Testing

Category: AI

  • Cognitive RPA – Automation for Next Gen Revolution in Telecom

    Cognitive RPA – Automation for Next Gen Revolution in Telecom

    Cognitive RPA (Robotic Process Automation), as the name itself, suggests, provides intelligence to conventional RPA. Conventional RPA is extremely good at automating rule-based tasks involving structured and semi-structured data.

    However, with enterprise processes being highly complex and technologically intertwined, utilizing both structured and unstructured data becomes complicated. It is imperative that only the RPA solution would not suffice. The digital workforce (Bots) would be required to make complex decisions that involve learning, reasoning, and self-healing capabilities.

    In a nutshell, Cognitive RPA is RPA on steroids. It utilizes artificial intelligence technologies like computer vision, OCR (Optical Character Recognition), document understanding, NLP (Natural Language Processing), Text Analytics, and numerous custom-built or out-of-the-box Machine learning & Deep Learning models that help bots make complex decisions while automating an end-to-end process.

    In addition, many vendors are providing Human in Loop capabilities where the output of AI/ML models is validated by humans (Business SME), and post their approval, bots take the automated process to its completion.

    Along with automating web-based applications, RPA can also automate Windows applications and legacy applications, for which developing IT integration would be a cost-intensive, time-consuming and gargantuan task. RPA can mimic what an end-user does with near-zero errors and without being fatigued, bored, or roguelike humans. It offers higher accuracy, increased performance, increased adherence to SLA, and better compliance. With this amalgamation of AI and RPA (Cognitive RPA), we can now automate end-to-end processes and can handle complex cases which would have earlier required human interventions.

    The main goal of Cognitive RPA is to take up all mundane, repetitive, and tedious tasks from humans so that they can focus on more strategic tasks rather than worrying about the former. The motto is “If you hate it, just automate it.”

    There have been a plethora of use cases for Cognitive RPA / Intelligent Process Automation (IPA). The following are some of the use cases:

    • New Subscriber Verification: Individual’s identity-related information is extracted from the submitted proof image and is matched against user input for any discrepancies. Moreover, the individual’s picture in ID proof is matched with their current picture and against pictures of fraudsters to verify the new subscriber’s identity.
    • Invoice Processing: Extracts vital information from invoices like Bill To, Ship To, Due Date, Invoice, line items, total, etc., to run an audit against system entries by reconciling extracted information against them.
    • Digital Assistant: Identifies failed jobs and takes remediation actions by understanding from underlying logs.
    • FCR (First Call Resolution): Resolving customer’s concerns from the first call to a customer care center, bots can assist employees by offering real-time guidance (retrieving customer information, re-keying updated information, trigger issue to resolution workflow for known issues, etc.).
    • Information Security Audits: Bots can easily collect evidence (logs, database records, flat files, etc.) across disparate systems and analyze them for any non-conformities against set enterprise policies, procedures, and guidelines.

    Other prevalent use cases are Anomaly Detection & Remediation workflow, Fraud Detection & Remediation, etc.

    We, at Subex, help customers realize value from Cognitive RPA implementation. We play the role of Trusted Advisor helping clients with Process discovery (identifying the process), Evaluation and Selection of the process – fit for RPA, Process Standardization (creation of user-friendly templates, documentation, communication plans, etc.), even Process Re-Engineering if required. Post finalization of the process for automation; High Level and Low-Level Designs are created in constant consultation with Business / Process SME. After rigorous iterative development and testing cycles, the full-fledge RPA solution is delivered so that customers can reap full benefits from it. We also undertake consulting assignments helping enterprises set up RPA CoE (Center of Excellence), Scale-Up their RPA journey, and assist them in stepping forward from conventional RPA to Cognitive RPA.

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  • Telco AI Transformation – Building the New Hyperscalers [Part 1: The big picture]

    Telco AI Transformation – Building the New Hyperscalers [Part 1: The big picture]

    Who would have imagined – at literally the flick of a switch the way we work and live will be changed forever – thanks to the pandemic.

    Amidst all this, the folks from the telecom industry have been the unsung heroes playing a critical role in the dramatic shift. A big thank you to the telco warriors – without your tenacity, resilience and grit this could not have been possible.

