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Frauds in telecom sector

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Telecom Frauds and detection Techniques by various resources and research

Telecom Frauds and detection Techniques by various resources and research

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Frauds in telecom sector Frauds in telecom sector Document Transcript

  • 1. EXECUTIVE SUMMARY 1|Page
  • 1.Executive Summary:In my words telecom frauds are unauthorized and illegal use of telecom services like cellular, network security, and infrastructure (include network equipment etc.) for an intention of misuse or for not paying the particular service. Due to this types of frauds many fraudsters are reducing their cost of getting that services or products and increasing the problems of communication service providers. Now day‘s frauds are happening due to the many new technologies are coming in the market which are easy to hack due to lack of security by operators. Due to all these frauds revenue leakages for operators are increasing year by year and loss to the operators are in billion dollars which is very big loss for them. Operators are using various to find out these frauds and also many big companies like Infosys, Cisco, IBM, TCS etc. are developing new security tools to reduce the frauds. Survey done by various organizations like Communications Fraud Control Association (CFCA), Neustar,Number Portability Administration Center (NPAC), TRAI etc. to detect where the mostly frauds are happening and how it can be addressable or removable. They found that mostly frauds are in PBX hacking, identity fraud, international revenue share fraud, by-pass fraud, credit card fraud etc. due to these frauds Communications Fraud Control Association (CFCA) estimated global telecom fraud losses to be in their 2011 survey – $40 billion annually. To detect these frauds there are various methods and technology are using by operators and these are Social media monitoring, setup a fraud control unit, Utilizing sector-focused forensic experts, Strengthening controls around customer identification and also logics like user profiles, neural networks, decision trees etc. are utilizing and due to use these tools and technique they got benefited and frauds are reduces as compared to 2011 survey by CFCA. ―The CFCA is committed to raising awareness of this worldwide problem and to providing a forum for education and information-sharing among communications service providers. It‘s where communications professionals go to learn about these schemes and become effective at stopping them.‖ So here I am trying to explain all these frauds and their detection techniques in detail by various survey results and from articles data‘s. As everybody know that today is a day of technology and in every technology there is some defects so frauds we cannot reduce fully but we can try to reduce higher. 2|Page
  • 2. INTRODUCTON 3|Page
  • 2.1 Definition of Fraud: Telecom frauds is an act of obtaining telecom services/products and/or the instruments, equipment or devices with no intention of paying for them or abuse of services. Also we can say that the use of the telecommunications network with the intention of avoiding payment. 1. Without correct payment 2. With no payment at all 3. Someone else pays. Fig.1 Sector wise Frauds The Association of Certified Fraud Examiners (ACFE) defined corporate fraud as: ―The use of one‘s occupation for personal enrichment through the deliberate misuse or application of the employing organization‘s resources or assets‖. Companies Act, 1956 (‘Act’): The Act does not contain any definition of the term ‗fraud‘. Definition was planned to be introduced into the Act vide the Companies Bill of 2009. Definition proposed to be drawn from the definition of ‗fraud‘ as provided under the UK Fraud Act, 2006 so as to provide a comprehensive, inclusive definition, defining the offence of fraud with regard to affairs of the company. 4|Page
