Graphs in Action
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Three leading retailers - Adidas, eBay, and Walmart - are using Neo4j graph databases to improve their operations and customer experiences. Adidas combines product and content data into a searchable graph to personalize customer experiences. eBay uses Neo4j to optimize delivery routing as growth strained their previous system. Walmart employs Neo4j recommendations to provide relevant personalization at scale. Neo4j allows these retailers to better connect and leverage their different data sources.
Graphs in Action
- 1. Driving Digital Transformation With Neo4j GRAPHS IN ACTION Atlanta, Sept 8, 2016
- 2. Social networks RetailHR & Recruiting Manufacturing & Logistics Health Care Telco Today we see graph-projects in virtually every industry Finance
- 3. Retail
- 4. NEO4j solves retail-related challenges for some of the largest companies in the world Adidas uses Neo4j to combine content and product data into a single, searchable graph database which is used to create a personalized customer experience “We have many different silos, many different data domains, and in order to make sense out of our data, we needed to bring those together and make them useful for us,” – Sokratis Kartelias, Adidas eBay Now Tackles eCommerce Delivery Service Routing with Neo4j “We needed to rebuild when growth and new features made our slowest query longer than our fastest delivery - 15 minutes! Neo4j gave us best solution” – Volker Pacher, eBay Walmart uses Neo4j to give customer best web experience through relevant and personal recommendations “As the current market leader in graph databases, and with enterprise features for scalability and availability, Neo4j is the right choice to meet our demands”. - Marcos Vada, Walmart
- 5. End Consumers Component Manufacturers Logistics Traditional Retail Value Chain RetailersWholesalers Assembly Plants
- 6. PAYMENTS SALES- CHANNELS SUPPLY CHAIN PRODUCTS MARKETING CRM CUSTOMER EXPERIENCE THE ONLINE RETAIL VALUE CHAIN
- 8. PAYMENTS SALES- CHANNELS SUPPLY CHAIN PRODUCTS MARKETING CRM CUSTOMER EXPERIENCE Store Mobile Shipping Inventory Express goods Home delivery Webstore
- 9. PAYMENTS SALES- CHANNELS SUPPLY CHAIN PRODUCTS MARKETING CRM CUSTOMER EXPERIENCE Store Mobile Shipping Inventory Express goods Home delivery Ratings Price-range Category Webstore
- 10. PAYMENTS SALES- CHANNELS SUPPLY CHAIN PRODUCTS MARKETING CRM CUSTOMER EXPERIENCE Store Mobile Shipping Inventory Express goods Home delivery Ratings Price-range Category Content Promotions Online advertising Webstore
- 11. PAYMENTS SALES- CHANNELS SUPPLY CHAIN PRODUCTS MARKETING CRM CUSTOMER EXPERIENCE Store Mobile Shipping Inventory Express goods Home delivery Ratings Price-range Category Content Promotions Online advertising Loyalty Programs Returns Feedback reviews Tweets Emails Customer support Webstore
- 12. PAYMENTS SALES- CHANNELS SUPPLY CHAIN PRODUCTS MARKETING CRM CUSTOMER EXPERIENCE Store Mobile Shipping Inventory Express goods Home delivery Ratings Price-range Category Content Promotions Online advertising Loyalty Programs Returns Feedback reviews Tweets Emails Customer support Credit Card Cash Mobile Pay Purchase History PAYMENTS Webstore
- 13. Digital transformation in retail today requires to put all this data into good use
- 16. Related products People who bought X also bought Y Recommendations (In Real-Time) The main product
- 21. LOOKS_AT Returns Home delivery Inventory Express goods Complaints reviews Tweets Emails Location/ KITCHEN AID SERIES Promotions Bundling
- 22. LOOKS_AT Returns Purchase History Price-range Home delivery Inventory Express goods Complaints reviews Tweets Emails Category Promotions Bundling Location/ KITCHEN AID SERIES
- 23. LOOKS_AT Returns Purchase History Price-range Home delivery Inventory Express goods Complaints reviews Tweets Emails Category Promotions Bundling Location KITCHEN AID SERIES
- 26. Data stored as a graph
- 27. TECHNICAL LEGACY
- 30. Pre-computed Purpose has to pre-determined Limited Context Static Non-graph approach RDBMS Real-Time Recommendations Dynamic Highly contextual Flexible and Scalable Graph approach
- 31. To get results, in real time, from a dataset that is highly interconnected – you need a graph database!
- 32. THANK YOU!
