Webinar: Fighting Fraud with Graph Databases

Webinar | Fighting Fraud with Graph Databases
DataStax and Cambridge Intelligence

Agenda • Introduction - Andrea Cross, Senior
Marketing Director, DataStax (5 mins)
• Setting the Scene - Kaush Kotak, Project
Manager, Cambridge Intelligence (10 mins)
• DSE Graph - Gehrig Kunz, Product
Marketing Manager, DataStax (15 mins)
• Fraud Management with Graphs - Kaush
Kotak (20 mins)
• Q & A (10 mins)
All Rights Reserved | Confidential

Fraud management with graphs
Graph technologies are an
integral part of modern fraud
management solutions

Traditional “monolithic”, single supplier, full-stack solutions:
• $$$ price tags: large investments lead to “make do” attitude, reluctance
to change
• Closed architectures: restricted extensibility and interoperability, fixed
models and ways of working
• Scale: unable to cope with demands for information from new disparate
data sources
• Limited analytics/ visualization: leads to poor decisions
• Stalled innovation: lack of agility, creates opportunities for exploitation
Traditional fraud solutions

A new approach
1. Data Sources
Structured, unstructured
and event data
Feedback
loop
Alerts
2. Structure/
Load
NLP, Entity Extraction,
Entity Resolution
3. Store
Fraud database
4. Process
Rule-based scoring &
predictive analytics
Additional core data
retrieved on demand
for visualization
New
rules
5. Case
Management
Data streamed or
loaded
New evolving “build your own” architecture
• Use best of breed tools
• Keep ahead of fraudsters
• Grow and develop as your business changes
6. Visualize
Aggregate & network
view

DataStax & KeyLines
1. Data Sources
Structured, unstructured
and event data
Feedback
loop
Alerts
2. Structure/
Load
NLP, Entity Extraction,
Entity Resolution
3. Store
Fraud database
4. Process
Rule-based scoring &
predictive analytics
Additional core data
retrieved on demand
for visualization
New
rules
5. Case
Management
6. Visualize
Aggregate & network
view
Data streamed or
loaded

Demo Preview

Investigating the basics
Cloud Applications:
• Contextual
• Always-On
• Real-time
• Distributed
• Scale

Real-time fraud detection
Your Application Message Queue
Streaming
Analytics
Batch
Analytics
Transactional
Single DSE Cluster
DataStax Driver

To their credit…
• Fraudsters are getting wiser
• In isolation, all of these
transactions are ‘ok’
• So how can we identify fraud that
doesn’t have obvious outliers?

Spoiler: it’s in the relationships

Spoiler: it’s in the relationships
RELATIONSHIPS

Graph to the rescue
Defining a graph problem
– Value is in the relationships
– A large number of joins
• Self-JOINs
Questionable query performance
?

Value in the relationships

Understanding the difference
Graph database
Relational database
Connectedness of data
Performance

And easier to build

DataStax Enterprise Graph
• Real-time graph database
• Manage complex and
highly connected data
• Discovering commonalities
and anomalies in data
• Stored as Cassandra
tables behind the scenes

Indexing
& Search
Streaming
Analytics
Graph
Batch
Analytics
DataStax Enterprise multi-model/mixed workload

Analytics at your command
• DSE Graph has seamless support
for DSE Analytics powered by
Apache Spark.
• No need to learn Spark – Gremlin
language used both for OLTP and
OLAP.
• Analytics workloads can be
separated from OLTP workloads.

Search your graph data
• DSE Graph has seamless support
for DSE Search powered by
Apache Solr.
• Simple, schema-driven index
management.
• DSE Graph’s query optimizer
automatically uses Solr behind the
scenes.

Powering cloud applications
Always-on
Designed to handle any failure,
no matter how catastrophic
Effortless scale
Take advantage of every opportunity.
Focus on what matters most to you.
Instant insight
Built into your application to create
actionable, modern experiences.

Our focus is graph/ network visualization (aka Link analysis)
What is graph visualization?
node node
Vertex
Edge
Vertex
link
Data in which the connections between entities are important to
understand, e.g. social network data, transaction data, etc.

1. Human brain is unrivaled for pattern finding
2. “Consume” a large amount of data
3. See data in full context
4. Intuitively explore data and connections
5. Explain what is happening
6. Communicate complex insight to non-experts
Why visualize graphs?

Graph patterns show how your business works
Graph visualization

Graph visualization
Business As Usual

Graph visualization
Unusually high connectivity,
requires investigation

Fraud use cases
Application Fraud
Social Media FraudIdentity Theft Account Takeover
Claims Fraud
+ many others
Transaction Fraud

Fraud investigation
• Unusual connectivity -> “suspect” behavior
• Visualization helps humans uncover these anomalies
• Anomaly analysis & investigation
Example nodes:
• Cases
• People
• Addresses
• Vehicles
Example links:
• Transactions
• Ownership
• Family relationships

Investigation:
• Suspect behavior is understood
• Rules can be defined
• Automation possible
• Outliers and anomalies require
investigation
• Analyst use visualization to verify
unusual cases
Investigation vs detection
Detection:
• Unknown fraudulent behaviour
• Generally not automated
• Analysts use visualization to
understand MO and patterns
• Requires domain knowledge
• Time consuming
• Uncover detection rules
Investigation Detection
known unknown
adapt
rules

Real fraud examples
Link through
Account/Policy holder
with same address,
email & phone

Real fraud examples
Initial claim rejected but policy
holder has reclaimed for a
similar incident a week later

Third parties involved in
two claims, share links
with policy holders
Real fraud examples

Multiple third parties, large
number of personal injury
claims (>$50k), inconsistent
pattern of activity
Real fraud examples

KeyLines is a powerful SDK for building network visualization web applications:
Introducing KeyLines
• Cross-browser compatibility
• Works on any device
• Full customization
• Powerful functionality
• A fast developer experience
• Rapid deployment
• Easy maintenance

• Graph visualization and graph analytics are critically
important for fraud investigation and detection
• Users need to see graphs and interact with them visually
to get insight
• KeyLines and DSE Graph are a powerful combination for
fraud management solutions
• DSE Graph offers great potential to scale to large graphs!
Summary

What’s next?
Free online training from DataStax Academy –
– DS330: DSE Graph
• 29 units
• ~12 hours
DSE Graph Resources
http://www.datastax.com/dse-graph-campaign/

KeyLines/ DataStax Architecture
Gremlin -
JavaScript
WebSocket
Apache TinkerPop and Apache TinkerPop Gremlin logos are
trademarks of The Apache Software Foundation.
DataStax logo owned by DataStax.
TinkerPop

Webinar: Fighting Fraud with Graph Databases

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