Credit Card Analytics on a Connected Data Platform

Credit Card Analytics on a
Connected Data Platform

2 © Hortonworks Inc. 2011 –2016. All Rights Reserved
Copyright © Capgemini 2016. All Rights Reserved.
Vamsi Chemitiganti
General Manager –
Financial Services
HORTONWORKS
Arindam Choudhury
Big Data Practice
Leader –
Financial Services
CAPGEMINI
Speakers

Agenda
• Payments Card Industry Trends 2016
• Our Vision for Credit Card Analytics
• Solution Architecture
• Solution Walkthrough
• Hortonworks & Cap-Gemini Partnership
• Benefits
• Q&A

State of the Payments Card Industry

Key Payment Industry Trends in 2016
• Payments has become the eye of the FinTech boom
• Digital Payments increasingly replacing cash
• Payments moving to an Omni channel world
• Loyalty Programs getting increasingly sophisticated
• Increasingly sophisticated regulations – E.g. PSD2
• National Payments infrastructures move to real time
• New Revenue models to strengthen fee income
• Digital Banks drive cost efficiencies
• More and more players want to “own the customer”
• Payments as part of the customer journey
• Cyberattacks keep increasing
• Legacy application architecture
– Product and LOB focused vs. Customer Centric
– Disparate data sources
– New data sources – no ability to manage
Omnichannel Payments
Realtime National
Payments infrastructures
New Revenue Models to
strengthen income
Digital becoming consumer /
Insured preferred interaction
channel – Customer 360
Location based data usage
Move to Real time risk & fraud
analytics

Background on Payment Card Fraud

Ã Primarily Credit Card Fraud
– Mushrooming into a global problem
– Problem for consumers, financial institutions
and regulators
– Complex Fraud that is part of a set of smaller frauds
Ã Fraud schemes are increasingly complex and cause a
high level of damage
Ã EMV technology expected to reduce POS fraud in a big
way
Ã Payment card fraud detection platforms
Ã Types of Fraud –
– CNP (Online), Lost, Counterfeit, Intercepted in Mail, Card Not
Received etc
Payment Card Fraud..

The Magnitude of Payment Card Fraud

US Share of Credit Card Fraud

Current Approaches To Tackling Fraud

The Fraud IT Landscape
Ã Fragmented Fraud Detection Systems
Ã Fragmented enterprise systems
Ã Typically proprietary vendor and in-house built solutions
Ã Overreliance on pure transaction monitoring
Ã Unable to keep pace with the progress of technology
Ã Move to combine Fraud (Credit Card Fraud, AML & InfoSec) into one platform
Ã Issues with flexibility, cost and scalability

Classical Approach to Fraud Monitoring and Detection
Ã Relying on an Expert Systems based approach
Ã Manual Investigation heavy
Ã New Rules manually added as new schemes of fraud are discovered
Ã A need to supplant and augment above with a more dynamic approach
around Descriptive and Predictive Analytics Methods

Capgemini & HWX Partnership

Capgemini & Hortonworks partnership
3+ years of active collaboration
• Co-engineering on the most recent innovations to build accelerators and
increase time-to-value
• Growing and industrializing capabilities
• Delivering successfully multiple client engagements worldwide,
including among largest banks and Insurance companies
• Providing seamless collaboration and strong partnering experience to
our clients

Our Vision for Credit Card Analytics …
§ Global Credit Card industry is rapidly changing and the
participants are increasingly facing new challenges with
snowballing volumes, regulatory pressures and new
entrants competing for the market share
§ The Credit Card industry has responded to these
challenges by looking at avenues to cut costs, increase
efficiencies, provide better, safer products and services
to attract new and retain existing customer base
§ Our solution is geared towards enhancing the decision
making process using all of the data that’s available to
the Credit Card participants, including customer data,
transactions, third party data, open data, government
data, location data, social data, etc.
§ With graph based search and advanced analytics along
with the ability to handle large volumes the solution
helps you find the hidden patterns in the data. It tells
you how your customers behave and what will interest
them. It tells you where and when to target them with
your partners ads. It tells you which customers are likely
to attrite. It tells you who is likely to defraud you. It
helps you detect and stop Fraud real time and predicts
it to increase your customers’ experience