  • 2.2 Types of Frauds: 2.2.1 Revenue Frauds: Its motivation is to make money or to use services at no or reduced cost. 2.2.2 Non-Revenue Frauds: Motivation to acquire anonymity to mask criminal activities or obtain the sheer thrill of challenging the telecom service provider‘s network security. Also frauds are divided into other categories: 2.2.3 Technical (External) Frauds: Technical external frauds are committed externally, i.e. from outside the network, and are executed by gaining access into the network system using tools such as hacking. External frauds are usually impersonal, opportunistic and driven by pure greed. Examples: Use of automatic telephone line isolators to penetrate into the secret code/password for dynamic STD lock/Personal Identification code (PIN). Accessing the O&M port of the switch from remote and perform switch opening & closing of telephones or other services. 2.2.4 Technical (Internal) Frauds: Technical Internal Frauds are committed by gaining internal access to the network system. Internal frauds can be very different from external frauds and are more often than not be driven by personal grudges or revenge. Internal fraud reveals the breakdown of internal structures, the relationship between employer and employee, and the lack of internal controls to provide safety nets, checks and balances. Examples: Manipulation of databases of billing, charging, routing and subscribers etc.Changing the equipment number d ring preparation of bulk billing tape soequipment number during bulk that the metering information is transferred to the spare equipment number resulting in non-billing. 2.2.5 Non-Technical Frauds: Non-technical frauds are committed without accessing or interfering with the network system. Examples: Subscription Fraud: Subscriber registers for phone service, makes up a large phone bill mostly though call selling and absconds before disconnection. 5|Page
  • Clip on fraud: The fraudster accesses the telecom infrastructure and diverts the line and makes calls or sells services to others. Call Forwarding: The incoming calls coming to line are forwarded to a destination such that the calls are charged at much lesser rate which otherwise would much which otherwise would have been charged as per the distance. According to: Indian Contract Act, 1872 (‘ICA’): • As per the provisions of Section 17 of the ICA, the term "Fraud" means and includes any of the following acts committed by a party to a contract, or with his connivance, or by his agent, with intent to deceive another party thereto or his agent, or to induce him to enter into the contract:1. 2. 3. 4. 5. The suggestion, as a fact, of that which is not true, by one who does not believe it to be true; The active concealment of a fact by one having knowledge or belief of the fact; A promise made without any intention of performing it; Any other act fitted to deceive; Any such act or omission as the law specially declares to be fraudulent. 6|Page
  • 3. Literature Review 7|Page
  • 3. Literature Review: Here is some articles on telecom frauds and its detection techniques, let us know what this articles are saying in briefly: A. Data mining approaches to fraud detection in telecommunicationsA short description of ongoing research Constantinos S. Hilas Dept. of Informatics and Communications Technological Educational Institute of Serres, Greece chilas@teiser.gr Telecommunications fraud is increasing dramatically each year resulting in loss of a large amount of Euros worldwide. An invaluable tool for the detection of fraud is the modeling of telecom users‘ behavior. Fraud detection is important to the telecommunications industry because companies and suppliers of telecommunications services lose a significant proportion of their revenue as a result. Moreover, the modeling and characterization of users‘ behavior in telecommunications can be used to improve network security, improve services, provide personalized applications, and optimize the operation of electronic equipment and/or communication protocols. The Communications Fraud Control Association (CFCA) recently announced the results of a global survey carried out in 2009. In accordance to this, the loss of companies as a result of fraud is estimated to be around $72 - $80 billion (USD), up34% from the CFCA Survey results of 2005. This amounts to c. 5% of their revenue. According to the same survey telecommunications fraud is closely linked to the financing of terrorist organizations throughout the world. All cases of telecom fraud can actually be viewed as fraud scenarios which are related to the way the access to the network was acquired. However, given the plethora of telecommunication services and the ingenuity of the fraudsters‘one may be confronted with diverse fraud techniques. So, detection techniques designed to detect one case may fail to detect other types of fraud. In general, fraud detection focuses on the analysis of users‘ activity and the related approaches are divided into two main subcategories. The absolute one which search for limits between legal and fraudulent behavior, and the differential approach which tries to detect extreme changes in the user‘s behavior. Nonetheless, one should keep in mind that detecting excessive usage may not lead us to identify a fraudster but a provider‘s best customer. 8|Page
