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© 2014 – PROPRIETARY AND CONFIDENTIAL INFORMATION OF CVIDYA When Big Data Analytics meets Fraud Prevention Daniel Glebocki - Director of Fraud Management-Orange Israel/012 smile Tal Eisner - Senior Director Product Strategy-cVidya

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Page 1: When Big Data Analytics meets Fraud Prevention EISNER_FINAL.pdfBig Data Analytics in Fraud Management 11 Significantly helps in areas such as: Analysis of unfair use and out-of-bundle

© 2014 – PROPRIETARY AND CONFIDENTIAL INFORMATION OF CVIDYA

When Big Data Analytics

meets Fraud Prevention

Daniel Glebocki - Director of Fraud Management-Orange Israel/012 smile

Tal Eisner - Senior Director Product Strategy-cVidya

Page 2: When Big Data Analytics meets Fraud Prevention EISNER_FINAL.pdfBig Data Analytics in Fraud Management 11 Significantly helps in areas such as: Analysis of unfair use and out-of-bundle

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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

What You Should Know - cVidya

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Page 3: When Big Data Analytics meets Fraud Prevention EISNER_FINAL.pdfBig Data Analytics in Fraud Management 11 Significantly helps in areas such as: Analysis of unfair use and out-of-bundle

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Turning your DATA into VALUE

Page 4: When Big Data Analytics meets Fraud Prevention EISNER_FINAL.pdfBig Data Analytics in Fraud Management 11 Significantly helps in areas such as: Analysis of unfair use and out-of-bundle

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An Entire New Ball Game

Page 5: When Big Data Analytics meets Fraud Prevention EISNER_FINAL.pdfBig Data Analytics in Fraud Management 11 Significantly helps in areas such as: Analysis of unfair use and out-of-bundle

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VISION: To create a world where everything intelligently connects via mobile networks, delivering rich services to businesses and consumers in every aspect of their lives (GSMA, Connected Living, November 2013)

Connected Living

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Page 6: When Big Data Analytics meets Fraud Prevention EISNER_FINAL.pdfBig Data Analytics in Fraud Management 11 Significantly helps in areas such as: Analysis of unfair use and out-of-bundle

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Some Numbers

Page 7: When Big Data Analytics meets Fraud Prevention EISNER_FINAL.pdfBig Data Analytics in Fraud Management 11 Significantly helps in areas such as: Analysis of unfair use and out-of-bundle

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New Fraud Management ChallengesNew Fraud Management Challenges

Page 8: When Big Data Analytics meets Fraud Prevention EISNER_FINAL.pdfBig Data Analytics in Fraud Management 11 Significantly helps in areas such as: Analysis of unfair use and out-of-bundle

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� Fraud units need to process & control extraordinary volumes of data which

entails:

– Info sources that did not exist before

– Extensive use of external sources e.g., social networks

– Cross analysis of non-associated sources of info

– A new set of risks and threats to be identified & controlled

– A whole new terminology to master and areas to cover

Finding Poisoned Fruit Trees In The Forest

Page 9: When Big Data Analytics meets Fraud Prevention EISNER_FINAL.pdfBig Data Analytics in Fraud Management 11 Significantly helps in areas such as: Analysis of unfair use and out-of-bundle

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Someone Is Moving My Cheese…

� Usage-based Voice Fraud becomes less relevant

� Real-Time Self Provisioning :

– Encourages Acquisition / Subscription Fraud

Issues

– Encourages Account Takeover

� Price Plan Abuse / Out-of-Bundle Usage

� “Controls” at POS are less relevant

– Virtual POS systems become the norm

– A shift towards OTA control

� Need for external sources for essential investigation

Page 10: When Big Data Analytics meets Fraud Prevention EISNER_FINAL.pdfBig Data Analytics in Fraud Management 11 Significantly helps in areas such as: Analysis of unfair use and out-of-bundle

� Manual, long-term investigation process changes to quick, fast and detailed retrieval of info

� Processing xls. sheets, human intelligence and long hours of manual analysis – have changed into an effective alerting mechanism of valuable insights

� BD Platform enables storing and analysis of info sources that were not available in the past, and in significantly larger retention

� Enabling the Fraud Unit to provide services and leverage capabilities for other non-fraud activities

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Page 11: When Big Data Analytics meets Fraud Prevention EISNER_FINAL.pdfBig Data Analytics in Fraud Management 11 Significantly helps in areas such as: Analysis of unfair use and out-of-bundle

Big Data Analytics in Fraud Management

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� Significantly helps in areas such as:

─ Analysis of unfair use and out-of-bundle data

─ Security matters for which info is needed “here and now”

─ Info sharing and intelligence gathering via Web data

─ Internal fraud investigations

Quicker, Richer, Better

Page 12: When Big Data Analytics meets Fraud Prevention EISNER_FINAL.pdfBig Data Analytics in Fraud Management 11 Significantly helps in areas such as: Analysis of unfair use and out-of-bundle

Analytics for Internal Fraud Analysis

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Page 13: When Big Data Analytics meets Fraud Prevention EISNER_FINAL.pdfBig Data Analytics in Fraud Management 11 Significantly helps in areas such as: Analysis of unfair use and out-of-bundle

Internal Fraud

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� Controlling sales personnel requires extensive use of:

─ Feeds from social networks─ Retention of wide-ranging historical data─ Traffic control of customers

─ Analysis of sales contracts─ Detailed control over internal systems─ CRM logs and information

Tedious “ant work”. . .

