SlideShare ist ein Scribd-Unternehmen logo
1 von 25
Downloaden Sie, um offline zu lesen
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 RecognitionCustomer 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?
55
Big Data Analytics
"Data is widely available,
what is scarce is the ability
to extract wisdom"
Hal Varian, Chief Economist, Google
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
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
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
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 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 BenefitsC2P
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&
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
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

Weitere ähnliche Inhalte

Was ist angesagt?

How to Leverage Big Data to Help Finding Fraud Patterns & Revenue Assurance
How to Leverage Big Data to Help Finding Fraud Patterns & Revenue AssuranceHow to Leverage Big Data to Help Finding Fraud Patterns & Revenue Assurance
How to Leverage Big Data to Help Finding Fraud Patterns & Revenue AssurancecVidya Networks
 
Tech M White Paper Revenue Assurance D0 9 180612 (1)
Tech M White Paper Revenue Assurance D0 9 180612 (1)Tech M White Paper Revenue Assurance D0 9 180612 (1)
Tech M White Paper Revenue Assurance D0 9 180612 (1)aprasoon
 
Telecom reporting training
Telecom reporting trainingTelecom reporting training
Telecom reporting trainingntel
 
Zen Infographic - Revenue Assurance Automation
Zen Infographic - Revenue Assurance AutomationZen Infographic - Revenue Assurance Automation
Zen Infographic - Revenue Assurance AutomationSubex
 
Revenue assurance in telecom
Revenue assurance in telecomRevenue assurance in telecom
Revenue assurance in telecomcVidya Networks
 
cVidya RA for Electric Utilities - RA Forum Conference
cVidya RA for Electric Utilities - RA Forum ConferencecVidya RA for Electric Utilities - RA Forum Conference
cVidya RA for Electric Utilities - RA Forum ConferencecVidya Networks
 
Revenue Assurance, Fraud Reduction and Cost Managment in Telecoms Conference
Revenue Assurance, Fraud Reduction and Cost Managment in Telecoms ConferenceRevenue Assurance, Fraud Reduction and Cost Managment in Telecoms Conference
Revenue Assurance, Fraud Reduction and Cost Managment in Telecoms ConferenceArena International
 
2014 march falcon business fraud classification model (3attendees)
2014 march falcon business fraud classification model (3attendees)2014 march falcon business fraud classification model (3attendees)
2014 march falcon business fraud classification model (3attendees)jcsobreira
 
Sharpening revenue assurance_july 2015
Sharpening revenue assurance_july 2015Sharpening revenue assurance_july 2015
Sharpening revenue assurance_july 2015Silas Musakali
 
How to Prevent Telecom Fraud in Real-Time
How to Prevent Telecom Fraud in Real-TimeHow to Prevent Telecom Fraud in Real-Time
How to Prevent Telecom Fraud in Real-TimeAlan Percy
 
Build competitive edge through differentiated customer experience
Build competitive edge  through differentiated  customer experienceBuild competitive edge  through differentiated  customer experience
Build competitive edge through differentiated customer experiencegiridharseorank
 
Enhanced B2B customer experience with UniServe NXT platform
Enhanced B2B customer experience with UniServe NXT platformEnhanced B2B customer experience with UniServe NXT platform
Enhanced B2B customer experience with UniServe NXT platformIntense Technologies Limited
 
CTRM in the Cloud – Research and Report
CTRM in the Cloud – Research and ReportCTRM in the Cloud – Research and Report
CTRM in the Cloud – Research and ReportCTRM Center
 
TM Forum Fraud Management Group Activities - Presented at TM Forum's Manageme...
TM Forum Fraud Management Group Activities - Presented at TM Forum's Manageme...TM Forum Fraud Management Group Activities - Presented at TM Forum's Manageme...
TM Forum Fraud Management Group Activities - Presented at TM Forum's Manageme...cVidya Networks
 
M Cardp2p
M Cardp2pM Cardp2p
M Cardp2pTom72
 
Managed IT as a Service White Paper
Managed IT as a Service White PaperManaged IT as a Service White Paper
Managed IT as a Service White PaperEdel Creely
 
Pricing and commissions Webinar English
Pricing and commissions Webinar EnglishPricing and commissions Webinar English
Pricing and commissions Webinar EnglishCamilo Tellez
 
Information technology
Information technologyInformation technology
Information technologyRoy Thomas
 

Was ist angesagt? (20)

How to Leverage Big Data to Help Finding Fraud Patterns & Revenue Assurance
How to Leverage Big Data to Help Finding Fraud Patterns & Revenue AssuranceHow to Leverage Big Data to Help Finding Fraud Patterns & Revenue Assurance
How to Leverage Big Data to Help Finding Fraud Patterns & Revenue Assurance
 
Tech M White Paper Revenue Assurance D0 9 180612 (1)
Tech M White Paper Revenue Assurance D0 9 180612 (1)Tech M White Paper Revenue Assurance D0 9 180612 (1)
Tech M White Paper Revenue Assurance D0 9 180612 (1)
 