  • B. Overview of Telecom Related Frauds TUSHAR CHAWLA Partner Economic Laws Practice November 19, 2010 In this article Mr. Chawla is trying to explain about the different types of frauds and the definition of frauds by various organizations. He suggests that there exists no specific definition for the term telecom Frauds‘ under the present day Indian legal system. He divided the frauds in technical and non-technical categories which are further divided into external and internal frauds. He has given some examples of frauds likeExamples: •Subscription Fraud: Subscriber registers for phone service, makes up a large phone bill mostly though call selling and absconds before disconnection. •Clip on fraud: The fraudster accesses the telecom infrastructure and diverts the line and makes calls or sells services to others. •Call Forwarding: The incoming calls coming to line are forwarded to a destination such that the calls are charged at much lesser rate which other wise old much which otherwise would have been charged as per the distance. The Growth Story: The Indian telecom sector has been one of the fastest growing in the world. Total telephone subscriber base reaching 601 million at the end of February 2010 from 582 million in January 2010, i.e. growth rate of 3.22%.Telecom Frauds have a multifarious impact on telecom companies, effecting operations as well as the revenue streams. c. Telecommunication Fraud Management Stephen Brown, CSO Waveroad Telecommunication Fraud is the intentional and successful employment of any deception, cunning,collusion, artifice, used to circumvent, cheat, or deceive another person, whereby that person acts upon it to the loss of his property and to his legal injury. However, there does seem to be a general consensus that telecom fraud, as the term is generally applied, involves the theft of services or deliberate abuse of voice and data networks. Furthermore, it is accepted that in these cases the perpetrator‘s intention is to completely avoid or at least reduce the charges that would legitimately have been charged for the services used. Telecommunications is an attractive target for fraudsters. In terms of volume, it is now measured in the billions worldwide. Service providers are being hit with fraudulent requests for service of over 85%. Recent highly sophisticated schemes are employed by organized crime using hackers and self-learning. Estimated that telecommunications fraud is more attractive than the drug 9|Page
  • trade.The Communications Fraud Control Association conducted a survey and determined that $35–$40 billion in losses is due to telecom fraud worldwide. Revenue losses due to fraud are approximately equal to revenue leakage within the systems and procedures of a company. Properly designed Revenue Assurance procedures extract data at every step of the revenue-earning chain, subjecting it to a rigorous integrity check. The traditional time-based and cost-based approaches of processing CDRs alone are rapidly becoming obsolete. They must be enhanced by methods of detecting fraud from multiple intelligence sources: services, content, broadband devices, service quality reports, etc. 10 | P a g e
  • 4. OBJECTIVE 11 | P a g e
  • 4.Objectives: 1. To study the frauds in telecom industry by different types of resources. 2. To study the various methods to detect and remove the frauds in telecom. Fig.2 Telecom fraud Triangle 12 | P a g e
  • 5. METHODOLOGY 13 | P a g e
  • 5.1 Methodology: Many organizations have done their survey on telecom frauds and also on the impact of frauds. They have done research on different types of frauds in telecom for all types of services like What are the frauds in? 1. 2. 3. 4. 5. Mobile communications Network security Data Storage Billing Infrastructure etc. Survey done by Associations like: 1. COMMUNICATIONS FRAUD CONTROL ASSOCIATION (CFCA) 2. NEUSTAR 3. NUMBER PORTABILITY ADMINISTRATION CENTER (NPAC) Neustar understands the landscape better than most. We enable four billion phone calls daily. We are trusted to support the world‘s largest number portability system holding more than 500 million telephone numbers. The Number Portability Administration Center (NPAC) and, our solutions provide scalable IP services to global communication service providers (CSPs). Neustar is at the heart of the world‘s largest telecom networks. It is this position that prompts many of the world‘s CSPs to turn to Neustar for help fighting telecom fraud. In response to the ongoing concern about telecom fraud, I recently published an e-book, ―What the Fraud? A look at Telecommunications Fraud and Its Impact,‖ where I discuss not only the top types of fraud affecting the industry, but also recommend best practices to combat and prevent fraud. 5.2 Telecom Fraud is Big Business Forty billion dollars is a lot of money. Losing $40 billion annually is unthinkable. Unfortunately, that is exactly what the Communications Fraud Control Association (CFCA) estimated global telecom fraud losses to be in their 2011 survey – $40 billion annually. 14 | P a g e
