How to Leverage Big Data to Help Finding Fraud Patterns & Revenue Assurance

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A joint presentation by Mobitel Sri Lanka and cVidya. Delivered on the Telecoms Fraud Management and Revenue Assurance World Summit 2014 in Singapore

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How to Leverage Big Data to Help Finding Fraud Patterns & Revenue Assurance

  1. 1. © 2014 – PROPRIETARY AND CONFIDENTIAL INFORMATION OF CVIDYA How to Leverage Big Data to Help Finding Fraud Patterns & Revenue Assurance  Sandagomi Jeewapadma, GM Enterprise Risk Management, Mobitel Sri Lanka  Amit Daniel, EVP Marketing & BD, cVidya
  2. 2. 2 About Mobitel - Sri Lanka Sri Lanka is a small island nation with a population of 21 million According to regulator statistics, by September 2013: 2013 Subscriber (Mn) Subscriber share Mobitel 5.0 25% Dialog 7.5 38% Etisalat 4.5 23% Other 2.8 14% Mobile Subscriber Market Share Number of Mobile Subscribers 20,234,698 Mobile Subscription per 100 people 98.78 There are five Mobile Operators, two of those have launched 4G LTE services to the market (Mobitel & Dialog) Mobitel, a wholly owned subsidiary of Sri Lanka Telecom, is a mobile and broadband service provider In Sri Lanka Mobitel 25% Dialog 38% Etisalat 23% Hutch - 5% Airtel - 9%
  3. 3. 3 A leading supplier of Revenue Analytics solutions to communications and digital service providers Founded: 2001 300 employees in 15 locations worldwide Deployed at 7 out of the 10 largest operators in the world 150 customers in 64 countries Processing 2.45 Billion subscribers in deployments globally Saving over $12 Billion to providers annual revenue Partnering with world leading vendors About cVidya
  4. 4. 4 BUSINESS ANALYTICAL LAYER BUSINESS GROWTH BUSINESS PROTECTION  Transformation Assurance  Fraud Management  Revenue Assurance  Marketing Analytics  Sales Performance Management BIG DATA PLATFORM Data collection Aggregation Enrichment DWH CRMMediation ERP IP&DPI Probes SwitchBilling Order & Provisioning DATA SOURCES Domain Expertise Education Center Professional Services Business Consulting Turning your DATA into VALUE
  5. 5. 5 Market Trends Source – GSMA Global cellular market trends and insight – Q4 2013
  6. 6. 66 An Entire New Ball Game
  7. 7. 7 Fraud & RA Units Must Process & Control Huge Amounts of Data  From info sources that did not exist before  Extensive use of external sources e.g., social networks  Need for cross analysis of non-associated sources of info  Including a new set of risks and threats to be identified & controlled  Entails a whole new terminology to master and areas to cover
  8. 8. 8 Mobitel Case Study
  9. 9. 9 New RA & Fraud Challenges  Industry definitions are rapidly changing  New complex data services are being populated across operator service offerings  Shift from data pipe provider role to content integrator position  Complex partnerships on SLA-driven and revenue-sharing basis  New technologies and business models (LTE, Mobile Money, rich communication services etc.)  Proactive identification of revenue leakages, risk mitigation, and fraud management is essential  New set of skills and capacity required for RA & FM staff
  10. 10. 10 Process in Selecting RA & Fraud Solution  Mobitel RA & FM function was newly set up and required visibility & control of the entire revenue map in terms of revenue leakages and fraudulent activities  Creating internal capacity was also a mandatory requirement, to be executed in parallel  Mobitel invited leading players in the domain (based on Gartner’s Magic Quadrant)  Comprehensive technical evaluation process, qualified by cVidya for RA & FM solution  Inclusion of organizational key risks & revenue sources and defining correct control points & KPIs are essential at the beginning of the project
  11. 11. 11 Mobile Money – Mobitel  New partnership with banks, merchants  New regulatory authorities (central banks)  New risks & controls on KYC and money laundering threats
  12. 12. 12 It’s Complicated… So Much to Check! GGSNIPSGSN HLR BSC/RNC MSC SMSC Gateway Router Service Platform & Portal AAA RADIUS Mobile Network Customers Agents Merchants Bank ATMs Agents Merchants Customers Agents Merchants CRM Billing: • Postpaid • Prepaid Reports • Banks • Agents • Merchants • Others PSDN www Secured Network
  13. 13. 13 LTE Challenges  Consumption or service based is much more complex than transport based charging ̶ New service requirements with Shorter Time to market ̶ Complex Price plans  Quality Of Service based rating create new challenges for verification and re-performance  Multiple charging policies in the same session So…  Do we measure usage correctly?  Are we applying the appropriate policy?  Are we charging according to the appropriate policy? 13
  14. 14. 14 It’s Complicated… So Much to Check! EPC e-NodeB e-NodeB RAN S-GW MME P-GW HSS PCRF SPR ePDG PDF CSCF AS MGW IMS MGCF OFCG OCG Wholesale Billing CRM Postpaid Billing e-NodeB www PSTN/PLMN PCEF Service Configuration Portal Configuration Usage RBA
  15. 15. 15 Big Data Analytics for Fraud Management Using Deep Packet Inspection (DPI) and Pattern Matching is highly effective for:  Identifying malicious calls & applications in real time  Detecting abnormal service consumption  Detecting subscriber frauds  Mobile Money related Frauds (Phishing attacks)  Detecting Tethering of Smart Phones  Detecting Proxy Services  Achieving visibility on OTT services
  16. 16. 16 Using DPI to identify Fraud  Tethering performed in a commercial manner is considered to be an abusive operation and impacts the telecom operator in several ways: – Affecting the network planning and causing overloads – Could force the operator to invest in expanding his network – Harming the user experience of other legitimate users www ISP Backbone Legitimate connections Non-Legitimate connections Abusive tethering operation
  17. 17. 17 Quicker, Richer, Better  Cross analysis of non-correlated sources  Accurate, fast & intelligent insights  Reduction of time & investigation resources  Larger retention - storing for longer periods of time  Enabling RA & Fraud units to provide services and leverage capabilities for other non-fraud activities When Big Data Meets RA & Fraud
  18. 18. THANK YOU! www.cvidya.com

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