Telecom reporting training
Telecom reporting trainingTelecom reporting training
Telecom reporting training
 
The future of r av3
The future of r av3The future of r av3
The future of r av3
 
Fraud in Telecoms
Fraud in TelecomsFraud in Telecoms
Fraud in Telecoms
 
Zen Infographic - Revenue Assurance Automation
Zen Infographic - Revenue Assurance AutomationZen Infographic - Revenue Assurance Automation
Zen Infographic - Revenue Assurance Automation
 
Revenue assurance in telecom
Revenue assurance in telecomRevenue assurance in telecom
Revenue assurance in telecom
 
cVidya RA for Electric Utilities - RA Forum Conference
cVidya RA for Electric Utilities - RA Forum ConferencecVidya RA for Electric Utilities - RA Forum Conference
cVidya RA for Electric Utilities - RA Forum Conference
 
Revenue Assurance, Fraud Reduction and Cost Managment in Telecoms Conference
Revenue Assurance, Fraud Reduction and Cost Managment in Telecoms ConferenceRevenue Assurance, Fraud Reduction and Cost Managment in Telecoms Conference
Revenue Assurance, Fraud Reduction and Cost Managment in Telecoms Conference
 
2014 march falcon business fraud classification model (3attendees)
2014 march falcon business fraud classification model (3attendees)2014 march falcon business fraud classification model (3attendees)
2014 march falcon business fraud classification model (3attendees)
 
Sharpening revenue assurance_july 2015
Sharpening revenue assurance_july 2015Sharpening revenue assurance_july 2015
Sharpening revenue assurance_july 2015
 
How to Prevent Telecom Fraud in Real-Time
How to Prevent Telecom Fraud in Real-TimeHow to Prevent Telecom Fraud in Real-Time
How to Prevent Telecom Fraud in Real-Time
 
Build competitive edge through differentiated customer experience
Build competitive edge  through differentiated  customer experienceBuild competitive edge  through differentiated  customer experience
Build competitive edge through differentiated customer experience
 
Enhanced B2B customer experience with UniServe NXT platform
Enhanced B2B customer experience with UniServe NXT platformEnhanced B2B customer experience with UniServe NXT platform
Enhanced B2B customer experience with UniServe NXT platform
 
CTRM in the Cloud – Research and Report
CTRM in the Cloud – Research and ReportCTRM in the Cloud – Research and Report
CTRM in the Cloud – Research and Report
 
TM Forum Fraud Management Group Activities - Presented at TM Forum's Manageme...
TM Forum Fraud Management Group Activities - Presented at TM Forum's Manageme...TM Forum Fraud Management Group Activities - Presented at TM Forum's Manageme...
TM Forum Fraud Management Group Activities - Presented at TM Forum's Manageme...
 
M Cardp2p
M Cardp2pM Cardp2p
M Cardp2p
 
Managed IT as a Service White Paper
Managed IT as a Service White PaperManaged IT as a Service White Paper
Managed IT as a Service White Paper
 
Pricing and commissions Webinar English
Pricing and commissions Webinar EnglishPricing and commissions Webinar English
Pricing and commissions Webinar English
 
Information technology
Information technologyInformation technology
Information technology
 

Andere mochten auch

Revenue Assurance Industry Update - Webinar by Dr. Gadi Solotorevsky, cVidya'...
Revenue Assurance Industry Update - Webinar by Dr. Gadi Solotorevsky, cVidya'...Revenue Assurance Industry Update - Webinar by Dr. Gadi Solotorevsky, cVidya'...
Revenue Assurance Industry Update - Webinar by Dr. Gadi Solotorevsky, cVidya'...cVidya Networks
 
Identity Fraud Protection Using Big Data Analytics - StampedeCon 2015
Identity Fraud Protection Using Big Data Analytics - StampedeCon 2015Identity Fraud Protection Using Big Data Analytics - StampedeCon 2015
Identity Fraud Protection Using Big Data Analytics - StampedeCon 2015StampedeCon
 
Ten Commandments for Tackling Fraud: The Role of Big Data and Predictive Anal...
Ten Commandments for Tackling Fraud: The Role of Big Data and Predictive Anal...Ten Commandments for Tackling Fraud: The Role of Big Data and Predictive Anal...
Ten Commandments for Tackling Fraud: The Role of Big Data and Predictive Anal...CA Technologies
 
Hacking PBXs for international revenue share fraud
Hacking PBXs for international revenue share fraudHacking PBXs for international revenue share fraud
Hacking PBXs for international revenue share fraudcVidya Networks
 
TM Forum Presentation with cVidya and Alltel
TM Forum Presentation with cVidya and AlltelTM Forum Presentation with cVidya and Alltel
TM Forum Presentation with cVidya and AlltelcVidya Networks
 
Masters thesis - Fraud & Big Data
Masters thesis - Fraud & Big DataMasters thesis - Fraud & Big Data
Masters thesis - Fraud & Big DataStephanie Canovas
 