  • CSPs aren‘t the only ones bearing the burden. Businesses and individuals are also paying the price of telecommunications fraud, from high long-distance charges on hacked private branch exchanges (PBXs) to stolen identities. The bad news is that most phone hackers aren‘t simply random criminals. It is often skilled engineers who focus on vulnerable telecommunications systems. The good news is that most hackers can be easily thwarted with existing security technology and common-sense security practices, like strong passwords. Although telecommunications fraud encompasses a variety of illegal activities, the CFCA identifies the five costliest culprits as: PBX Hacking Identity Fraud International Revenue Share Fraud By-Pass Fraud Credit Card Fraud After studying of all these types of frauds many results has come out and frauds are decreasing year on year. 15 | P a g e
  • 6. RESULTS 16 | P a g e
  • 6.1 Results and Findings: 6.1.1 Impact of Frauds on the Telecom Sector The Growth Story: • The Indian telecom sector has been one of the fastest growing in the world. • Total telephone subscriber base reaching 601 million at the end of February 2010 from 582 million in January 2010, i.e. growth rate of 3.22% How the Growth Story is hindered: • Globally, fraud accounted for US $150 billion in lost revenue for telecom operators in 2009 • As per CFCA Fraud survey, 2009, India is emerging as one of the top 5 hotspots for telecom fraud • Fraud in telecom is second only to fraud in banking. It is growing exponentially and is expected to go up to 200 percent. • The immediate concern and issues that telecom operators foresee due to fraudulent activity is loss of revenue, estimation of the loss of revenue is in the range of 1-5 % of total revenue • Developing markets face higher revenue leakage than developed markets due to rapid growth and the fast pace of technological changes. Telecom Frauds have a multifarious impact on telecom companies, effecting operations as well as the revenue streams. 6.1.2 Some of the impacts of fraud for a telecom company are: • Direct financial loss through fraudulent call misuse (internal or external) • Missed cost savings opportunities through identification on un-needed circuits • Adverse publicity, damage to reputation and loss of customer confidence • Litigation and consequential financial loss 17 | P a g e
  • Loss of service and inability to dispense contract al obligations • Service inability contractual • Regulatory fines or increased regulatory supervision Telecom industry has had its share of losses due to sophisticated technology-aided frauds. As per the survey, globally, telecom frauds are estimated to cost the industry US$40 billion (which is roughly 2 percent of the global telecom revenues), despite significant efforts made by operators and their IT vendors to limit theft. "Operators' billing systems and network vulnerabilities are always key target areas for most fraudsters who exploit any weaknesses in these areas," the survey said. For Indian Telco‘s, fraud is one of the biggest risks confronting them, even as they battle it out in the market with their competitors and declining ARPU (average revenue per user), margin pressures, and other growth-related challenges. Fraud primarily occurs due to weak internal controls. In the case of the telecom industry, it also affects the customer. "For example, a Telco customer suffers when a fraudster, taking advantage of the poor identity checks at the Telco, is able to use the Telco network for voice/data connectivity and the bill has to be footed by the customer. This problem is worsened in case the Telco‘s have mobile money offerings. In such a situation, the customer also stands to lose his mobile money balance.‖ 6.2 Frauds Detection Techniques: 6.2.1 Tools and Techniques The common FMS techniques are rules based detection, rules discovery techniques, customer specificsand behavior specific, neural networks, audits, use of pins prior to placing a call (80 – 96% drop in fraud), setting limits on dialing capabilities (i.e. home area only) and roaming exchange services. The common FMS tools & technologies are radio frequency finger printing, authentication (symmetrical keys in phone and base station), digital systems with encryption, various probes, IP mediation and billing mediation systems can assist in collecting the data, PKI (public keys), SS7 surveillance, anti-virus and anti-trojan software, firewalls, encryption, water marking and digital signatures. 6.2.2 Social media monitoring: Fraudsters are using social media to communicate and misuse any existing gaps in telco networks--whether it is billing or other network related gaps. Further, some fraudsters even float Web sites luring the public with low or free call rates and other features. "To combat this, 18 | P a g e
