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Revenue Guard Your Operational Risk Management (ORM) Partner Harnessing innovation to protect your revenues
Transcript of Brand photo banki3forum.org/wp-content/uploads/sites/2/user_uploads/caroline/amdo… · Revenue...
Revenue GuardYour Operational Risk Management (ORM) Partner
Harnessing innovation to protect your revenues
Information Security Level 2 – Sensitive
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Revenue Guard ORM Services
Fraud
Management
Revenue
Assurance
Smart Bill
QA
Payment
Assurance
Digital Partner
Assurance
Financial
Assurance
Service Providers
Risk Management
Map
B2B Managing Enterprise Risk
PBX Protection
Call Center Protection
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The Future is Now
The best way to predict the future is to invent it.Alan Kay
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© 2017 – Proprietary & Confidential Information of Amdocs4 Cities of the future
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Is this the city of the Future?
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Or this?
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More likely this
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99%
1T
Of things are not yet connected
Cost of Sensors
Cost of Bandwidth
Cost of Processing Power
things connected in 2020
The future ain't what it
used to be.Yogi Berra
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Service Provider world is changingthe CSP to DSP Journey
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It’s a brave new world
Connected Car
Digital RTSS
E-Health
Smart CitySmart Grid
E-Payments
Smart Home
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New RA and Fraud Challenges
Revenue Assurance
Multiple digital partners
Multiple services
Multiple business models
Multiple platforms (Omni Channel)
Multiple devices
Fraud Management
Multiple partners
Multiple services
Cyber Fraud
Multiple devices (Omni Channel)
Customer Authentication per transaction
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The Cyber Threat
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No Need to Hide
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Using Government Weapons
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Advanced Persistent Threat (APT)
Distributed Denial of Service (DDoS)
Cross-Platform Malware (CPM)
Phishing
Common Attack Methods
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The Cyber Arena
<20 %
Will click
Move from
machine to human
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Authentication
Who is the
next Gen
fraudster ?
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< 75% Financial Verizon 2016
Cyber crime to reach
$2 T by 2019 Forbes
$More than 500K
attacks a day
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The Cyber Threat
Phishing
Link/
Attachment
Malware
Person/System
Steal Credentials
/Data
Use of Stolen
Credentials
Purchase
Payment
Control
Cyber Security
The ‘Enabler’
Cyber Fraud
The ‘Result’
Data Breach “There are only two types of companies:Those that have been hacked and those that would be” Robert Mueller
FBI Director 2012
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Market Needs - A Cyber-
Fraud Based Solution
Integrate with existing cyber-security solutions Anti-phishing, Anti-malware, Insider fraud detection solution
Use both security and fraud related indications
Agile/Flexible
Machine learning as core detection component
Advanced correlation capabilities
Provide an holistic view across all network elements
Advanced authentication features and algorithms
Use crowd sourcing approach (‘contribute and be contributed’)
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Machine Learning/ Artificial Intelligence
The Value
Ruled based systemsRely on prior knowledge
Need constant adaptation
Have high % of false positives
Limited results analytics
High reliance on manual human intervention
The Need
Ability to utilize processing powerMultiple models
Multiple technologies (deep learning, neural networks,
Bayes network and more)
Ability to detect unknown unknowns
Self tuning and low false positives
Preventive models
Ability to create and use NLP, sentiment analysis,
biometric and behavior analysis
Automation
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Machine Learning in
Revenue Guard– How ?
Utilization of RapidMiner , A powerful &
collaborative analytics platform for
machine learning capabilities and data
science
01Combining embedded ML algorithms in RA
and FMS
‘built-in’ set of APIs that provides accessibility
and transparency to data providing intuitive
semantics, results can be stored and used by
Revenue Guard Platform
Consultancy service performing
ML audits detecting unknown
threats
02
03
04
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The Machine learning process
Training
Raw Data
Feature Extraction
Feature Selection
Model Training
Data Cleansing
ModelModel
Evaluation
Labels
New Data
Feature Extraction
Apply model
Predict / Score / Cluster
Labels
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Machine Learning main categories
Supervised
learningUnsupervised
learningReinforcement
learning
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CEV Service – High Level Concept
SALES CHANNELS
OMS
ORDERING SHIPPING ENDCUSTOMER
REVENUE GUARD
IVR
SERVICECENTER
• Appling multiple machine learning models and known rules to detect fraudulent transactions
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Authentication Methods:
Device Unlock
Social Media
Movement Analysis
Behavior
Analysis
Device Finger prints
Cyber
Journey
Device
identity /proximity
Two/multi factor authentication
Biometrics
Unlock
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Platform services
Monitoring
Auditing
Detection Techniques
Machinelearning
Rulebased
Data APIs
En
tity
ma
na
ge
me
nt
Existingcustomers
Occasional customers
External DB
Business services
Security
BI la
ye
r
POS/CNP fraud
Digital Partner Management
CustomerWeb portal
Reports
Geo-location
Dispatching
Social media
Biometrics
Dashboards
Statisticalanalysis
Time &Space
Device FP
Social media
MFS fraud Partners fraud
RA/Fraud
Employees
Partners
Wallets
Web
Channels - Inbound layer
IVR Smartphone Apps SMSService desk Internal activities
Revenue Guard Future Platform
Billing/Payment Assurance
Data fraud
Existing Systems
Analytics
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Our world is changing rapidly
New services, technologies and
business models
New fraud enablers and methods
We need New approaches, new
technologies and new skill-sets to
meet these challenges
An ongoing process
We need to be prepared for the future
and secure the present
Summary
Thank you