Training Report at Mobitel
Training Report at MobitelTraining Report at Mobitel
Training Report at MobitelDinusha Dilanka
 
Webinar: Using Big Data Technology in Fraud Prevention
Webinar: Using Big Data Technology in Fraud PreventionWebinar: Using Big Data Technology in Fraud Prevention
Webinar: Using Big Data Technology in Fraud PreventionNetGuardians
 
Big Data Applications & Analytics Motivation: Big Data and the Cloud; Centerp...
Big Data Applications & Analytics Motivation: Big Data and the Cloud; Centerp...Big Data Applications & Analytics Motivation: Big Data and the Cloud; Centerp...
Big Data Applications & Analytics Motivation: Big Data and the Cloud; Centerp...Geoffrey Fox
 
Online Fraud Detection Using Big Data Analytics Webinar
Online Fraud Detection Using Big Data Analytics WebinarOnline Fraud Detection Using Big Data Analytics Webinar
Online Fraud Detection Using Big Data Analytics WebinarDatameer
 
TM Forum Case study handbook_2013
TM Forum Case study handbook_2013TM Forum Case study handbook_2013
TM Forum Case study handbook_2013Locutus1of3
 
ICT创新驱动新产业革命-industrie 4.0 20151117
ICT创新驱动新产业革命-industrie 4.0   20151117ICT创新驱动新产业革命-industrie 4.0   20151117
ICT创新驱动新产业革命-industrie 4.0 20151117Aron Shannon
 
Dialog telekom limite1
Dialog telekom limite1Dialog telekom limite1
Dialog telekom limite1niroshiniz
 
Marketing report mobile service industry (1)
Marketing report mobile service industry (1)Marketing report mobile service industry (1)
Marketing report mobile service industry (1)cherath
 
SWOT Analysis on Dialog PLC
SWOT Analysis on Dialog PLCSWOT Analysis on Dialog PLC
SWOT Analysis on Dialog PLCJetwing Travels
 
Hadoop BIG Data - Fraud Detection with Real-Time Analytics
Hadoop BIG Data - Fraud Detection with Real-Time AnalyticsHadoop BIG Data - Fraud Detection with Real-Time Analytics
Hadoop BIG Data - Fraud Detection with Real-Time Analyticshkbhadraa
 
Medical University of South Carolina: Using Big Data and Predictive Analytics...
Medical University of South Carolina: Using Big Data and Predictive Analytics...Medical University of South Carolina: Using Big Data and Predictive Analytics...
Medical University of South Carolina: Using Big Data and Predictive Analytics...Seeling Cheung
 
Fiducia & GAD IT AG: From Fraud Detection to Big Data Platform: Bringing Hado...
Fiducia & GAD IT AG: From Fraud Detection to Big Data Platform: Bringing Hado...Fiducia & GAD IT AG: From Fraud Detection to Big Data Platform: Bringing Hado...
Fiducia & GAD IT AG: From Fraud Detection to Big Data Platform: Bringing Hado...Seeling Cheung
 

Andere mochten auch (20)

Revenue Assurance Industry Update - Webinar by Dr. Gadi Solotorevsky, cVidya'...
Revenue Assurance Industry Update - Webinar by Dr. Gadi Solotorevsky, cVidya'...Revenue Assurance Industry Update - Webinar by Dr. Gadi Solotorevsky, cVidya'...
Revenue Assurance Industry Update - Webinar by Dr. Gadi Solotorevsky, cVidya'...
 
Aamod_Chandra
Aamod_ChandraAamod_Chandra
Aamod_Chandra
 
Identity Fraud Protection Using Big Data Analytics - StampedeCon 2015
Identity Fraud Protection Using Big Data Analytics - StampedeCon 2015Identity Fraud Protection Using Big Data Analytics - StampedeCon 2015
Identity Fraud Protection Using Big Data Analytics - StampedeCon 2015
 
Ten Commandments for Tackling Fraud: The Role of Big Data and Predictive Anal...
Ten Commandments for Tackling Fraud: The Role of Big Data and Predictive Anal...Ten Commandments for Tackling Fraud: The Role of Big Data and Predictive Anal...
Ten Commandments for Tackling Fraud: The Role of Big Data and Predictive Anal...
 
Motivation for big data
Motivation for big dataMotivation for big data
Motivation for big data
 
Hacking PBXs for international revenue share fraud
Hacking PBXs for international revenue share fraudHacking PBXs for international revenue share fraud
Hacking PBXs for international revenue share fraud
 
TM Forum Presentation with cVidya and Alltel
TM Forum Presentation with cVidya and AlltelTM Forum Presentation with cVidya and Alltel
TM Forum Presentation with cVidya and Alltel
 
Masters thesis - Fraud & Big Data
Masters thesis - Fraud & Big DataMasters thesis - Fraud & Big Data
Masters thesis - Fraud & Big Data
 
Training Report at Mobitel
Training Report at MobitelTraining Report at Mobitel
Training Report at Mobitel
 
Webinar: Using Big Data Technology in Fraud Prevention
Webinar: Using Big Data Technology in Fraud PreventionWebinar: Using Big Data Technology in Fraud Prevention
Webinar: Using Big Data Technology in Fraud Prevention
 
Big Data Applications & Analytics Motivation: Big Data and the Cloud; Centerp...
Big Data Applications & Analytics Motivation: Big Data and the Cloud; Centerp...Big Data Applications & Analytics Motivation: Big Data and the Cloud; Centerp...
Big Data Applications & Analytics Motivation: Big Data and the Cloud; Centerp...
 