  • companies should proactively monitor social media to identify and block any network-related gaps, and premium number ranges and sites that result in unintentional calling by subscribers only to find that they have been duped," it said. "Additionally, they could also proactively educate their subscribers to not fall prey to such schemes." 6.2.3 Setting up a fraud control unit (FCU): This utilizes data analytics to identify fraudulent trends. Telco‘s have started considering implementation of FCUs by setting up dedicated teams who use pre-defined data analytics routines to continuously monitor voluminous data--both within and outside their ERP--to proactively identify red flags that could prevent fraud-related losses. "This is specifically being used in Telco‘s to identify fraudulent patterns in the procurement process, the commissionpayout process, accounts payable process, etc.," the study said. 6.2.4 Utilizing sector-focused forensic experts: Telco‘s can deploy resources with specialist forensic skillsets to deal with telecom specific frauds/issues such as subscription frauds, unauthorized use of network, leakage of sensitive information, interconnect billing problems, misconduct in award, and execution of large scale outsourcing contracts, etc. 6.2.5 Strengthening controls around customer identification: Stringent know-your-customer (KYC) norms and de-dupe checks based on available patterns are being implemented to detect impersonators who intend to defraud the telco by assuming false identities and using the network without any intention to pay for it. According to the study, Telco‘s need to follow a two-pronged strategy to combat frauds. "This should consist of setting up a FCU to proactively monitor transactions in processes that are more vulnerable to manipulation, and simultaneously adopting a co-sourcing model with external 6.3 Fraud Detection Logics: 6.3.1 User profiles The main idea behind user profiling is that the past behavior of a user can be accumulated in order to construct a profile, or a ―user dictionary‖, or a ―user signature» of what might be the expected values of a user‘s behavior. This profile is a vector that contains single numerical summaries of some aspect of behavior or some kind of multivariate behavioral pattern. Future behavior of the user can then be compared with his profile in order to examine the consistency with it (normal behavior) or any deviation from his profile, which may imply fraudulent activity. The comparison can be made by means of statistical or artificial intelligence methods. Information theory criteria may also be applied. 19 | P a g e
  • 6.3.2 Applications of data mining techniques for fraud detection A. Neural Networks In order to test the ability of each profile to discriminate between legitimate usage and fraud, feed-forward neural networks (FF-NN) were used as classifiers. The problem is a supervised learning one with the task to adapt the weights so that the input-output mapping corresponds to the input-output pairs the teacher has provided. The evaluation of each classifier‘s performance was made by means of the corresponding Receiver Operating Characteristic (ROC) curve. A ROC curve is actually a graphical representation of the tradeoff between the true positive and the false positive rates for every possible cut off point that separates two overlapping distributions. Fig.3 ROC curve B. Decision trees: 20 | P a g e
  • Learning algorithms usually use a divide-and-conquer approach. The input space is incrementally divided using splits that maximize information gain or some other expression of the change in knowledge. This approach leads to tree-like data structures. The aim is to have leaves that are as pure as possible, i.e. contain objects of the same class. Appropriate purity measures should be used and the procedure will ideally lead to pure leafs. As the most common measure of this purity one uses the Kullback - Leibler distance, or relative entropy. This is used to express the information of the parent node minus the information of any possible division. Fig.4 Decision Tree 6.4 NGN fraud detection model Since all gateways of the converged networks are connected to the Internet, NGNs will inherit IP inherent security vulnerabilities. Due to their open architectures IP networks suffer from much 21 | P a g e