    Yet, as has been the case in the last several years most of the wealth making opportunities have continued to elude the telcos. It is a curious paradox that on one hand telcos have become the backbone of our new world order, and on the other, the pressure on revenues, margins, and that of customer expectations has only grown.

    While one might find multiple reasons for this – in my opinion, there are some things telcos can do to address this situation.

    • Embrace AI: Shift gears from scattered and sporadic AI experimentations for decision analytics to a strategic view of transforming all decision analytics in the organization by leveraging AI. This has to be a top-down CXO-driven imperative. It is clear across the telco value chain AI driven decision analytics has the ability add a few percentage points in terms of revenue uplift and cost reduction
    • Re-think tech stack: Cloud for elasticity and Open Source for agility is absolutely necessary. Ensure best practices from hyperscalers such as Google, Uber, GoJek are adopted.
    • Derive value from data: Ensure data and features are well cataloged and searchable. And Ensure AI infra and services from Image to Speech to Forecasting to Deep Learning are easily and readily available. That’s the only way to attract and retain Data Science talent.

    Learn from the hyperscaler enterprises that are built around data and AI-driven decision analytics. Just think about it – if you were to let go of the entire data science teams in telcos there be very minimal impact. Now compare it with the impact of the same activity on the likes of Google, Uber or GoJek. The repercussions will be catastrophic, to say the least!

    No – it’s not to say that telcos do not understand the value of AI-driven decision making. The truth is that across the telco value chain there are several opportunities to leverage AI-driven decision analytics with the possibility of boosting revenues and profits. But most efforts thus far have been sporadic and scattered set of experiments.

    It’s not all gloom though. And I would like to use the example of a telco who are clearly thinking at a planetary-scale. I had the opportunity to listen in to Rakuten’s CTO Tareq Amin during a webinar session and I was absolutely floored by his vision, clarity of thought, and conviction to think planetary scale. He calls it Rakuten Un-limit. Rakuten sees itself as a platform company and is combining the power to Open Source for agility, Cloud for scale and elasticity and a co-create/ collaborate model to redefine how telcos can compete against the best and win. Telcos like Rakuten, Jio and Telefonica to name a few have a tremendous focus on leveraging data, and AI-driven decision platform as a must have layer in their entire stack.

    In my mind, I am absolutely convinced. Intense digitalization, the impending launch of 5G services combined with AI in decision analytics has the power to propel telcos into the league of hyperscalers. It is time telcos recognize this and start organizing themselves around data – a precious commodity they are already awash with!

    Note: This blogpost is part of a multi-part series on AI transformation for telcos. Look forward to the subsequent parts that will be published under the title.

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  • Why do Telecom Operators need AI-based Contract Management?

    Why do Telecom Operators need AI-based Contract Management?

    There is a well-known adage that businesses run on people’s trust. However, in the real practical world, contract agreements remain the ultimate element of trust. Contract documents provide the necessary assurance to companies that certain obligations are legally binding, and thereby business can be undertaken with complete confidence. In that sense, looking at the larger context, contract documents are the driving force that enable businesses to run smoothly and efficiently.

    Value-addition

    In the case of medium-to-large-sized companies, it is next to impossible to track all active contracts manually. Gone are the days when contract managers manually indexed all contracts within filing cabinets, updated Excel sheets and tracked soft copies within random emails. Imagine yourself as a contract manager answering all types of queries coming from different stakeholders (such as legal clauses, liability damages, non-adherence penalties, payment schedules, renewals notifications, service quality, rebate clarifications, penalty clauses, escalation matrices, etc.) for 1000s of contracts. Considering contract managers are the custodians of all contract requests, imagine being responsible for these kinds of queries day-in and day-out. How exhausting it could turn out to be!

    Today, contract managers’ time and skills are more precious for adding value to the business, rather than hustling for some of these clerical routines. While digitization seems to be the most obvious answer, it is easier said than done. Even if one can digitize and bring all the contract data into a searchable database, the contracting complexity does not end there. In fact, it all begins when the contract is created.

