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Telecom fraud is increasing at an alarming rate globally and is one of the most significant sources of revenue erosion for every telecom operator. With bottom lines under tremendous pressure, it is now time for CSPs to invest in advanced technologies to strengthen their fraud management systems. This would enable them to stay ahead of fraudsters and prevent frauds before they occur, and reduce the impact on revenues.

Before you start looking for products available on the market, you should first take note of such features and characteristics when selecting a fraud management solution for your business. Here are a few points to be mindful of when choosing the right management system for your business

Transparent view of the expected results

Having a clear understanding of the expected outcome of your management program will help better set up systems accordingly. Employing a new fraud detection system could significantly differ from the current controls that CSP might be currently using. Once it is up, you will need to drive it towards carrying out exact functions as per your business’s needs. Hence, it would be good conduct to assess the planned strategies and the intended outcomes to check for complete compatibility with the intended fraud management system.

Latency and scalability

Considering the large amount of data CSPs have access to, it is recommended to have low latency and highly scalable models incorporated in the fraud management systems to deal with the large datasets much faster without consuming ever-growing amounts of resources like memory. Also, it is essential to be mindful that the impact of latency on the business scenario can be different for different use cases.

Automation level

It is critical to plan your fraud management strategy, whether to completely rely on a fraud management solution or to have a team of fraud analysts that will use system software to optimize their work.

The most basic configuration is to decide on the level of automation a company wants to employ in their fraud management system.

Comprehensiveness and self-learning capability

You never know what technique stealing fraudsters may use in a particular case. That’s why a fraud management system should also incorporate AI/ML techniques that will adapt to the changing business scenarios and quickly detect patterns and suspicious activity and prevent such cases instantaneously. An anti-fraud team system should be comprehensive to protect the services of telcos without any exception, should be protean to be able to handle all types of data, and highly-performing to process massive data streams. The management system should be able to self-learn from data to detect not only predefined but also new types of fraud and cyber threats.

Compliance with security standards

Another forecheck would be to see if the solutions comply with your organization’s requirements for data security. Keeping a list of your organization’s compliance requirements handy when vetting web fraud detection systems and asking each vendor on the shortlist to provide documentation that indicates the product’s compliance support is key to fulfilling precise requirement metrics.

Customer support

Make sure you’ll be able to effortlessly reach the solution provider if you run into difficulties in managing the product or have any queries.

Discussing the onboarding process and service level agreement with the vendor would help understand what kind of technical support a customer should expect from a provider: when customer care professionals are available, how to reach them and report concerns, what the usual response time is, under what circumstances the services might not be lent, and other conditions.

Research and keeping updated with the latest industry trends may also help customers use the solution to its full potential and make the right choice in choosing a fraud management vendor.

Through the use of current technologies like machine learning, you can reduce risk by quickly identifying patterns in data with minimal human input needed, all in real-time. Although manual reviews will remain necessary in some cases, automated fraud management software can drastically shorten the time spent and allow for better efficiency of your team. Furthermore, it gives you a transparent solution for your team to analyze transactions.

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

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Keeping a track of company assets is essential for every organisation.

Records of assets are mandated for regulatory compliance purposes, moreover, accurate descriptions of physical and digital assets greatly help efficient resource planning.

In the past, organizations were seen keeping manual asset management registers or excel sheets or recording their asset whereabouts. Nowadays, there are a competent variety of asset management apps available for businesses to choose from, they can save time and improve the efficiency in keeping a record of the company’s assets as well as accessing them instantly whenever required without any hassle.

Here we will elaborate on the process of Network asset management and why it’s necessary. Also, here you will discover the most intuitive asset management system available online thus far.

What Is Network Asset Management?

Network asset management refers to an all-encompassing asset maintenance software that tracks the organization’s assets. The International Association of IT Asset Managers (IAITAM) has defined IT asset management or Network asset management as “a collection of business acts that demonstrates Network assets across the business divisions within the organization.” The process of putting these network asset management tools to support strategic decision making in a company’s ecosystem is helping companies take strides in accessing their asset data from anywhere and using them more efficiently.

The objective of Network asset management software is to:

Effectively help manage the assets.
Improve visibility and accessibility of assets.
Ensure optimum utilization of assets.
Reduce IT and software costs.
Ensure compliance with regulatory requirements.