  • vulnerability and can be easily exploited for fraudulent actions such as IP spoofing (the use of a stolen IP address for impersonation) making it easier to conceal fraud. Fig.5 NGN fraud detection model 6.5 Cases related to Telecom frauds: Case.1 A group set up a bogus company offering Premium Rate Services for a helpline for a pop music. A group manufactured fake high value phone cards. Using fake cards, they dialed the Premium Rate Servicesline from phone kiosks. The Telco providing Premium Rate Serviceslost a few 100 thousand US$. Case .2 PBX dial-through can be used fraudulently by placing a call to a business then requesting to be transferred to "9-0" or some other outside toll number. 9 is normally an outside line and 0 then connects to the utility's operator.) The call appears to originate from the business (instead of the original fraudulent caller) and appears on the company's phone bill. Trickery (such as impersonation of installer and Telco personnel "testing the system") or bribery and collusion with dishonest employees inside the firm may be used to gain access. Case.3 MasterCard/Visa Scam A friend was called on the telephone thisweek from 'VISA' and I was called on Thursday from ‗MasterCard‘.It worked like this: Person calling says, 'this is Carl Patterson (anyname) and I'm calling from the Security and Fraud Department at VISA. My Badge number is 12460. Your card has been flagged for an unusualpurchase pattern, and I'm calling to verify. Did you purchase 22 | P a g e
  • anAnti-Telemarketing Company a device/any expensive item, for £497.99 from a marketing company based in (any town?)when you say 'No'. The caller continues with, 'Then we will be issuing a credit to your Account. This is a company we have been watching and the charges range from £297 to £497, just under the £500 purchase pattern that flags most cards. Before your next statement, the credit will be sent to (they give you your address), is that correct?' You say, 'Yes'. The caller continues. . 'I will be starting a fraud investigation. If you have any questions, you should call the 0800 number listed on your card and ask for Security. You will need to refer to this control number. They then give you a 6-digit number. 'Do you need me to read it again?' Caller then says he needs to verify 'you are in possession of your card' (this is where the scam takes place as up until now they have requested nothing!). They then ask you to turn your card over. There are 7numbers; the first 4 are 1234 (or whatever, as they have your number anyway).The next 3 are the security numbers that verify that you are in possessionof the card (these are the numbers they are really after as these are the numbers you use to make internet purchases to prove you have the card).. 'Read me the 3 numbers.' When you do he says 'That is correct. I justneeded to verify that the card has not been lost or stolen, and that youstill have your card. Do you have any other questions? Don't hesitate to call back if you do.' You actually say very little, and they never ask for or tell you the Card number. But after we were called on Wednesday, we telephoned back within 20 minutes to ask a question. Are we glad we did! The REAL VISA security department told us it was a scam and in the last 15 minutes a new purchase of £497.99 was put on our card. We made a real fraud report and closed the VISA card. What the scam wants is the 3-digit number and that once the charge goes through, they keep changing every few days. By the time you get your statement, you think the credit is coming, and then it's harder to actually file a fraud report. THE REAL VISA/MASTERCARD DEPARTMENT REINFORCED THE POINT THAT THEY WILL NEVER ASK FOR ANYTHING ABOUT THE CARD SINCE THEY ALREADY KNOW EVERYTHING About it, What makes this even more remarkable is that on Thursday I got a call From 'Jason Richardson of MasterCard' with a word for word repeat of the VISA Scam. This time I didn't let him finish. I hung up. We filed a police report (as instructed by VISA), and they said they are taking several of these reports daily and to tell friends, relatives and co-workers so please pass this on to your friends Case.4 Fraud continues to pound the U.S. telecom industry with little sign of letting up. Fraudulent use of networks and theft of services in all sectors of the telecom industry continue to grow between 10 percent and 12 percent annually. Though accurate fraud figures are nearly impossible to pin down, between $4 billion and $22 billion is lost each year, and up to 10 percent of a carrier‘s bottom line is lost to simple subscription fraud and other low-tech scams, such as when criminals 23 | P a g e
