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

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

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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%

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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

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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
Market Trends
Source – GSMA Global cellular market trends and insight – Q4 2013

6. 66
An Entire New Ball Game

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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

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Mobitel Case Study

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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

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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

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Mobile Money – Mobitel
 New partnership with banks, merchants
 New regulatory authorities (central banks)
 New risks & controls on KYC and money laundering threats

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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

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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

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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

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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

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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

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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. THANK YOU!
www.cvidya.com