A Network asset management software ties the assets with the IT infrastructure of the organization. With a robust asset management system, management and IT professionals can review and inspect all types of assets within the organization. The data can be used to make intricate decisions about purchases and other aspects of the asset’s lifecycle.

You can view Network asset management as a combination of IT and accounting services. The asset management tools are used to record assets for accounting purposes. The information stored in the systems can be used to organise an accurate balance sheet. This can support the management in making informed business decisions.

In addition, investors can dissect the financial situation of the business more accurately.

Which is the best Asset Management Software?

You will discover many options for asset management software online; however not all tools are created equally.

To handle the volume of assets, telecom operators must ensure their asset management tool has the following features :

Asset Visibility

The fundamental issue with most asset management exertion is that operators don’t have an accurate picture of information about their assets and inventory, let alone how these assets are being processed and exploited to employ in their strategies.

If this is one of your concerns, then here are the particulars that you will need to have a complete view of :

ROI and time-to-value
Resource utilization and disposition of all assets
Asset activity history

Measures and Controls

In the telecom world, performance measurement of assets persists to be a challenge. Telcos today require a robust performance measurement and management system that can understand the performance metrics of various elements within their system.

Integration of the following modes into your asset measurement strat is sure to help the management system :

Link KPIs to Capex
Track spares and optimizes sparing levels
Coordinate Asset movement across the teams
Automate reconciliation of FAR with inventory

Network Analytics and Optimization

An analytical approach towards asset management can help telecom operators address 50% of the challenges they face today. Leveraging insights provided by analytics will be the differentiating strategy for the future.

Here’s where you can leverage asset analytics and optimize your strategies to the fullest :

Drive process and behaviour changes to save Capex
Optimize movement of assets
Regard vendor performance insights through contract management
Optimize sparing levels across spare assets

Intelligent Contract Analytics

There is an increased demand to improve efficiencies and costs optimization of managing contracts across industries. The telecom industry is no exception. The nature of contracts in the telecom world span from supplier contracts to customer contracts, leases, commercial property rentals, and so on. It is becoming crucial for Telco teams to digitize the entire contract management process to optimize Opex costs and automate the risk mitigation process with advanced AI/ML techniques.

To improve the Contract Management efficiency, here are a few cases to run by :

Improve visibility through comprehensive contract database
Evaluate customer SLAs against service parameters to highlight risks
Leverage advanced AI / ML for smart contract ingestion, contract drafting, and risk analysis

These analytics and features packed into your network management system will enable an intelligence process by which communication service providers can understand their collected data and draw actionable insights. These insights, when seen as real-world metrics, can lead to improvements in customer experience, loyalty and ARPU, as well as increased efficiencies in all the processes mentioned above.

Monitor, manage and take control of your network assets

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Network Capacity Planning is providing the resources a network needs to prevent any effect on business-critical applications. There is no fixed way to tell when action needs to be taken to prevent any issues. Whether you’re assuring that there is enough bandwidth through a service provider or confirming the load on network devices, having accurate insights is essential.

A fundamental feature of network planning is finding how much bandwidth the network requires. Managers need to determine what capacity will be suitable for network growth in the future. Key initiatives require detailed views into bandwidth usage, combined with previous accounts of usage. Network capacity planning also helps with precise budgeting and provisioning.

Modern networks consist of a collection of routers, switches, firewalls, and other network components. While they are all configured to maintain the best possible function and throughput — both inside the peripheries of the internal network and across links to other networks — procedures teams leverage network capacity planning to identify potential flaws, misconfigurations, or other functionality that could affect a network’s availability within an indicated timeframe. From a high-level perspective, network operators employ a network capacity plan to understand some key network metrics:

Classifications of network traffic
The capacity of existing network infrastructure
Network utilization in the network
Existing network traffic volumes for internal networks and external networks

By conducting this type of network surveying, analysts can understand the maximum capability of exciting resources and the influence of adding incremental new resources needed to help future conditions. While capacity planning helps with the design of new network infrastructure, it can also help to determine additional staff or resources that will control and oversee the network.

Key Metrics for Planning Network Capacity

Network processes often set a baseline for network performance, a key question in each process is: What is expected of the network performance when it is running casually? Network engineers speculate the optimal performance metrics, which can then be applied by network management tools. Key metrics include:

Bandwidth — the maximum rate at which the information can be transferred, generally measured in bits/second
Throughput — the exact rate at which the information is transferred
Latency — the delay between the receiver and the sender
Jitter — the difference in packet delay rate at the destination source
Packet loss — measured as a percentage of packets lost vs the packets sent
Error rate — the total amount of corrupted bits of the sent data

When a threshold for a key performance metric is clocked, the icon for a network element can be indicated, and depending on the severity of the event, may also issue a caution.