  • sign up for service using fake names. The use of fake addresses is also on the way up. Studies show that more fraud is being set up on legitimate accounts with good credit, indicating that outsiders are stealing ID information from good customers and opening fraudulent accounts they subsequently abuse and abandon. A bad address is the key indicator in 90 percent of fraud cases. According to telecom security firms, no sector of the industry is immune. Perpetrators, through fraudulent access to networks, can avoid paying for wireless service, steal and resell longdistance minutes to friends and strangers, or hijack a network device to send unsolicited commercial email or pornographic spam to unsuspecting end users. Case.5 Misuse of TRAI’s name for installation of tower It has been brought to the notice of Telecom Regulatory Authority of India that some companies / agencies/individuals are asking people to deposit amounts of money in their personal / companies account as Government Tax under Telecom Act 1972 or for clearing of the advanced payment etc. in lieu of leasing the premises of individuals for installation of Mobile tower. After collecting the money these companies/agencies/individuals become unreachable. These companies are issuing fake ‗No Objection Certification‘ permission for the Installation of Tower issued by Ministry of Communication and Information Technology. The Public at large is hereby informed that TRAI is not directly or indirectly involved in levying any tax/ fees on leasing the premises for installation of a mobile tower or for issuing any ‗No Objection Certification‘ for the purpose. A Mobile tower may be installed by either Telecom service provider or Infrastructure service provider (IP-I) as per their licensing/registration conditions. List of the Telecom service providers and IP-I service providers is available on Department of Telecommunications (DoT) website i.e.www.dot.gov.in. Any person or entity found fraudulently involved in such activity using TRAI name / logo / recommendations or national emblem is liable to be prosecuted under applicable law. Anyone dealing with such companies/agencies/individuals will be doing so at his/her own risk and TRAI cannot be held responsible for any loss or damages suffered directly or indirectly. 24 | P a g e
  • 7. DISCUSSION 25 | P a g e
  • 7.1 Discussion/Analysis: India's teledensity has improved from under 4% in March 2001 to around 76% by the end of March 2012. Cellular telephony continues to be the fastest growing segment in the Indian telecom industry. The mobile subscriber base (GSM and CDMA combined) has grown from under 2 m at the end of FY00 to touch 919 m at the end of March 2012 (average annual growth of nearly 64% during this 12 year period). Tariff reduction and decline in handset costs has helped the segment to gain in scale. The cellular segment is playing an important role in the industry by making itself available in the rural and semi urban areas where teledensity is the lowest. Not only for India but also world‘s teledensity is growing vastly and also frauds are also increasing, CSP‘s are continuously trying to reduce all these types of frauds and yes they have done well and decreases the frauds in a satisfied level. Global Telecom Fraud Increases By 0.21% From 2011, Still Near 5-Year Low October 1, 2013 - The CFCA recently announced the results of a comprehensive worldwide communications industry fraud loss survey. Experts estimate 2013 fraud losses at $46.3 billion (USD), up 15% from 2011. As a percent of global telecom revenues, fraud losses are approximately 2.09%—a 0.21% increase from 2011. The main reason for the relative increase in fraud is due to more fraudulent activity targeting the wireless industry. Highlights of the survey results include: Operators with 1-10 million subscribers reported more fraud incidents than any other group. The top five countries where fraud terminates are: Latvia, Gambia, Somalia, Sierra Leone, and Guinea. 92 % of operators surveyed said fraud losses had increased or stayed the same within their own companies—a 3% increase from 2011. The top five methods for committing fraud were:      o $5.22 Billion (USD) – Subscription Fraud $4.42 Billion (USD) – PBX Hacking $3.62 Billion (USD) – Account Take Over/ID Theft $3.62 Billion (USD) – VoIP Hacking $3.35 Billion (USD) – Dealer Fraud The top five types of fraud reported by operators were:   $6.11 Billion (USD) – Roaming Fraud $5.32 Billion (USD) – Wholesale Fraud 26 | P a g e
