID Spoofing’ explica por qué el simple uso de los estándares de la industria es insuficiente para combatir la amenaza CLI. El desafío es alcanzar una masa crítica, lo suficientemente significativa como para causar una mella en el problema. Similar a cómo, en la película, simplemente enviar soldados para luchar en la “The Tomorrow War” resultó inútil hasta que encontraron una manera de llegar a la raíz del problema y derribar el ataque inminente.

En el caso de CLI Spoofing, la raíz es Real-time Signaling Security.

El hecho es que nada supera a la prevención como la medida más eficaz para frustrar los ataques (vea la película, sabrá a lo que me refiero). Esto requiere capacidades en tiempo real basadas en probabilidades, investigación y análisis integral de las firmas de fraude.

En última instancia, un enfoque en tiempo real que utiliza una solución que identifica firmas de llamadas, ejecuta algoritmos de ML, proporciona inteligencia de amenazas y bloquea de forma proactiva las llamadas fraudulentas; por lo tanto, estar alerta e inteligente es la necesidad del momento.

Esto me lleva a la historia de GO Malta y su enfoque pionero para luchar contra la suplantación de CLI.

“Habíamos observado un aumento significativo en los casos de suplantación de CLI y manipulación del número ‘A’. Tenía que manejarse de manera rápida y efectiva debido al impacto negativo en el cliente”.

Signaling Security de Subex

Los clientes de GO Malta estaban preocupados por los casos recurrentes de CLI Spoofing que también generaban grandes pérdidas de ingresos para el operador. Los estafadores se estaban volviendo cada vez más inteligentes y usaban herramientas sofisticadas para evitar ser detectados y persistir en sus ataques.

La solución de Signaling Security de Subex ayudó a transformar el enfoque anterior y pasar de un enfoque reactivo a uno proactivo que inmediatamente proporcionó inteligencia de amenazas en tiempo real, un enfoque basado en la prevención y una toma de decisiones más rápida.

En un mes, los resultados eran visibles.

Subex ayudó a GO Malta a detectar llamadas falsificadas antes del ataque, lo que les permitió tomar medidas rápidamente enviando alertas a los respectivos operadores. Ahora están asegurando sus ingresos, disfrutando de una facturación precisa, monitoreando a los socios del canal, todo gracias a mayores habilidades de detección de fraudes y un tiempo de ejecución de fraudes más corto.

“La solución nos ayudó a reducir las llamadas falsificadas, pero también usamos la herramienta para determinar si una campaña de llamadas en curso es genuina o no”.

Si tiene curiosidad acerca de si Chris Pratt y su equipo ganaron ‘The tomorrow War’, prometí no hacer spoilers. Pero, si quiere saber exactamente cómo Subex ayudó a GO Malta a ganar su batalla contra CLI Spoofing, continúe y lea el caso de estudio.

Un enfoque proactivo, que aprovecha la red para prevenir el fraude en el ecosistema digital

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