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These are the voyages of cVidya in its quest to battle big data fraud
and to boldly go where no fraud solution has gone be...
2
Key Facts
Canada
Brazil
Guatemala
South Africa
Israel
Spain
UK Ukraine
India
Singapore
Bulgaria
USA Macedonia
 cVidya i...
3
In 60 Seconds
Consumption
Payments
Social
Interactions
Location
Retailing
Web Browsing
Apps Usage
60% of online data com...
4
Interpreting Big Data Hype
When new technologies make bold promises, how do you separate the hype
from what's commercial...
55
Big Data Analytics
"Data is widely available,
what is scarce is the ability
to extract wisdom"
Hal Varian, Chief Econom...
66
What Do We Provide?…
cVidya provides an analytical platform embedded with
best practices use cases for different purpos...
7
New Fraud Challenges
The telecom market is in a dramatic transition
phase that influences the fraud department’s
challen...
8
 According to the latest CFCA report
(published in 2013) there is a 15%
increase in fraud losses (compared
to 2011)
 P...
9
Operators need to balance between getting to
know the new and emerging types of fraud,
and coping with the traditional t...
1010
Examples of new threats
and prevention methods
11
Fraud detection and prevention through DPI
− DPI reveals new areas that up till now weren’t covered - allowing for dete...
12
Abnormal Patterns Analysis
13
Abnormal Patterns Analysis
 The Issue
− Fraudsters commit mobile / e-commerce fraud while accessing
websites from thei...
14
Abnormal Patterns Analysis – Use Case
 The system characterizes what is defined as “reasonable” usage
patterns of a no...
15
Proxy Fraud
16
Proxy Fraud
Issue  Disguising premium data traffic to avoid
additional payments to telcos
Need
 Telcos are moving to ...
17
Proxy Fraud (Cont.)
 Users connect to proxy services (located outside / beyond the
ISP network) that allow them to con...
18
IP PBX Hacking Detection
19
IP PBX Hacking Detection
 The Issue
− Toll fraud is being performed by compromising corporate IP PBX
− Recent CFCA-rep...
 Massive parallel processing
P = Performance
 Scalability & linear growth
 Longer retention time
 Shorter processing d...
21
What is needed
22
 A new initiative of the TMF - Unified Analytics
Big Data Repository (ABDR)
 Supports multiple use-case & analytics s...
23
Real-time
Event Queuing
Big Data Architecture
Unified
Analytical
Layer
Data
Node
Data
Node
Ad-Hoc
Reports
Real-time Str...
24
Why cVidya
cVidya is leading the way with Big Data
Expanded RA, Fraud and Analytics products to
support big data based ...
THANK YOU!
www.cvidya.com
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The Great Unknown - How can operators leverage big data to prevent future revenue losses in the data based world

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cVidya's presentation in the Revenue Assurance & Fraud Prevention event in Rio de Janeiro, September 28-29, 2015.

Publié dans : Données & analyses
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The Great Unknown - How can operators leverage big data to prevent future revenue losses in the data based world