Online Fraud Detection Using Big Data Analytics Webinar
Online Fraud Detection Using Big Data Analytics WebinarOnline Fraud Detection Using Big Data Analytics Webinar
Online Fraud Detection Using Big Data Analytics Webinar
 
TM Forum Case study handbook_2013
TM Forum Case study handbook_2013TM Forum Case study handbook_2013
TM Forum Case study handbook_2013
 
ICT创新驱动新产业革命-industrie 4.0 20151117
ICT创新驱动新产业革命-industrie 4.0   20151117ICT创新驱动新产业革命-industrie 4.0   20151117
ICT创新驱动新产业革命-industrie 4.0 20151117
 
Dialog telekom limite1
Dialog telekom limite1Dialog telekom limite1
Dialog telekom limite1
 
Marketing report mobile service industry (1)
Marketing report mobile service industry (1)Marketing report mobile service industry (1)
Marketing report mobile service industry (1)
 
SWOT Analysis on Dialog PLC
SWOT Analysis on Dialog PLCSWOT Analysis on Dialog PLC
SWOT Analysis on Dialog PLC
 
Hadoop BIG Data - Fraud Detection with Real-Time Analytics
Hadoop BIG Data - Fraud Detection with Real-Time AnalyticsHadoop BIG Data - Fraud Detection with Real-Time Analytics
Hadoop BIG Data - Fraud Detection with Real-Time Analytics
 
Medical University of South Carolina: Using Big Data and Predictive Analytics...
Medical University of South Carolina: Using Big Data and Predictive Analytics...Medical University of South Carolina: Using Big Data and Predictive Analytics...
Medical University of South Carolina: Using Big Data and Predictive Analytics...
 
Fiducia & GAD IT AG: From Fraud Detection to Big Data Platform: Bringing Hado...
Fiducia & GAD IT AG: From Fraud Detection to Big Data Platform: Bringing Hado...Fiducia & GAD IT AG: From Fraud Detection to Big Data Platform: Bringing Hado...
Fiducia & GAD IT AG: From Fraud Detection to Big Data Platform: Bringing Hado...
 

Ähnlich wie The Great Unknown - How can operators leverage big data to prevent future revenue losses in the data based world

Shift at work of Fraud Management
Shift at work of Fraud ManagementShift at work of Fraud Management
Shift at work of Fraud ManagementcVidya Networks
 
Shift at work of fraud management
Shift at work of fraud managementShift at work of fraud management
Shift at work of fraud managementcVidya Networks
 
Ai and machine learning help detect, predict and prevent fraud - IBM Watson ...
Ai and machine learning help detect, predict and prevent fraud -  IBM Watson ...Ai and machine learning help detect, predict and prevent fraud -  IBM Watson ...
Ai and machine learning help detect, predict and prevent fraud - IBM Watson ...Institute of Contemporary Sciences
 
Big Data & Analytics, Peter Jönsson
Big Data & Analytics, Peter JönssonBig Data & Analytics, Peter Jönsson
Big Data & Analytics, Peter JönssonIBM Danmark
 
Detecting Opportunities and Threats with Complex Event Processing: Case St...
Detecting Opportunities and Threats with Complex Event Processing: Case St...Detecting Opportunities and Threats with Complex Event Processing: Case St...
Detecting Opportunities and Threats with Complex Event Processing: Case St...Tim Bass
 
Protect Your Revenue Streams: Big Data & Analytics in Tax
Protect Your Revenue Streams: Big Data & Analytics in TaxProtect Your Revenue Streams: Big Data & Analytics in Tax
Protect Your Revenue Streams: Big Data & Analytics in TaxCapgemini
 
Robotic Process Automation (RPA) Webinar - By Matrix-IFS
Robotic Process Automation (RPA) Webinar - By Matrix-IFSRobotic Process Automation (RPA) Webinar - By Matrix-IFS
Robotic Process Automation (RPA) Webinar - By Matrix-IFSIdan Tohami
 
Business Intelligence For Anti-Money Laundering
Business Intelligence For Anti-Money LaunderingBusiness Intelligence For Anti-Money Laundering
Business Intelligence For Anti-Money LaunderingKartik Mehta
 
Big Data and MDM altogether: the winning association
Big Data and MDM altogether: the winning associationBig Data and MDM altogether: the winning association
Big Data and MDM altogether: the winning associationJean-Michel Franco
 
Business Intelligence For Aml
Business Intelligence For AmlBusiness Intelligence For Aml
Business Intelligence For AmlKartik Mehta
 
