GraphConnect Europe 2017 Darko Križić, PRODYNA AG

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PRODYNA
GRAPH DATABASE
USE CASES
FRAUD DETECTION COOKBOOK

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EXAMPLE: RETAIL SECTOR
BUSINESS GENERATES COMPLEX DATA

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DIGITAL DISRUPTORS
7000
jobs per day
1,5 TB data
per day
60 million
active users
75+ Countries
500+ Cities
1000s of
ops / city
Daily / Weekly
statistics
Real time
analythics
Up to 24 GB
per second
50 million
subscribers

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DECISIONS
INSIGHTS TO REAL TIME DECISIONS
§ Cross Selling
§ Dynamic Pricing
§ Shopping Offers
§ Portfolio management
HOW DATA DRIVES BUSINESS | REAL TIME RESULTS / BUSINESS / WHAT ?
SMART SOFTWARE
DATA
MANAGE LARGE DATA VOLUMES
§ High performance
§ Scalable
§ Real Time
INSIGHTS
DATA TO INSIGHTS AND PREDICTIONS
§ Mesure buying patterns
§ Know your customers
SMART ENTERPRISE / MOBILE APPLICATIONS

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CORNERSTONES OF SUCESS
DIGITAL DISRUPTORS
DISTRIBUTED DATA MAP AND REDUCE
CLOUD TECHNOLOGIES

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THE PROBLEM
§ Detect fraud
§ Example: Airline business
§ Frauds can be simple
§ Frauds can be very complex
§ False positives
§ Short-term bookings for expensive flights
§ Bookings for other code share partners
§ e.g. Book via Lufthansa for United Airlines
§ Booking with unknown credit card in an
away-region (PSCC)

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STUPID LONG LISTS OF DATA
THE SOURCE DATA

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IN ORDER TO GAIN SOME INSIGHT
WHAT DO WE NEED
§ Facts
§ All we can get
§ Connected!
Ø Fortunately Neo4j offers a great way for doing this

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§ Stupid long lists of data § Facts with relationships
§ Booking, passengers, flights, regions,
time, historic data
DATA HAS MORE THAN VALUES
COLLECTING ALL FACTS

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DIFFERENT SOURCES INTO ONE GRAPH

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SEARCHING FOR DATA

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WALKING THE GRAPH

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…AND FINDING ALL RELATED INFORMATION

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RDBMS Neo4j
Mathematical model Set theory Graph theory
Basic concepts Table Node
Relationship Implicit (keys) Explicit (type)
Query language SQL Cypher
Transactionality yes yes
Network access Proprietary REST + BOLT
COMPARISON RDBMS AND NEO4J

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THE BASIC DATA
FRAUD DETECTION

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Stolen user account Stolen credit card
NOW EASY TO SEE
HERE ARE SOME FRAUDS

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§ Human written rules
detect fraud
§ Rules are improved over
time
§ Deep learning
§ Human teaches
neuronal network
§ Frauds are detected
automatically
§ False positive are
eliminated over time
§ Graphical interactive
viewer
MANUAL
LEVEL 1
AUTOMATED RULES
LEVEL 2
MACHINE LEARNING
LEVEL 3

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LEVEL 2: RULE BASED
FRAUD DETECTION

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HOW DO I FLY FROM VIENNA TO LOS ANGELES?
ROUTING
§ Neo4j can find pathes
§ Very fast
§ Routing logic

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COMPLEX DATA
STRATEGIC DATA

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WHAT HAS APPLE TO DO WITH GERMANY?
STRATEGY

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THANK YOU
ANY QUESTIONS?
VISIT US AT OUR PRODYNA BOOTH

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TWITTER
@dkrizic

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FOR MORE INFORMATION VISIT PRODYNA.COM
CONTACT US
+49 69 597 724 0
info@prodyna.com
Facebook/prodynaag
Twitter/prodynaag

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DARKO KRIŽIĆ – PRODYNA FRANKFURT
PRODYNA IN YOUR REGION
§ PRODYNA AG
§ Ludwig-Erhard-Straße 12-14 65760 Eschborn
§ T +49 69 597 724 - 175 F +49 69 597 724 - 700 M +49 176 178 70 175
§ darko.krizic@prodyna.com prodyna.com