Key Capacity Planning Solution Requirements

Key needs for capacity planning solutions vary based on the type of organization utilising it. Network operators can instantly take action on insights from capacity planning tools, some basic onboard solutions include:

Built-in link utilization charts
Ability to set the level of detail of the reports
Ability to create entries using customizable performance values
Predictability of timestamps when full utilization of network resources is predicted to happen
Automatically set alert limits based on data
Active reporting of performance metrics
Ability to generate a custom report and analyse on a routine basis
Allocation of key capacity planning metrics

A Few Network Capacity Planning Best Practices

1. Eliminate Bandwidth Complexities and Metrics Document the extent to which sources, destinations, devices, users, applications, protocols, and services are generating traffic. Employ dashboard services that deliver a page to help the complex bandwidth visibility problems and provide metrics regarding both bandwidth and device strain.

2. Cut Costs and Achieve Uninterrupted Services

Calculate and analyze traffic metrics to confirm performance and capacity baselines. After toggling through periodic reports, an accurate projection of future bandwidth consumption is easily attainable. Having an assessment of the future bandwidths with actual data will help against overextending service provider restrictions on sites that may be growing or experiencing excessive bandwidth consumption.

3. Be Aware of Load Balancing Equipment

Another critical element of network capacity planning is CPU/Memory administration. If the CPU on a device is under too much strain it may throttle its performance in return increasing latency or in the worst-case, crash. Thereby, causing a catastrophic chain reaction putting additional load on other devices. Likewise, insufficient memory handling could cause routing functions on a device to fail. Being conscious of increasing CPU/memory usage on devices will help provide insight into the network.

4. Ensure optimal performance delivery by identifying network bottlenecks

All links have congestion issues and have occasional spikes in traffic. network bottleneck policies are essential to ensure traffic spikes/congestion peaks are smoothed out, and additional bandwidth is allocated to critical network traffic. Without proper policies in place, all traffic has identical priority, and it does not apply to your business-critical applications are getting sufficient bandwidth

For example, without a thorough understanding of the type of traffic passing through a network, it is not possible to indicate if threshold parameters for services like VoIP are meeting target levels. Network Analytics will give you the insights that you need to properly plan network capacity and ensure capacity planning.

5. Understand the effect of network bandwidth utilization

Network bandwidth monitoring is invaluable to assist you to understand bandwidth requirements and network utilization. Bandwidth Monitor can monitor traffic, identify traffic trends, mark out traffic patterns, analyze traffic growth and identify applications that use the majority of the bandwidth. This data is delivered in extensive reports that track real-time usage as well as recorded trends of bandwidth usage over time.

The Bandwidth Usage report summarizes bandwidth utilization for a specified group of devices/interfaces over a calculated period. The information can then be screened based on interfaces, hosts, traffic direction and data range.

Additionally, statements like Top Protocols and Top Applications can also show recorded/real-time bandwidth usage. Network bottleneck policies can also be monitored through classification based reports, which offer a comprehensive view of pre-policy and post-policy traffic side by side, allowing administrators to control targets and determine critical issues like router saturation.

6. Recognize the performance volume of individual components on network infrastructure

Bandwidth Capacity Planning is an active process, anticipating future business requirements and providing the resources for the network to cope when an increase in demand hits. Extra draw on bandwidth can come from both advancement in the type of technology used (e.g. a new VoIP communication system or media streaming service) and an upsurge in the number of end-users (e.g. the following growth into a new market or a merger).

7. Network Redundancy Handling

Redundancy should also be a major concern, and when compared to the cost of unavoidable downtime, redundant connectivity pays for itself numerous times over! When designing redundant connectivity, the best practice is to use an alternate provider, utilizing an alternate link that is geo-separated from the primary connection. Redundancy checking will also require an audit of existing technology. Network flow monitoring tools can be used to locate such tendencies in traffic.

Just as a design engineer uses computer-aided design to create and test the strength of a tower, network engineers can use tools to plan, design and test a complete IT network even before it’s built. Network capacity planning is a crucial aspect of sound network analytics. A healthy network has the growth capacity to meet future needs.