Big-Data oriented

Information

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Page 14: When Big Data Analytics meets Fraud Prevention EISNER_FINAL.pdfBig Data Analytics in Fraud Management 11 Significantly helps in areas such as: Analysis of unfair use and out-of-bundle

Internal Fraud Disclosure by Analytics

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� Obtain relevant processes and controls are put in place to

monitor internal sales procedures:

─ Average sales per rep per day─ Who are the customers? What services are being sold?─ What social feeds are being posted?─ Transactions in billing and CRM applications─ Characteristics of car usage by sales rep─ Communication forensics of sales rep

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Page 15: When Big Data Analytics meets Fraud Prevention EISNER_FINAL.pdfBig Data Analytics in Fraud Management 11 Significantly helps in areas such as: Analysis of unfair use and out-of-bundle

...And More

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� Controls are in place for monitoring new customers activated

by sales rep:

─ Trends and patterns of usage ─ % of credit and collection issues─ % of fraud cases in history─ Connections between known fraudsters─ Calls from customers to known high-risk destinations

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Page 16: When Big Data Analytics meets Fraud Prevention EISNER_FINAL.pdfBig Data Analytics in Fraud Management 11 Significantly helps in areas such as: Analysis of unfair use and out-of-bundle

Cross Source Analysis For Fraud Insights

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Social Networks

CustomerManagement

Finance,Accounting,

AR,Collection

Rating &Billing

Network & Usage

Management

ContractsAudit

CarUsage

Analysis

Fraud Alert

Apps LocationSocialFraud

History

Page 17: When Big Data Analytics meets Fraud Prevention EISNER_FINAL.pdfBig Data Analytics in Fraud Management 11 Significantly helps in areas such as: Analysis of unfair use and out-of-bundle

Results

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� Activation average - Up 267%� VAS activation - Up 153%� Benefits given to new subscribers - Up 218%� Intercepted anti-company posts on rep’s Facebook Wall� Car KM/Fuel consumption - Up 233%� Major adjustments in CRM system made during non-business

hours� Over 70% of subscribers had credit / payment issues during

1st billing cycle

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Page 18: When Big Data Analytics meets Fraud Prevention EISNER_FINAL.pdfBig Data Analytics in Fraud Management 11 Significantly helps in areas such as: Analysis of unfair use and out-of-bundle

Results (Cont.)

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� Combination of the above provided strong indications for fraud by reps

� Info received in near real time� Only by cross analyzing are such insights made possible � With traditional investigation methods, similar

accumulation of info could have taken weeks!

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Page 19: When Big Data Analytics meets Fraud Prevention EISNER_FINAL.pdfBig Data Analytics in Fraud Management 11 Significantly helps in areas such as: Analysis of unfair use and out-of-bundle

Analytics within the Big Data enables

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� Real-time retrieval of actionable alerts from cross sources:

─ Saves days of manual work ─ Strengthens independence and minimizes dependency on

other departments for info─ Alerts on deviations from patterns of sale─ Provides strong indications of fraudulent activities─ Gives insights into new trends of fraud by employees─ Provides valuable information on business level and

quality of work

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Page 20: When Big Data Analytics meets Fraud Prevention EISNER_FINAL.pdfBig Data Analytics in Fraud Management 11 Significantly helps in areas such as: Analysis of unfair use and out-of-bundle

Significant Indicators For Fraud

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Page 21: When Big Data Analytics meets Fraud Prevention EISNER_FINAL.pdfBig Data Analytics in Fraud Management 11 Significantly helps in areas such as: Analysis of unfair use and out-of-bundle

Trends and Patterns - Quicker ,Richer, BetterAlerts

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Page 22: When Big Data Analytics meets Fraud Prevention EISNER_FINAL.pdfBig Data Analytics in Fraud Management 11 Significantly helps in areas such as: Analysis of unfair use and out-of-bundle

More Ahead . . .

In the near future, Fraud Investigators will have to connect to

even more external sources of info to “complete the big

picture”:

─ OTT Application Content─ Unstructured Data─ Mobile Payment Platforms─ M2M

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Page 23: When Big Data Analytics meets Fraud Prevention EISNER_FINAL.pdfBig Data Analytics in Fraud Management 11 Significantly helps in areas such as: Analysis of unfair use and out-of-bundle

� Data tsunami and fraud risks inflation highlight the need for an automated mechanism

� Analytics provide what fraud management has always needed:

– Patterns & Trends

– Cross analysis of non-correlated sources of info that were not used in the past

– Alerting of deviations

– Significant reduction of false positive

– Accurate, fast & intelligent insights

� Dramatic reduction of time & investigation resources

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Page 24: When Big Data Analytics meets Fraud Prevention EISNER_FINAL.pdfBig Data Analytics in Fraud Management 11 Significantly helps in areas such as: Analysis of unfair use and out-of-bundle

THANK YOU!www.cvidya.com