- 33. Social networks RetailHR & Recruiting Manufacturing & Logistics Health Care Telco Today we see graph-projects in virtually every industry Finance
- 34. Finance
- 35. LEVERAGING GRAPHS TO FIGHT ECONOMIC FRAUD
- 37. The Impact of Fraud The payment card fraud alone, constitutes for over 16 billion dollar in losses for the bank-sector in the US. $16Bpayment card fraud in 2014* Banking $32Byearly e-commerce fraud** Fraud in E-commerce is estimated to cost over 32 billion dollars annually is the US.. E-commerce The impact of fraud on the insurance industry is estimated to be $80 billion annually in the US. Insurance $80Bestimated yearly impact*** *) Business Wire: http://www.businesswire.com/news/home/20150804007054/en/Global-Card-Fraud-Losses-Reach-16.31-Billion#.VcJZlvlVhBc **) E-commerce expert Andreas Thim, Klarna, 2015 ***) Coalition against insurance fraud: http://www.insurancefraud.org/article.htm?RecID=3274#.UnWuZ5E7ROA
- 38. Who Are Today’s Fraudsters?
- 39. Organized in groups Synthetic Identities Stolen Identities Who Are Today’s Fraudsters? Hijacked Devices
- 40. “Don’t consider traditional technology adequate to keep up with criminal trends” Market Guide for Online Fraud Detection, April 27, 2015
- 41. Endpoint-Centric Analysis of users and their end-points 1. Navigation Centric Analysis of navigation behavior and suspect patterns 2. Account-Centric Analysis of anomaly behavior by channel 3. PC:s Mobile Phones IP-addresses User ID:s Comparing Transaction Identity Vetting Traditional Fraud Detection Methods
- 42. Unable to detect • Fraud rings • Fake IP-adresses • Hijacked devices • Synthetic Identities • Stolen Identities • And more… Weaknesses DISCRETE ANALYSIS Endpoint-Centric Analysis of users and their end-points 1. Navigation Centric Analysis of navigation behavior and suspect patterns 2. Account-Centric Analysis of anomaly behavior by channel 3. Traditional Fraud Detection Methods
- 43. INVESTIGATE Revolving Debt Number of Accounts INVESTIGATE Normal behavior Fraud Detection With Discrete Analysis
- 44. Revolving Debt Number of Accounts Normal behavior Fraud Detection With Connected Analysis Fraudulent pattern
- 45. CONNECTED ANALYSIS Augmented Fraud Detection Endpoint-Centric Analysis of users and their end-points Navigation Centric Analysis of navigation behavior and suspect patterns Account-Centric Analysis of anomaly behavior by channel DISCRETE ANALYSIS 1. 2. 3. Cross Channel Analysis of anomaly behavior correlated across channels 4. Entity Linking Analysis of relationships to detect organized crime and collusion 5.
- 46. Modeling a fraud ring as a graph ACCOUNT HOLDER 2 ACCOUNT HOLDER 1 ACCOUNT HOLDER 3
- 47. ACCOUNT HOLDER 2 Modeling a fraud ring as a graph ACCOUNT HOLDER 1 ACCOUNT HOLDER 3 CREDIT CARD BANK ACCOUNT BANK ACCOUNT BANK ACCOUNT PHONE NUMBER UNSECURED LOAN SSN 2 UNSECURED LOAN
- 48. ACCOUNT HOLDER 2 Modeling a fraud ring as a graph ACCOUNT HOLDER 1 ACCOUNT HOLDER 3 CREDIT CARD BANK ACCOUNT BANK ACCOUNT BANK ACCOUNT ADDRESS PHONE NUMBER PHONE NUMBER SSN 2 UNSECURED LOAN SSN 2 UNSECURED LOAN
- 49. FRAUD DEMO
- 50. USING NEO4j FOR REAL-TIME CONNECTED ANALYSIS
- 51. Account-Centric Analysis of anomaly behavior correlated across channels 4. Entity Linking Analysis of relationships to detect organized crime and collusion 5. CONNECTED ANALYSIS Endpoint-Centric Analysis of users and their end-points Navigation Centric Analysis of navigation behavior and suspect patterns Account-Centric Analysis of anomaly behavior by channel DISCRETE ANALYSIS 1. 2. 3. Augment Fraud Detection with Neo4j Traditional Vendors
- 52. ACCEPT / DECLINE MANUAL User/Transaction CONNECTED ANALYSIS User/Transaction ACCEPT / DECLINE(DISCRETE ANALYSIS) + User/Transaction (sub-second performance to any data size and connection) ACCEPT / DECLINE REAL TIME TRADITIONAL VENDORS (DISCRETE ANALYSIS) (DISCRETE ANALYSIS) ACCEPT / DECLINE How Neo4j fits in
- 53. • Today’s fraudsters are organized and highly sophisticated • Legacy technology does not detect fraud sufficiently and in real-time • Graph-databases enable you to discover fraudulent patterns in real- time • Augment your current fraud detection infrastructure with connected analysis KEY TAKE AWAYS
- 54. THANK YOU!