Niclas Elfström
Niclas ElfströmNiclas Elfström
Niclas ElfströmEvensify
 
Fast Data and Architecting the Digital Enterprise Fast Data drivers, componen...
Fast Data and Architecting the Digital Enterprise Fast Data drivers, componen...Fast Data and Architecting the Digital Enterprise Fast Data drivers, componen...
Fast Data and Architecting the Digital Enterprise Fast Data drivers, componen...Stuart Blair
 
Real-Time Analytics for Industries
Real-Time Analytics for IndustriesReal-Time Analytics for Industries
Real-Time Analytics for IndustriesAvadhoot Patwardhan
 
How To Build Mature SM - final
How To Build Mature SM - finalHow To Build Mature SM - final
How To Build Mature SM - finalDanijel Božić
 
Rebooting IT Infrastructure for the Digital Age
Rebooting IT Infrastructure for the Digital AgeRebooting IT Infrastructure for the Digital Age
Rebooting IT Infrastructure for the Digital AgeCapgemini
 
Neo4j GraphTalk Copenhagen - Next Generation Solutions using Neo4j
Neo4j GraphTalk Copenhagen - Next Generation Solutions using Neo4j Neo4j GraphTalk Copenhagen - Next Generation Solutions using Neo4j
Neo4j GraphTalk Copenhagen - Next Generation Solutions using Neo4j Neo4j
 
delphix-ebook-using-data-effectively-compliance-banking-1
delphix-ebook-using-data-effectively-compliance-banking-1delphix-ebook-using-data-effectively-compliance-banking-1
delphix-ebook-using-data-effectively-compliance-banking-1Jes Breslaw
 
Who changed my data? Need for data governance and provenance in a streaming w...
Who changed my data? Need for data governance and provenance in a streaming w...Who changed my data? Need for data governance and provenance in a streaming w...
Who changed my data? Need for data governance and provenance in a streaming w...DataWorks Summit
 
Towards the Next Generation Financial Crimes Platform - How Data, Analytics, ...
Towards the Next Generation Financial Crimes Platform - How Data, Analytics, ...Towards the Next Generation Financial Crimes Platform - How Data, Analytics, ...
Towards the Next Generation Financial Crimes Platform - How Data, Analytics, ...Molly Alexander
 

Ähnlich wie The Great Unknown - How can operators leverage big data to prevent future revenue losses in the data based world (20)

Shift at work of Fraud Management
Shift at work of Fraud ManagementShift at work of Fraud Management
Shift at work of Fraud Management
 
Shift at work of fraud management
Shift at work of fraud managementShift at work of fraud management
Shift at work of fraud management
 
Ai and machine learning help detect, predict and prevent fraud - IBM Watson ...
Ai and machine learning help detect, predict and prevent fraud -  IBM Watson ...Ai and machine learning help detect, predict and prevent fraud -  IBM Watson ...
Ai and machine learning help detect, predict and prevent fraud - IBM Watson ...
 
Big Data & Analytics, Peter Jönsson
Big Data & Analytics, Peter JönssonBig Data & Analytics, Peter Jönsson
Big Data & Analytics, Peter Jönsson
 
Detecting Opportunities and Threats with Complex Event Processing: Case St...
Detecting Opportunities and Threats with Complex Event Processing: Case St...Detecting Opportunities and Threats with Complex Event Processing: Case St...
Detecting Opportunities and Threats with Complex Event Processing: Case St...
 
Protect Your Revenue Streams: Big Data & Analytics in Tax
Protect Your Revenue Streams: Big Data & Analytics in TaxProtect Your Revenue Streams: Big Data & Analytics in Tax
Protect Your Revenue Streams: Big Data & Analytics in Tax
 
Robotic Process Automation (RPA) Webinar - By Matrix-IFS
Robotic Process Automation (RPA) Webinar - By Matrix-IFSRobotic Process Automation (RPA) Webinar - By Matrix-IFS
Robotic Process Automation (RPA) Webinar - By Matrix-IFS
 
Business Intelligence For Anti-Money Laundering
Business Intelligence For Anti-Money LaunderingBusiness Intelligence For Anti-Money Laundering
Business Intelligence For Anti-Money Laundering
 
Big Data and MDM altogether: the winning association
Big Data and MDM altogether: the winning associationBig Data and MDM altogether: the winning association
Big Data and MDM altogether: the winning association
 
Business Intelligence For Aml
Business Intelligence For AmlBusiness Intelligence For Aml
Business Intelligence For Aml
 
Niclas Elfström
Niclas ElfströmNiclas Elfström
Niclas Elfström
 
Fast Data and Architecting the Digital Enterprise Fast Data drivers, componen...
Fast Data and Architecting the Digital Enterprise Fast Data drivers, componen...Fast Data and Architecting the Digital Enterprise Fast Data drivers, componen...
Fast Data and Architecting the Digital Enterprise Fast Data drivers, componen...
 