Network capacity planning can provide solutions to new “what if” scenarios such as bandwidth changes due to the deployment of new applications or technologies, changes in traffic, data centre consolidation and migration in multi-vendor networks.

Check Out Our Capacity Management Success Stories

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Telecom operators are challenged to maintain their position in a world where adapting to technology and competition is rapidly eating into their market share. At the same time, CSPs are also struggling to keep operational costs associated with network infrastructure low when lean yet effective operations are critical.

Previously, a significant chunk of spending was directed towards network hardware. In the current landscape, the interest is towards investing in innovative software models, including network virtualization. At the same time, network teams are challenged with rolling out new technologies under stringent budgets. But without commercial models that link technology investments to business objectives, it becomes increasingly hard to measure the ROI of Capex investments. This is mission-critical, seeing how telecom operators lose 20% of their revenue or USD 65 million per year in Capex wastage.

What is the need for network asset management system?

To achieve the benefits of 5G and 4G-LTE-based network transformation, operators must reform swathes of connections, upgrade infrastructure, and purchase network spectrum, which involves massive investment. GSMA estimates that mobile operators will spend USD 1.1 trillion in mobile Capex between 2020 and 2025. Moreover, telecom operators may spend nearly USD 890 billion on 5G networks over the next five years.

As urban cities expand, operators must constantly assess emerging network requirements, project usage, procure assets, and allocate and install these at critical sites. But network assets procurement is an expensive and lengthy process. There are contracts to follow, compliances to meet, and clearances to be reviewed before the device/product is shipped to the site for rollout.

Faced with these challenges, network asset management tools are touted as the answer to provide network teams with a holistic view of all their network equipment and sites and whether assets operate at optimum capacity, as planned. Asset Maintenance software also helps identify which assets need attention and which can be sunset. Network asset management software may also reveal unutilized or underutilized assets, presenting an opportunity to re-harvest network assets to reduce the Capex burden.

Modern themes for network assets – Reuse. Reduce. Retire.

Intelligent resource allocation capabilities of modern Network Asset Management tools allow operators to make every investment dollar count, especially during network modernization programs. It monitors assets and automatically suggests those that can be safely relocated for better utilization. Retiring these assets in time reduces the Capex burden. Similarly, shutting down or removing unutilized assets reduces power consumption.

Network asset management tools allow telcos to build ‘virtual warehouses, ‘ i.e., where assets are decommissioned and ready for re-harvest. Paired with network asset metadata or Capex harvest analytics, this helps operators realize numerous benefits. To understand more, read the point of view here.

See how our Network Asset Management Solution can help your organization

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The majority of B2B management teams believe their pricing decisions need refinement, and about 15% have adequate information and tools to set and monitor prices according to a recent Bain global survey of more than 1,700 business leaders. For the companies that lean on finance for their growth, evaluating AI and Machine Learning would be your moment of upliftment. To succeed in today’s competitive, multi-channel globe, brands need to quickly catch and act on the optimal buying paths that are producing attention and revenue. With the new insights and studies under Revenue Assurance and product revenue segmenting, all sources of data such as media, email, website, ads, and discounts require precise marketing, achieved only by creating highly relevant and deeply automated revenue management at every touchpoint. Here are 5 uses of artificial intelligence for improved Revenue Assurance today:

1. Using AI to detect then prevent the most ineffective customer discounts and features, freeing up more monetary resources and time for those that earn profits.

A recent Bain & Company research composition, Bringing Order to Discounts Gone Haywire, provides a clear example of how AI can be used to demarcate discounts by each customer segment and type of discount. It mentions how focused analysis of discounts can help stop revenue leakage due to the trades that are more complex and the levers more varied, linking investment and customer behaviour difficult to tease out. An effective approach according to them uses segmented statistical analysis to look at the effect of a discount across all variables—product, region, customer segment and so on. That analysis can become the baseline for an artificial intelligence-supported engine to continually optimize results and inform trade-offs for different types of investments. It shows which transactional levers contribute most to profits, which ones reduce the value and what the pricing chart should look like to maximize profitable growth.

2. Pricing procedures are automated with AI in revenue management systems to boost Revenue Assurance.

The best pricing rule automatically calculates the prices based on considerable factors like similar product prices, historical selling prices, and customer feedback for the products, within the Minimum and Maximum price barriers that are set. Using AI and Machine Learning to look for patterns in pricing, volume and mix analysis to gain many insights then make changes accordingly to capitalize on the transactional data for measurable results today. These patterns include diagnosing the price, volume, and mix instabilities often locked within the restrictions of transactional data. Combining them has proven difficult and a challenge to combine in an intuitive application. Being able to deliver real-time price optimization driven by regional market conditions is a significant doing with AI implementation.