… delivers Insights for all credit card participants
Customer 720
Transactions
Analytics
Third Party
Data Sources
Location
Based
Intelligence
Third Party Processors
•Fraud detection and prevention
•Transaction pattern based Fraud Prevention
•Customer behavior based Fraud Prevention
•Merchant analytics
•Analyze Merchant performance amongst peers
•Customer Segmentation - understand behaviors
based on transactions for targeted Campaign
Management
•Attrition Management
•Capture Voice of Customer from Social Media to
manage churn
•Transaction patterns based churn management
Card Associations
•Fraud detection and prevention for Retailers and
Merchant Acquirers
•Macro-economic predictions based on spend patterns
•Marketing Analytics
•Market share analysis
•Analytics for Retailers
•Customer Behavior Analytics
•Customer Spend Analytics
•Customer Churn Prediction
•Analytics for Merchant Acquirers
•Credit Risk Analytics
•Merchant performance (decline) analytics
•IoT & Payments Analytics –wearable, RFID, etc.
Issuers
•Customer Acquisition
•Customer Attrition Management
•Social media integration to identify influence network
and augment referral programs
•Geo-location based partner Ads./ real time offers
•“Measure” success of marketing campaigns
•Fraud Management
•Real time Fraud Management
•Location based Fraud Management
•Usage Analytics
•Card issued but not used –auto sms with introductory
offers
•Track decrease in usage –provide lucrative offers

… utilizes graph based search and advanced analytics to gather Insights and leverages
an integrated transactions and customer intelligence Hub
• Integrate with Customer 720 view
• Advanced “customer identification”
techniques
• Ability to identify “Cohorts”
• Models for cluster link analysis
Transaction
Intelligence
Customer
Intelligence
Customizable Rules
Analytical Models
Payments Ingestion
Self Learning Models
Behavioral Profiling
Social media
Third-party Indicators
• Transaction clustering
• Multiple Machine
Learning Algos. for
pattern detection
• Gather Insights from
Transactions
• Cluster and segmentations
• 3rd
Party Data Sources
• Personas
• Links and Relationships
720 Customer View
Reporting
• Feedback loop for
increasing Insights
effectiveness
• Insights integrated with
existing business
processes

Solution Architecture

Reference Architecture for Payment Fraud Detection
Search
Customer and Transactions Store
Fraud Cluster
Kafka
Unsupervised
Feature Extraction
Social Network Analysis
Supervised
Training Sets
ML Algorithms
Predictive Models
Pattern Detection & Machine Learning
Payment
Internal
Data
3rd Party
Visualization
Historical
Transactions
Customer
Datasets
Customer 720
for Fraud
Hot / Real time
Enrichment
Rules
Warm / Streaming
Data
Few seconds
< 100 ms
Offline Data
Few hours to days to train models and gather Insights
Local Cache
Real Time
Tracking
Customer
Response
Social Media
Streams
Feedback
Model
Output
Sink
Gateways
APIs
Request to Track
relevant parties
Fraud
Notification

Machine Learning: Enterprise Data Science at Scale
Use flow of data and the computing power of the
connected platform to enable autonomous machine
learning
• Real time data flows combined with massive parallel
computing allows AI to continuously improve
• Enables AI to make decisions in the “Grey Areas”
Build and train AI on full volume data not a sample
• Time, effort, accuracy, scale
• Visualize data as it is being manipulated
Deploy the AI model without re-implementing
• Spark models can be plugged into a modern
connected platform.

Solution Walkthrough..

Copyright © Capgemini 2016. All Rights Reserved. Page 22
Improved
Experience
/Reduced Cost
Immediate
Customer
Feedback
Years of
Customer
Transaction Data
Fraud Detection
Complete
Customer
Profile
Real time
ingest of
transactions
Proactively identify potential
fraudulent transactions to
protect the customer and
improve customer experience
• Proactively monitor every credit
card transaction using machine
learning to catch potential fraud
• Customer Service Analyst reviews
flagged transactions in real time via
a next generation application
running on the connected platform
• HDF controls real time flow of data
in and out of the connected
platform to the various source and
destination points
Innovate
Renovate
Purchase
Behavior
Insight
Journey to Fraud Detection

Credit FraudAnalyst Inbox

Fraud AnalystView

Data Flow Design in Hortonworks Data Flow (HDF)

Benefits..

Ã A free open source linearly scalable data & analytics platform has only become available within
the last few years
Ã Predictive Analytics & Data Science are a new way of approaching Fraud Detection
Ã A Big Data Platform is linearly and incrementally scalable and can eliminate the cost issues
associated with relational platforms (e.g. Exadata & Netezza), parallel computing platforms (e.g.
Platform & Data Synapse and large scale data caching platforms (e.g. Oracle Coherence &
Gemfire:
– No software licensing fees mean that the cost is only for the generic blade and disk farms
needed to support the data and in memory compute environments.
– This reduces the upfront database licensing and hardware costs from several million dollars
down to around a half million dollars.
– Up front parallel compute and data caching platform licensing costs are reduced from
several million to zero dollars.
Ã It can replace the Data Warehouse, the upstream Risk, Finance and Compliance Operational
Data Stores (ODS’s) and the Business Managed Applications (BMA’s).
– Eliminates the costs associated with data feeds and transformation between the Data
Warehouse and the various enterprise ODS’s and BMA’s.
– Eliminates the redundant IT and Business staff that are required to support the fragmented
risk and compliance systems.
Why Will This Work Now?

Q&A

Thank you!

Credit Card Analytics on a Connected Data Platform

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