Opportunities derived by AI
Opportunities derived by AIOpportunities derived by AI
Opportunities derived by AI
 
Real-Time Analytics for Industries
Real-Time Analytics for IndustriesReal-Time Analytics for Industries
Real-Time Analytics for Industries
 
How To Build Mature SM - final
How To Build Mature SM - finalHow To Build Mature SM - final
How To Build Mature SM - final
 
Rebooting IT Infrastructure for the Digital Age
Rebooting IT Infrastructure for the Digital AgeRebooting IT Infrastructure for the Digital Age
Rebooting IT Infrastructure for the Digital Age
 
Neo4j GraphTalk Copenhagen - Next Generation Solutions using Neo4j
Neo4j GraphTalk Copenhagen - Next Generation Solutions using Neo4j Neo4j GraphTalk Copenhagen - Next Generation Solutions using Neo4j
Neo4j GraphTalk Copenhagen - Next Generation Solutions using Neo4j
 
delphix-ebook-using-data-effectively-compliance-banking-1
delphix-ebook-using-data-effectively-compliance-banking-1delphix-ebook-using-data-effectively-compliance-banking-1
delphix-ebook-using-data-effectively-compliance-banking-1
 
Who changed my data? Need for data governance and provenance in a streaming w...
Who changed my data? Need for data governance and provenance in a streaming w...Who changed my data? Need for data governance and provenance in a streaming w...
Who changed my data? Need for data governance and provenance in a streaming w...
 
Towards the Next Generation Financial Crimes Platform - How Data, Analytics, ...
Towards the Next Generation Financial Crimes Platform - How Data, Analytics, ...Towards the Next Generation Financial Crimes Platform - How Data, Analytics, ...
Towards the Next Generation Financial Crimes Platform - How Data, Analytics, ...
 

Mehr von cVidya Networks

Utilizing Big Data to Optimize Customer Value Management Strategies
Utilizing Big Data to Optimize Customer Value Management StrategiesUtilizing Big Data to Optimize Customer Value Management Strategies
Utilizing Big Data to Optimize Customer Value Management StrategiescVidya Networks
 
“Full Strike – using your data to hit targeting, proposition and strategic in...
“Full Strike – using your data to hit targeting, proposition and strategic in...“Full Strike – using your data to hit targeting, proposition and strategic in...
“Full Strike – using your data to hit targeting, proposition and strategic in...cVidya Networks
 
Why should RA & Fraud Managers rethink the way they manage their business?
Why should RA & Fraud Managers rethink the way they manage their business?Why should RA & Fraud Managers rethink the way they manage their business?
Why should RA & Fraud Managers rethink the way they manage their business?cVidya Networks
 
Smart Margin Analytics: Adding Margin Assurance Capability to Revenue Assurance
Smart Margin Analytics: Adding Margin Assurance Capability to Revenue AssuranceSmart Margin Analytics: Adding Margin Assurance Capability to Revenue Assurance
Smart Margin Analytics: Adding Margin Assurance Capability to Revenue AssurancecVidya Networks
 
TM Forum #MWA12 Catalyst Presentation with cVidya
TM Forum #MWA12 Catalyst Presentation with cVidyaTM Forum #MWA12 Catalyst Presentation with cVidya
TM Forum #MWA12 Catalyst Presentation with cVidyacVidya Networks
 
Telco’s change in Climate Brings new opportunities for growth
Telco’s change in Climate Brings new opportunities for growthTelco’s change in Climate Brings new opportunities for growth
Telco’s change in Climate Brings new opportunities for growthcVidya Networks
 
The Impact Data Traffic Explosion and LTE on Revenue Assurance
The Impact Data Traffic Explosion and LTE on Revenue AssuranceThe Impact Data Traffic Explosion and LTE on Revenue Assurance
The Impact Data Traffic Explosion and LTE on Revenue AssurancecVidya Networks
 
Enterprise Fraud Management - Challenges Brings New Opportunities
Enterprise Fraud Management - Challenges Brings New OpportunitiesEnterprise Fraud Management - Challenges Brings New Opportunities
Enterprise Fraud Management - Challenges Brings New OpportunitiescVidya Networks
 
Pricing Analytics - Pricing Mobile Data, London 2012
Pricing Analytics - Pricing Mobile Data, London 2012Pricing Analytics - Pricing Mobile Data, London 2012
Pricing Analytics - Pricing Mobile Data, London 2012cVidya Networks
 
Joint Oracle-cVidya Cloud webinar - SaaS Market Growth & Opportunities
Joint Oracle-cVidya Cloud webinar - SaaS Market Growth & OpportunitiesJoint Oracle-cVidya Cloud webinar - SaaS Market Growth & Opportunities
Joint Oracle-cVidya Cloud webinar - SaaS Market Growth & OpportunitiescVidya Networks
 
Cloud based fraud detection and management solution – alaska communications c...
Cloud based fraud detection and management solution – alaska communications c...Cloud based fraud detection and management solution – alaska communications c...
Cloud based fraud detection and management solution – alaska communications c...cVidya Networks
 