3. AI and machine learning are helping pricing managers find what a given customer is willing to pay or optimizing price across their product base.

Identifying arbitrary regions in pricing, discount, and deal size decisions are difficult to identify for buyers and products using data alone. This helps managers to analyze whether existing discounts make sense by correlating deal size to deals made, recognising outliers where discounts have been granted due to the negotiating discernment of the customer and therefore optimising revenue assurance.

AI can act as great sales mentors. The technology will allow the machine to listen in and analyze interactions, then formulate plans for how to improve in the future. Over time, the algorithms will become masters at predicting what will and won’t work. This means a sales program can approach connections with the best strategies right out of the gate.

4. Revenue optimization and flexibility are increasing beyond industries with limited inventories, including airlines and hotels, proliferating into manufacture-on-order only services.

All marketers are scouring for competitive, contextually relevant pricing delivered through AI. Machine learning is enabling varied price optimization to encompass product and services pricing scenarios depending on many factors like product, factoring in medium, customer detail, sales course, and the product’s standing in an overall pricing strategy. AI can eliminate the guesswork in product pricing strategies by automating the pricing process based on data.

AI will look at a diverse range of factors (including other exchanges a company has made) and compare the numbers against the odds to come up with a price offer that will produce customer satisfaction while assuring you earn a profit.

5. AI is allowing Configure, Price, Quote (CPQ) effectiveness by bringing greater precision and control to price management and optimization, which grows margins, reduces costs, and increases promising financial performance.

AI have the computing power and are rigged with precise functions to value based on its contributions to improving pricing management, optimization, and long-term performance as part of CPQ selling strategies. Thus helping with the ability of an organization to attain stable gross margin, revenue, and profitability performance year-over-year. AI applications can suggest the price that each segment of the customer base is willing to pay to sales and revenue managers. Using AI and Machine Learning algorithms in pricing guidance in CRM and CPQ systems is key to pricing segmentation strategies success. The larger the sample size of high-quality sales and transaction data and AI systems can obtain, the better the machine learning algorithms can accurately predict willingness to pay levels.

With enough data and a stable system in place, AI and Machine Learning will no doubt help grow revenue and assure great insight into the customer segment, product prices, revenue leakage, and transactional information to help managers make contemporary decisions. If a company is not taking benefit of AI and machine learning, your deals and discounts are not performing at peak efficiency, which might mean losing revenue. It’s not an overstatement to say that AI is a game-changer in the industry. The tier of the understanding you can attain from analyzing data is on another level. With the depth and complexity of the data, there’s nothing in the revenue optimization space that AI can’t help you do more effectively.

References :

https://public.tableau.com/app/profile/mckinsey.analytics/viz/Artificial_Intelligence/Impact_of_AI_and_Analytics

https://www.bain.com/insights/pricing-global-private-equity-report-2020/

https://www.globenewswire.com/news-release/2020/03/09/1996962/0/en/The-revenue-management-market-size-is-projected-to-grow-from-USD-14-1-billion-in-2019-to-USD-22-4-billion-by-2024-at-a-Compound-Annual-Growth-Rate-CAGR-of-9-6.html

https://www.bcg.com/en-us/capabilities/pricing-revenue-management/overview

To understand the essential capabilities for an effective Revenue/Business Assurance tool to combat evolving business risks.

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The telecom industry is in the midst of an exciting yet turbulent period. 5G is on the horizon and opens new doors of opportunity for communication service providers (CSPs). Investing in 5G is top-of-mind for almost every CSP, with an estimated USD 900 billion forecasted as the total spending on the new technology in the next five years, of which 80% will be towards 5G networks.

The burden of Capex is highest in the telecom industry when compared to other sectors, standing at a grim 12-18% depending on the CSP. 5G and activities around 5G network planning will only add to the Capex strain faced by this industry. This will add even more pressure on CSPs to ensure high returns on their investments, with the network being at the center.

CSPs are also looking to modernize their network to scale to meet rising customer demand and expectations and the associated data volume growth. This endeavor has led to a rise in network complexity stemming from network-wide densification and virtualization. As the 4G network expands further and the 5G network evolves, a CSP’s network will become more prevalent, dynamic, and complex. All this will require more effort. It will become virtually impossible for a human to comprehend and find an optimized solution for day-to-day troubleshooting in such a complex network.