  •    $4.73 Billion (USD) – Premium Rate Service $3.55 Billion (USD) – Cable or Satellite $2.96 Billion (USD) – Hardware Reselling 7.2 Highlights of the survey results include: 34 respondents reported a total of $2.0 Billion (USD) in confirmed fraud losses at their companies. Operators with 1-10 Million and 50+ Million subscribers reported more incidents of fraud loss than smaller operators. The top 5 countries where fraud originates: United States, India, United Kingdom, Pakistan, and the Philippines. The top 5 countries where fraud terminates: Cuba, Somalia, Sierra Leone, Zimbabwe, Latvia 98 % of operators surveyed said that global fraud losses have increased or stayed the same an 8% increase from 2008. 89 % of operators surveyed said fraud losses had increased or stayed the same within their own Companies—a 13% increase from 2008. 27 | P a g e
  • 8. CONCLUSION 28 | P a g e
  • 8.1 Conclusion: Due to increase in process-oriented frauds and malicious frauds, it is important to implement an effective fraud management system • People who commit frauds are more creative and innovative than the operator in terms of figuring out how to use these new services, so innovative measures need to be taken on a regular basis. • Experienced, well-trained staff, access to current fraud data, in addition to a powerful fraud management system is important tools in the fraud prevention store. • By establishing a strong connection between marketing and fraud control, it is possible for carriers to offer new products and improve margins. • Telecom and other industry associations must lobby for a more stringent legal regime to be implemented with specific focus on telecom fraud. • Laws must permit companies to permanently blacklist employees found guilty of committing fraud as the same shall act as an important restrictive. ―The results of this survey confirm that telecom fraud remains a lucrative criminal business,‖ explained Jacob Howell, CCSP, CFCA Board Member and Director of Fraud Management Solutions for TEOCO Corporation. ―The CFCA is committed to raising awareness of this worldwide problem and to providing a forum for education and information-sharing among communications service providers. It‘s where communications professionals go to learn about these schemes and become effective at stopping them.‖ 8.2 Recommendations: CSP‘s should use various tools and techniques for the security of their network. CSP‘s are not applying the fraud detection techniques due to high investment. Regularly updating of software and hardware tools is necessary. Infosys co-created the revenue assurance process from scratch for the m-commerce product of the client. This included: 29 | P a g e
  • Risk assessment Identifying key performance indicators (KPIs) Controls monitoring and operations Content payment settlement with third-party vendors Testing for identifying service failure scenarios for the client‘s mobile app store 8.3 Benefits The operations set up by Infosys process around 1,000 cash back transactions per day. Infosys operations for the client‘s revenue assurance and fraud management have resulted in minimizing revenue leakages and led to a direct impact on the client‘s top line. More than 70 fraud cases amounting to INR 100,000 have been identified and closed while recovering 8 times worth the fraud amount. Revenue leakage of more than INR 3 million identified. INR 2 million of revenue saved. According to me all CSP‘s should apply this type of services provided by various operators for decreasing the frauds. 8.4 Limitations of Research: My research is totally based on the secondary data from the books, articles and internet. I went through the various articles on the internet from various organizations like ITU, TRAI and CFCA etc. and also referred from various white papers or research papers. If any data which is in This project is incorrect or affecting anyone‘s information I will be responsible for that. 30 | P a g e
  • 9.Bibliography:  OSS/BSS for Converged Telecommunication Networks – A practical approach by Prof. Rahul Wargad.  http://www.trai.gov.in/  TUSHAR CHAWLA , Partner - Economic Laws Practice  Constantinos S. Hilas Technological Educational Institute of Serres, Serres 621 24, Greece chilas@teiser.gr  S. H. Oh and W. S. Lee, ―An anomaly intrusion detection method byclustering normal user behavior,‖ Computers & Security, Vol. 22, No. 7,pp 596-612, 2003.  www.dot.gov.in/  Fraud OverviewTAF Regional Seminar on Costs andTariffs, 28-31 January 2008, Djibouti Peter Hoath peter.hoath@bt.com 31 | P a g e
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