Cloud Services: Resolving the Trust vs. Uptake Paradox
Cloud Services: Resolving the Trust vs. Uptake ParadoxCloud Services: Resolving the Trust vs. Uptake Paradox
Cloud Services: Resolving the Trust vs. Uptake ParadoxcVidya Networks
 
Bringing Shadow IT into the Light with a Centralized IT Cloud Migration Strategy
Bringing Shadow IT into the Light with a Centralized IT Cloud Migration StrategyBringing Shadow IT into the Light with a Centralized IT Cloud Migration Strategy
Bringing Shadow IT into the Light with a Centralized IT Cloud Migration StrategycVidya Networks
 
Uncovering Fraud Dilemmas - cVidya in London May 2012
Uncovering Fraud Dilemmas - cVidya in London May 2012Uncovering Fraud Dilemmas - cVidya in London May 2012
Uncovering Fraud Dilemmas - cVidya in London May 2012cVidya Networks
 
When revenue intelligence meets the cloud
When revenue intelligence meets the cloudWhen revenue intelligence meets the cloud
When revenue intelligence meets the cloudcVidya Networks
 
Unlocking Customer Behavior Insights To Boost Pricing Performance - cVidya We...
Unlocking Customer Behavior Insights To Boost Pricing Performance - cVidya We...Unlocking Customer Behavior Insights To Boost Pricing Performance - cVidya We...
Unlocking Customer Behavior Insights To Boost Pricing Performance - cVidya We...cVidya Networks
 

Mehr von cVidya Networks (16)

Utilizing Big Data to Optimize Customer Value Management Strategies
Utilizing Big Data to Optimize Customer Value Management StrategiesUtilizing Big Data to Optimize Customer Value Management Strategies
Utilizing Big Data to Optimize Customer Value Management Strategies
 
“Full Strike – using your data to hit targeting, proposition and strategic in...
“Full Strike – using your data to hit targeting, proposition and strategic in...“Full Strike – using your data to hit targeting, proposition and strategic in...
“Full Strike – using your data to hit targeting, proposition and strategic in...
 
Why should RA & Fraud Managers rethink the way they manage their business?
Why should RA & Fraud Managers rethink the way they manage their business?Why should RA & Fraud Managers rethink the way they manage their business?
Why should RA & Fraud Managers rethink the way they manage their business?
 
Smart Margin Analytics: Adding Margin Assurance Capability to Revenue Assurance
Smart Margin Analytics: Adding Margin Assurance Capability to Revenue AssuranceSmart Margin Analytics: Adding Margin Assurance Capability to Revenue Assurance
Smart Margin Analytics: Adding Margin Assurance Capability to Revenue Assurance
 
TM Forum #MWA12 Catalyst Presentation with cVidya
TM Forum #MWA12 Catalyst Presentation with cVidyaTM Forum #MWA12 Catalyst Presentation with cVidya
TM Forum #MWA12 Catalyst Presentation with cVidya
 
Telco’s change in Climate Brings new opportunities for growth
Telco’s change in Climate Brings new opportunities for growthTelco’s change in Climate Brings new opportunities for growth
Telco’s change in Climate Brings new opportunities for growth
 
The Impact Data Traffic Explosion and LTE on Revenue Assurance
The Impact Data Traffic Explosion and LTE on Revenue AssuranceThe Impact Data Traffic Explosion and LTE on Revenue Assurance
The Impact Data Traffic Explosion and LTE on Revenue Assurance
 
Enterprise Fraud Management - Challenges Brings New Opportunities
Enterprise Fraud Management - Challenges Brings New OpportunitiesEnterprise Fraud Management - Challenges Brings New Opportunities
Enterprise Fraud Management - Challenges Brings New Opportunities
 
Pricing Analytics - Pricing Mobile Data, London 2012
Pricing Analytics - Pricing Mobile Data, London 2012Pricing Analytics - Pricing Mobile Data, London 2012
Pricing Analytics - Pricing Mobile Data, London 2012
 
Joint Oracle-cVidya Cloud webinar - SaaS Market Growth & Opportunities
Joint Oracle-cVidya Cloud webinar - SaaS Market Growth & OpportunitiesJoint Oracle-cVidya Cloud webinar - SaaS Market Growth & Opportunities
Joint Oracle-cVidya Cloud webinar - SaaS Market Growth & Opportunities
 
Cloud based fraud detection and management solution – alaska communications c...
Cloud based fraud detection and management solution – alaska communications c...Cloud based fraud detection and management solution – alaska communications c...
Cloud based fraud detection and management solution – alaska communications c...
 