Considering the high Capex intensity and network complexity, it is evident that CSPs have fallen short of achieving this objective, i.e., witnessing optimal returns on their network investments from their network capacity planning efforts. Considering the amount of data CSPs can leverage, generating the right insights to create more accurate and productive network investment plans can certainly help. Of course, this would make data science and advanced analytics, i.e., artificial intelligence (AI), machine learning (ML), and deep learning (DL), imperative for telecom CSPs to succeed. But will the application of AI/ML/DL models suffice?

Why accuracy matters

In an industry struggling to overcome tremendously high Capex and Opex challenges, CSPs need to make the right decisions at the right time. However, globally, a significant amount of Capex and Opex is wasted due to inaccuracies and inefficiencies in the network analytics process, which can no longer be afforded.

Dealing with these challenges is not new to CSPs. However, the legacy tools and processes that CSPs have at their disposal for capacity analytics add a massive, unprecedented burden, making the job a lot more inefficient. What if there was a way CSPs could be fully equipped to deliver exceptional network performance through enhanced agility and accuracy?

How CSPs can enable accurate network planning and optimization activities

The journey towards building accuracy for CSPs begins with laying the foundation in three key areas:

Ensuring the highest quality of data through intelligent data management
Bringing in operational efficiency by leveraging advanced automation methods
Infusing subject matter expertise (SME) to the outcome through domain-driven data science

To understand more on the three strategic pillars of a network analytics solution

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Network administration is becoming increasingly difficult as networks scale across wired, wireless, and virtual IT environments, leaving network administrators in need of all the aid they can get. Telecom Network Asset Management software can help with the proper management of network assets, ensuring that Capex and Opex are kept in check. Simultaneously, regulatory and audit compliance is maintained. You can easily access essential information and manage events and workflows with a centralised repository for network assets.

Site Inventory Mapping

Telecom Network Asset Management software gives your site operations managers and asset administrators the insight and control they need over their inventory and their locations. These tools assist in the mapping of all assets on a site, including passive infrastructures like gensets, HVAC systems, site security, and active equipment like antennas, microwave backhaul, BTS, and RRUs, to ongoing telecom infra projects like tower rollout, construction, and maintenance. The resulting assets delivered to the sites are linked with the source request or project once operation teams produce material requests (Bill of Quantities or BOQ), which are often fulfilled through supply chain management systems. When your tower tenants ask for assets to be deployed on their properties, these assets are tagged with the customer order. As a result, every asset on a site has a source reference, which allows you to see where the asset came from.

Furthermore, Network Asset Management software makes it easier to link equipment lease agreements to site asset inventories, reducing revenue loss from unregistered equipment. This is a crucial component in ensuring a site’s profitability, and it’s easy to do thanks to the centralised nature of a site management platform. A properly integrated system captures contract abstraction with a complete history of expiry dates/renewal dates, as well as a document repository for these contracts.

Asset Maintenance

Huge and expensive assets require regular upkeep, or they will stop working at any time. Maintenance does not always imply asset repair. The assets are thoroughly assessed during the maintenance procedure to determine the problem. Network Asset Management software is used to perform this maintenance in a proactive manner. This eliminates the possibility of a sudden asset collapse. Proactive maintenance cuts downtime and saves money in the long run by reducing losses.

Asset Tracking

Network asset management tools like asset tracking are crucial for operations managers during site deployments, daily operations, and decommissioning. To correct process flaws and uncover operational gaps, conducting audits and highlighting process deviations is critical. CSPs want a network auditing system that can automatically detect the addition, relocation, and removal of assets in the network and notify users if these changes are outside of recorded workflows. This data can also be utilised to make suggestions for process changes. Network asset management systems consist of a good Spare Management module that can track spare assets across the network and offer information on spare assets, as well as spare levels (min, max) and spare classifications. Users should also be able to start procedures to move extra assets for excess returns, repair, and fault replacement requests. A spare management module must also use predictive analytics to calculate the exact spare levels for each network site based on past fault data in network asset management systems.