  1. 1. These are the voyages of cVidya in its quest to battle big data fraud and to boldly go where no fraud solution has gone before
  2. 2. 2 Key Facts Canada Brazil Guatemala South Africa Israel Spain UK Ukraine India Singapore Bulgaria USA Macedonia  cVidya is a leading supplier of Analytics solutions to communications and digital service providers. cVidya’s big data technology platform and analytical applications enable operators to optimize profits and enhance decision-making.  160+ customers in 64 countries  300 Employees  Founded 2001 Leading Provider of Analytics Solutions  Business success with proactive revenue assurance (2013)  TM Forum Leadership (2012)  Partner Network Specialized Award (2012)  Revenue Analytics & Fraud Mgmt leader (2012)  Revenue Management leader (2012)  Most innovative vendor (2012)  #1 Revenue Management Global Market (2011)  Serving 7 out of the 10 largest operators in the world Global Footprint – 13 locations worldwide Industry RecognitionCustomer Base & Partnerships  Partnership with leading global vendors
  3. 3. 3 In 60 Seconds Consumption Payments Social Interactions Location Retailing Web Browsing Apps Usage 60% of online data comes from mobile
  4. 4. 4 Interpreting Big Data Hype When new technologies make bold promises, how do you separate the hype from what's commercially viable? And when will such claims pay off, if at all?
  5. 5. 55 Big Data Analytics "Data is widely available, what is scarce is the ability to extract wisdom" Hal Varian, Chief Economist, Google
  6. 6. 66 What Do We Provide?… cVidya provides an analytical platform embedded with best practices use cases for different purposes such as RA, FM, Marketing Analytics & Data Monetization - all using industry standard big data environments
  7. 7. 7 New Fraud Challenges The telecom market is in a dramatic transition phase that influences the fraud department’s challenges and activities  What new types of risks are out there?  What needs to be monitored?  Using what tools?  How do we support the enormous amount of data and find the “needle in the haystack?” 7
  8. 8. 8  According to the latest CFCA report (published in 2013) there is a 15% increase in fraud losses (compared to 2011)  PBX hacking, PRS/IRSF, bypass and subscription fraud still cause the industry damages of billions of $ annually Traditional Fraud is Still a Major Pain
  9. 9. 9 Operators need to balance between getting to know the new and emerging types of fraud, and coping with the traditional types that still cause them major damages 9
  10. 10. 1010 Examples of new threats and prevention methods
  11. 11. 11 Fraud detection and prevention through DPI − DPI reveals new areas that up till now weren’t covered - allowing for detection of new types of fraud types and service abuse − The amount of DPI transactions is tremendous! − BD capabilities are a must when dealing with DPI information  Some examples of fraud scenarios which can only be detected using DPI: − Abnormal usage Analysis − Proxy Fraud - Disguising premium data traffic to avoid additional payments − IP PBX hacking detection - Toll fraud conducted by fraudsters by compromising corporate IP PBX − Tethering - Revenue loss to the operator due to sharing of a single Internet connection by several devices Case:
  12. 12. 12 Abnormal Patterns Analysis
  13. 13. 13 Abnormal Patterns Analysis  The Issue − Fraudsters commit mobile / e-commerce fraud while accessing websites from their smartphones / tablets − Mobile / e-commerce companies can only detect fraud attempts on their own websites  The Solution − A DPI-based solution that enables telcos to monitor and detect the OTT activity of the mobile data user − The solution looks for suspicious behavior in the entire network  Business Value − Telcos can offer / share the insights gained from monitoring activity − Providing mobile / e-commerce companies with insight into fraud committed across the network − Enables mobile / e-commerce companies to reduce their exposure to fraud
  14. 14. 14 Abnormal Patterns Analysis – Use Case  The system characterizes what is defined as “reasonable” usage patterns of a normal user in the network and alerts abnormal behavior  Normal user browses several websites throughout the day, attackers will most probably access only the targeted website)  Number of accesses to specific websites should be reasonable (Multiple accesses to eBay or Amazon are suspicious)  Sequential destination port numbers  A “normal” mobile data user / subscriber profile is based on the DPI component that reveals the applications and services being used by the user
  15. 15. 15 Proxy Fraud
  16. 16. 16 Proxy Fraud Issue  Disguising premium data traffic to avoid additional payments to telcos Need  Telcos are moving to advanced billing schemes  Detects users that are trying to bypass the billing processes / avoid additional charges solution  A DPI-based solution that enables telcos to detect such disguised traffic Business Value  Telcos can recover lost revenues
  17. 17. 17 Proxy Fraud (Cont.)  Users connect to proxy services (located outside / beyond the ISP network) that allow them to connect to the requested website preventing the ISP from monitoring and billing this activity.  By using DPI the fraud system can use SSL protocol to detect disguise proxy activity.  The DPI record demonstrates using YouTube using an encrypted protocol and destination IP which doesn’t belong to YouTube subnet
  18. 18. 18 IP PBX Hacking Detection
  19. 19. 19 IP PBX Hacking Detection  The Issue − Toll fraud is being performed by compromising corporate IP PBX − Recent CFCA-reports estimate fraud damage at > $4.96B per annum  The Need − Organizations are legally liable for fraudulent traffic in their networks and must proactively monitor their PBX activities and detect hacking attempts  The Solution − An IP probe / DPI device located within the corporate LAN − The device monitors abnormal PBX port scanning activity  Business Value − Detects the hacking attempts effectively − Performs corrective actions to remove all malicious devices within the network − Prevents PBX hacks / toll fraud
  20. 20.  Massive parallel processing P = Performance  Scalability & linear growth  Longer retention time  Shorter processing durations  Wider back office processing & analysis C = Cost  Reduction in HW & SW licenses  Commodity hardware & storage  Better planning & targeting  High availability  Historical & Real-time data C = Coverage  Verticals & LOBs  Multiple sources & systems  Multiple departments  Structured & Unstructured  Centralized platform  Multiple user types 20 Big Data Analytics Benefits - cVidya Big Data Analytics Platform BenefitsC2P
  21. 21. 21 What is needed
  22. 22. 22  A new initiative of the TMF - Unified Analytics Big Data Repository (ABDR)  Supports multiple use-case & analytics systems  Data repository of loosely coupled data entities  Standard definition using data dictionary  Benefits  Avoiding data replications  Saving in ETL costs & time  Faster time to implement new use-cases  Open platform ABDR
  23. 23. 23 Real-time Event Queuing Big Data Architecture Unified Analytical Layer Data Node Data Node Ad-Hoc Reports Real-time Streaming Component Data Node Map Reduce Data Node Business Widgets Case Management … cVidya’s Unified Analytics Business& OperationalDashboards Premodeled CustomerData Applications Columnar Data Base (Optional) MoneyMap® Plus| FraudView® Plus | Enrich™ | Engage™ cVidya’s Big Data Platform Real-time Comparison Advanced AnalyticalModels All Data Sources CRM Mediation ERP IP&DPI Probes Switch Billing DWH Order & Provisioning cVidya’s ETL
  24. 24. 24 Why cVidya cVidya is leading the way with Big Data Expanded RA, Fraud and Analytics products to support big data based infrastructures − Leveraged the latest Big Data technologies to enable enormous data volume processing and advanced analytics − Leading the TMF ABDR project - Analytics Big Data Repository
  25. 25. THANK YOU! www.cvidya.com

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