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

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 Recognition Customer Base & Partnerships

Partnership with leading global vendors

3

In 60 Seconds

Consumption

Payments

Social

Interactions

Location

Retailing

Web Browsing

Apps Usage

60% of online data comes from mobile

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

Big Data Analytics

"Data is widely available, what is scarce is the ability

to extract wisdom" Hal Varian, Chief Economist, Google

6 6

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

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

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

$5.22 B – Subscription Fraud $4.42 B – PBX Hacking $3.62 B – Account Take Over / Identity

Theft $3.62 B – VoIP Hacking $3.35 B – Dealer Fraud

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

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 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

Abnormal Patterns Analysis

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

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

Proxy Fraud

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

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

IP PBX Hacking Detection

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

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 Benefits C2P

21

What is needed

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

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 & Operational Dashboards

Premodeled Customer Data

Applications

Columnar Data Base (Optional)

MoneyMap® Plus| FraudView® Plus | Enrich™ | Engage™

cVidya’s Big Data Platform

Real-time Comparison

Advanced Analytical Models

All Data Sources CRM

Mediation ERP

IP&DPI Probes Switch

Billing DWH Order & Provisioning

cVidya’s ETL

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

THANK YOU! www.cvidya.com