AI-based Contract Management

Contract management in the telecom industry is a complicated task that involves a wide range of contract agreements. Comprehensive contract management with automation and AI capabilities is becoming increasingly important for telco teams. Operators want a Contract Management module that can ingest contract data from any data source, including ERP systems, map each contract to relevant network assets, and notify specific users or teams when contracts expire or contract line items are not covered. Enhanced efficiency and accuracy levels can be attained by integrating artificial intelligence into the contract ingestion mechanism with Network asset management tools.

Audit Trails

You will be able to observe audit trails in telecom asset management software that chronologically capture and log events, providing historical evidence of actions made on an asset from installation to removal. Asset audit trails ensure that users are held accountable for their actions and that internal process criteria are met. Asset transaction information is more accurate, accessible, and usable thanks to audit trails created by automatic electronic logs using network asset management tools.

Timely Reports

Run reports on a regular basis to evaluate and track asset data. It assists you in forecasting future demand and identifying bottlenecks in your procedures. The major goal of such reports is to help you achieve your business objectives and keep all of your assets working at peak performance. Network asset management tools like asset maintenance and service reports, asset usage reports, asset location reports, asset audit reports, and other useful reports can be generated using telecom network asset management systems.

Download this Case Study to how a South Asian CSP leveraged Subex’s Network Asset Management solution to get the most of their asset investments

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With 5G adoption and technology enhancement, the telecom operators are expecting an exponential increase in the data volume, the hardware footprint, and the OpEx required to perform daily revenue assurance activities. With the observed trend of falling ARPUs year on year, it has become evident for the telecom operators to look for a newer approach in Revenue Assurance practices.

Anomaly detection has the ability to monitor and detect anomalies on revenue metrics. This helps to provide deeper insight into revenue trends and business performance beyond what traditional BI tools deliver. To discover and understand how to prevent revenue leaks, revenue assurance teams require granular data, which requires weeks of work to combine, analyze, and correlate. While systems can save data for up to 60 days, figuring out what’s going on requires weeks of collaboration between engineering, big data, billing, and customer teams. Anomaly detection allows the processing of aggregate data based on specific dimensions to monitor millions of KPIs, identify discrepancies quicker and correlate with other associated datasets. The approach uses lesser hardware and provides near real-time insights to only KPIs, which ultimately helps to save on Total cost. Following are a few areas where Anomaly detection is proving to highly effective for Telecom operators:

1. Preventing Price Errors

In the telecoms industry, price errors are one of the most common sources of revenue leakage. Despite the fact that shops have implemented different checks and balances, pricing errors are widespread. The main contributions are data entry errors, missed decimal points, digit reversal, and other clerical errors done in a hurry. It can also happen due to misfeeding promotional offer dates, such that promotions may start or stop earlier or later than planned. Such revenue leakages go unreported at first but are discovered after they become a big problem. Automated anomaly detection can help telecommunications monitor sales price, volume, number of transactions, visitors, and other components in real-time and correlate them based on region, demographics, and behavior.

2. Monitor CDRs

Every time a subscriber uses a service via their phone, a call detail record (CDR) is created. These CDR files are subsequently transferred from the network to the mediation system, which then sends them to the billing system, which is in charge of processing CDRs and billing customers for their usage according to their agreed-upon plan. Telecoms may observe CDR leakage in revenue assurance solutions between multi-vendor mediation and billing systems. Although each correctly processed CDR generates income for the service provider, the billing system can drop or suspend some CDRs, resulting in direct revenue leakage. The machine learning model detected an anomaly whenever the response time of any billing API was beyond the normal range. Anomalous behavior was also detected in the infrastructure layer, such as irregular CPU use on a Unix virtual machine. The operations team responded by changing the billing API and repairing the Unix virtual machine in response to these abnormalities to prevent telecom revenue assurance.

3. Contract Analysis in Revenue Assurance Solutions

Aside from the contract terms, which must be fulfilled in the letter to avoid income leakages, the additional provisions inside the contracts lead to the organization creating value or leaking revenue. When a business creates and amends hundreds of thousands of contracts, it becomes practically hard to dive into the specifics and manage the contracts flawlessly, resulting in value realization and the avoidance of losses. While there are a variety of reasons for revenue leakage, the most common ones are data entry errors, unpaid accounts, client management issues, incorrect reporting, and discounting. All these are rooted in a lack of visibility, transparency, automation, and accountability, which have a significant financial impact on the company’s telecom revenue assurance. Anomaly detection identifies the anomalies in the form of errors and accuracies and helps in saving the total cost for the company.