    Contract Drafting

    Drafting a contract is often a tedious and complex activity involving multiple stakeholders. Few organizations have found a way of templatizing certain types of contracts as a step to reduce the complexities involved. Nonetheless, when a typical medium-to-large organization handles a large variety of contracts, the drafting process indeed becomes a lengthy and complex web of interrelated tasks. Often, there are no smooth workflows when it comes to drafting a contract, collaborating, capturing metadata and the other nitty-gritties that come along with it.

    Take a moment to ponder a new world – where you, as a contract manager, can initiate a contract drafting process through a few clicks. In this world, you can pick a contract template based on the contract type, tailor it and invite other stakeholders to collaborate online. Imagine that from start to finish, you are guided, step-by-step, by a system that keeps track of all necessary elements, while the contract document is under construction. The system in question has all the necessary in-built features like redlining, change tracking, comment threads, references to other documents, notes comparison, etc. The system can, additionally, review similar contracts or clauses drafted in the past, and has an all-powerful search capability that digs out information from the repository. How liberating it could be! As a contract manager, you would then be able to contribute to higher-value tasks once you get ‘hands-off’ from the currently tedious process of drafting.

    Telecom Operators and AI Contracting

    In large complex organizations like telecom operators, the volume and variety of contracts that are handled on daily basis are usually high. Operators need to manage contract agreements and liaise with various parties like suppliers, customers, landowners, property lessors, agencies, etc. Not every type of contract can be templatized according to the operator’s liking. At the same time, contract managers need to handle hundreds of variants spanning across different regions of operations. Life is not at all simple!

    In such cases, it is important to equip contract managers with automation tools that can handle new contracts or renewal documents more effectively. The idea is to automate contract ingestions, index certain important clauses, raise alerts against the contract lines that are deviating, contextualize every type of contract and finally push the data into a large datastore.

    With advancements in computing power and cost-effective storage, AI-based applications are becoming a reality in many domains, and the contract domain is no exception. Purpose-built AI/ML models can extract specific data from contract documents to reasonable levels of accuracy. Over time, as AI/ML models train on new contract variants, accuracy levels improve considerably. In terms of Opex cost-saving, AI-contracting can free a good amount of manpower which can be applied to other value-centric activities in the business. Moreover, by reducing manual inputs and automating the entire process, AI-contracting can improve contract data accuracy and thereby mitigate risks. Through the added ability to integrate with other systems, potential liabilities and loses can be averted. Additionally, contract managers can have clear visibility on vendor performance which is tremendously useful during renegotiations.

    Many telecom operators are now looking to onboard various levels of automation by leveraging technologies such as AI/ML and move forward in their digital transformation journey. AI Contracting is one such important step into the digital future.

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  • Network Capacity Management and how AI/ML can improve Network Investment Planning

    Network Capacity Management and how AI/ML can improve Network Investment Planning

    The telecom world as we know it is constantly evolving, with technology innovation and enhancements coming on a regular basis. And today, with mobile technology evolving from 4G/LTE to 5G, there is an evolution in terms of customer experience demand and competition landscape as well. This puts CSPs in a precarious position to deliver the best experience to their consumers while keeping costs low, and competitive advantage intact.

    This is where Intelligent Network Capacity Management comes in:

    What CSPs need today are network capacity management solutions that can significantly help them enable smart network investment planning to maximize return on network investments.

    However, in reality, this is far from as straightforward.

    Strategically, CSPs leverage capacity management investments to gain competitive advantage and enhance end-user experience. However, such investments are highly cost intensive, both in terms of Capex and Opex. Any inaccuracies in the overall network investment planning can lead to unintended impacts on company financials and can create significant network inefficiencies.

    How do CSPs come up with network investment plans which are accurate, has optimal cost, meets current & future requirements of users and gives competitive advantage over a long term?

    In our view, the overall Network Investment Plan has 3 prime enablers which can help CSPs to formulate an efficient and robust Network Capacity Management framework comprising of: Network Capacity Assessment, Network Capacity Planning, Network Capacity Optimization, Network Capex Optimization, and a lot more.

