© 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

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

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

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

CRM Mediation

ERP

IP&DPI Probes

Switch Billing

Order & Provisioning

DATA SOURCES

Domain Expertise

Education Center

Professional Services

Business Consulting

Turning your DATA into VALUE

5

Market Trends

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

6 6

An Entire New Ball Game

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

Mobitel Case Study

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

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

Mobile Money – Mobitel

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

12

It’s Complicated… So Much to Check!

GGSN IP SGSN

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

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

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

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

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

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

THANK YOU! www.cvidya.com