4. Contract Monitoring

Preventing revenue leakage necessitates constant monitoring of contracts by revenue assurance solutions to create, amend, and implement them. Income leaks can be identified, recouped, and prevented by evaluating contracts and rigorously analyzing processes. Artificial intelligence (AI) is gaining traction across business operations and processes, including contract analysis and management, thanks to technological advances such as machine learning, natural language processing (NLP), and text analytics.

Conclusion

Due to network data’s complexity, dynamic nature, and newer 5G services, AI/ML-based autonomous solutions are essential for attaining business goals and avoiding blind spots. Dashboards and manual criteria aren’t responsive, robust, or agile enough to handle this challenge. Anomaly detection solutions can analyze several dimensions of data sources, looking at the cell, subscriber, and device-level KPIs. It can also effectively monitor network equipment defects, correlate alarms for noise reduction, and prevent revenue loss with root cause investigation. With falling ARPUs, the discrepancies quantified are not justifying the investment made towards onboarding 5G services into the traditional revenue Assurance practices; hence it makes Anomaly detection critical for the telecoms to meet the complexity of reaching the end goal in the Digital Era.

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The telecom world is changing, all while enabling the digitization of services across different sectors; these changes, however, are increasing the fraud risks and threats within the telecom world itself. Fraudsters have long developed methods and practices to exploit the telecom network. Losses from telecom fraud have a direct impact on a carrier’s margin. It also impacts the brand image and their customer’s experience. A holistic fraud management system help safeguard operators’ revenues and continue to provide superior quality of service to their customers. This allows the carrier to compete more effectively and profitably.

Rule-Based Systems Aren’t Enough

A traditional approach to fraud detection has been through Rule Engines, which could be:

If-Else Conditions
Thresholds
Expressions
Evaluating Data Patterns

These are widely known as deterministic solutions where an event triggers an action. The biggest pros and cons of this approach is that human intervention is needed to feed the logic.

Key Attack Vectors

The world of telecommunication fraud is constantly changing. There has been an increase in critical attack vectors, such as:

Fake and synthetic IDs: Fraudsters now have a wider choice of alternatives for obtaining ID documents, whether through phishing or a Rent-an-ID business. Since parts of the IDs are valid; this makes fraud detection much more difficult.
eSIMs: While eSIMs, or virtual SIM cards, are more secure since they are more difficult to clone or steal, they are still vulnerable to malware and social engineering assaults.
Social engineering: The pandemic has dramatically increased the social engineering attacks. Social engineering fraudsters use a variety of means to carry out their attacks, some of which are: Phishing, Spear Phishing, Caller ID Spoofing, etc.

AI as a Fraud Detector

Artificial Intelligence (AI) is not new, and it has been around for decades. However, with the advent of big data and distributed computing that is available today, an effective Fraud Management (FM) strategy includes 3 important pillars: Detect, Investigate & Protect. We believe AI can positively influence all the 3 pillars of fraud management, from reducing false positives to helping in mining root cause analysis to creating enhanced customer experience in protection.

AI/ML brings in the below benefits:

Higher Accuracy – Because AI can learn and adapt to Business scenarios faster, AI can significantly increase True Positive ratio.
Reduced time-to-detect – How fast a telecom fraud event can be detected.
Self-Learning – How over a period changing business scenarios and seasonality in data can be adopted to fraud detection and prevention.
Fraud Intelligence– How customer or any other entity behaviors can be learned and categorized for better fraud detection and prevention.
Proactiveness – Ability to mine for unknown patterns not seen in the data earlier, thereby enabling proactive mitigation of telecom fraud.

Fraud Detection in the Modern World

CSPs need AI with strong machine learning skills to handle extraordinarily massive volumes of data while also coping with the high levels of agility and complexity demonstrated by fraudsters from all over the world to effectively manage and fight telecommunication fraud in the modern landscape.

CSPs indeed need a robust fraud management system that incorporates advanced technologies such as AI/ML to combat telecom fraud and stay ahead of fraudsters.

With ML Algorithms being a part of the fraud management system, it will be able to mine data from historic fraudulent behaviors and create models. These models are then used to evaluate real production datasets to score whether a certain activity is fraud or not. An advantage is that these models are very good at looking at the datasets from multiple dimensions and measures at the same time and concluding whether the event is fraud or not.

The application of AI has its own significant challenges and requires a new frame of thought, however looking at the Data Tsunami that has hit the fraud management teams, it looks an AI pro approach would only help Fraud Management teams to scale further.

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