    Key Enablers:

    • End to end domain expertise: CSPs with capability to proactively manage end-to-end network would ensure high flexibility, reliability and availability of network service continuity. Deep network domain and service expertise to address broad set of use cases deployment fitting across CSPs key business objectives will ensure superior network and business performance.
    • Strong data management capability: As per GSMA mobile economy report, by 2025 around 130 exabytes of data would be generated every month, four times increase in volume as compared to current data volume, which is around 30 exabytes per month. Seamless handling of such a massive amount of data would ensure CSPs to efficiently extract relevant and meaningful insights at the right time for analysis.
    • Leveraging advanced analytics: Complexity of running a 4G and 5G network with plenty of data points in IoT will make it impossible for human hands for network planning, capacity management, quality optimization and predictive capacity analytics to enhance the customer experience. CSPs leveraging machine learning and deep learning models today will reap significant benefits in their overall network management, an ecosystem comprising of 2G, 3G, 4G and 5G technologies.

    The three major enablers are quite significant in today’s digital era and will be extremely crucial in the 5G ecosystem. Delivering extraordinary customer experiences, staying ahead of competition and most importantly being profitable should be part of every CSPs strategy for evolution towards 5G technology.

    Predictive network management modules considering the key aspects of digital transformation can help CSPs consolidate current 4G LTE network and plan for 5G network in a most cost-effective way with focus on enhancing the customer experience and maximizing returns on network investments.

    Read a case study on how we helped a Tier-I telcos increase their revenues by over 12% through intelligent network investment planning.

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    This blog was originally published on Whatech

  • How Artificial Intelligence and Machine Learning is the key to combat Identity Fraud

    How Artificial Intelligence and Machine Learning is the key to combat Identity Fraud

    In an increasingly digitally connected world, identity fraud is a growing problem. According to the Communications Fraud Control Association’s (CFCA) annual fraud survey, identity fraud took the top spot as the number one fraud method present globally and at individual companies. Also featuring as the first among the top ten fraud methods, identity fraud in telecommunication during the subscription process (subscription fraud) resulted in costs of $2.03 billion.1

    Identity theft, where the use of fabricated identities at point of sale, enables the fraudulent use of telecom services and/or perpetuates subsequent fraudulent activities, can result in serious implications in the present age. With highly interconnected 4G and soon to be launched 5G networks enabling not just value-added services but also financial services like mobile payments and banking, it opens up access like never before.  Identity theft can work as an entry point for myriad types of fraud or even terrorism. With access to secondary authorizations (PIN code verification), subscription fraud can be used for any number of illegal activities.

    This has facilitated an urgent need for a proactive and dynamic response to fostering digital identity security.

    Securing against Identity Fraud

    The answer to fighting fraud lies in detecting data anomalies in real time. For example, to tackle telecom fraud, the Technology Research Institute (TRI) has stated that, real-time point-of-sale identity verification services are an invaluable aid to stopping fraudsters from exploiting identity theft.2 Historically, rules have always been in the system. But in the increasingly connected world, effective fraud coverage is only possible with a combination of rules and applied Artificial Intelligence (AI) and Machine Learning (ML) technologies.

    With AI and ML technologies, companies are able to detect data anomalies in real-time and make decisions based on information as it happens, empowering them to anticipate and take proactive action. For instance, AI/ML techniques can use facial recognition technology to identify high risk by making checks against blacklists. ML can augment traditional rule-based systems to develop and train algorithms to determine the characteristics of traffic and identify anomalies that could end up being fraud.

    Furthermore, the immense amounts of unsecured data flowing in from connected devices onto operator networks can be secured only with AI and ML. AI technologies are equipped with the capacity to scale up efforts and enable fraud detection at a massive scale by handling the management of millions of customer or network data points.

    As networks continue to expand and new fraud schemes continue to evolve, a combination of rules and applied AI/ML models will serve as the most effective way in combating identity fraud.

    If you are interested to learn how AI /ML techniques can help you combat Identity Fraud

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    1 . https://v2.itweb.co.za/whitepaper/Amdocs_LINKED_2017_CFCA_Global_Fraud_Loss_Survey.pdf

    2 . https://technology-research.com/products/fraudmgt/telecom_fraud_management_executive_summary.pdf?_sm_au_=iTHSrnwsn4HFwT3q