Cloud Services: Resolving the Trust vs. Uptake Paradox
Cloud Services: Resolving the Trust vs. Uptake ParadoxCloud Services: Resolving the Trust vs. Uptake Paradox
Cloud Services: Resolving the Trust vs. Uptake Paradox
 
Bringing Shadow IT into the Light with a Centralized IT Cloud Migration Strategy
Bringing Shadow IT into the Light with a Centralized IT Cloud Migration StrategyBringing Shadow IT into the Light with a Centralized IT Cloud Migration Strategy
Bringing Shadow IT into the Light with a Centralized IT Cloud Migration Strategy
 
Uncovering Fraud Dilemmas - cVidya in London May 2012
Uncovering Fraud Dilemmas - cVidya in London May 2012Uncovering Fraud Dilemmas - cVidya in London May 2012
Uncovering Fraud Dilemmas - cVidya in London May 2012
 
When revenue intelligence meets the cloud
When revenue intelligence meets the cloudWhen revenue intelligence meets the cloud
When revenue intelligence meets the cloud
 
Unlocking Customer Behavior Insights To Boost Pricing Performance - cVidya We...
Unlocking Customer Behavior Insights To Boost Pricing Performance - cVidya We...Unlocking Customer Behavior Insights To Boost Pricing Performance - cVidya We...
Unlocking Customer Behavior Insights To Boost Pricing Performance - cVidya We...
 

Kürzlich hochgeladen

5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best Practices5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best PracticesDataArchiva
 
AI for Sustainable Development Goals (SDGs)
AI for Sustainable Development Goals (SDGs)AI for Sustainable Development Goals (SDGs)
AI for Sustainable Development Goals (SDGs)Data & Analytics Magazin
 
SFBA Splunk Usergroup meeting March 13, 2024
SFBA Splunk Usergroup meeting March 13, 2024SFBA Splunk Usergroup meeting March 13, 2024
SFBA Splunk Usergroup meeting March 13, 2024Becky Burwell
 
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024Guido X Jansen
 
ChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics InfrastructureChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics Infrastructuresonikadigital1
 
YourView Panel Book.pptx YourView Panel Book.
YourView Panel Book.pptx YourView Panel Book.YourView Panel Book.pptx YourView Panel Book.
YourView Panel Book.pptx YourView Panel Book.JasonViviers2
 
The Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayerThe Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayerPavel Šabatka
 
MEASURES OF DISPERSION I BSc Botany .ppt
MEASURES OF DISPERSION I BSc Botany .pptMEASURES OF DISPERSION I BSc Botany .ppt
MEASURES OF DISPERSION I BSc Botany .pptaigil2
 
Mapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptxMapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptxVenkatasubramani13
 
Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...PrithaVashisht1
 
Virtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product IntroductionVirtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product Introductionsanjaymuralee1
 
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptxTINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptxDwiAyuSitiHartinah
 
Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023Vladislav Solodkiy
 
CI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual interventionCI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual interventionajayrajaganeshkayala
 
How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?sonikadigital1
 
Master's Thesis - Data Science - Presentation
Master's Thesis - Data Science - PresentationMaster's Thesis - Data Science - Presentation
Master's Thesis - Data Science - PresentationGiorgio Carbone
 
Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for ClarityStrategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for ClarityAggregage
 

Kürzlich hochgeladen (17)

5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best Practices5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best Practices
 
AI for Sustainable Development Goals (SDGs)
AI for Sustainable Development Goals (SDGs)AI for Sustainable Development Goals (SDGs)
AI for Sustainable Development Goals (SDGs)
 
SFBA Splunk Usergroup meeting March 13, 2024
SFBA Splunk Usergroup meeting March 13, 2024SFBA Splunk Usergroup meeting March 13, 2024
SFBA Splunk Usergroup meeting March 13, 2024
 
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
 
ChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics InfrastructureChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics Infrastructure
 
YourView Panel Book.pptx YourView Panel Book.
YourView Panel Book.pptx YourView Panel Book.YourView Panel Book.pptx YourView Panel Book.
YourView Panel Book.pptx YourView Panel Book.
 
The Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayerThe Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayer
 
MEASURES OF DISPERSION I BSc Botany .ppt
MEASURES OF DISPERSION I BSc Botany .pptMEASURES OF DISPERSION I BSc Botany .ppt
MEASURES OF DISPERSION I BSc Botany .ppt
 
Mapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptxMapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptx
 
Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...
 
Virtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product IntroductionVirtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product Introduction
 
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptxTINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
 
Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023
 
CI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual interventionCI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual intervention
 
How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?
 
Master's Thesis - Data Science - Presentation
Master's Thesis - Data Science - PresentationMaster's Thesis - Data Science - Presentation
Master's Thesis - Data Science - Presentation
 
Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for ClarityStrategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
 

The Great Unknown - How can operators leverage big data to prevent future revenue losses in the data based world

  • 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 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 In 60 Seconds Consumption Payments Social Interactions Location Retailing Web Browsing Apps Usage 60% of online data comes from mobile
  • 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. 55 Big Data Analytics "Data is widely available, what is scarce is the ability to extract wisdom" Hal Varian, Chief Economist, Google
  • 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 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  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 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. 1010 Examples of new threats and prevention methods
  • 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:
  • 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 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
  • 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 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 IP PBX Hacking Detection
  • 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.  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
  • 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 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 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