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GraphDay Stockholm - Fraud Prevention
This document summarizes how several large companies, including Adidas, eBay, and Walmart, are using Neo4j graph databases to gain insights from their data. Adidas uses Neo4j to combine product and content data into a single searchable graph to personalize customer experiences. eBay uses Neo4j to optimize delivery routing as their previous system could not handle growth. Walmart uses Neo4j to provide personalized recommendations to customers on their website. The document then discusses how graph databases are better suited than relational databases for real-time, dynamic queries on connected data. It provides examples of using graph analysis to detect fraud, such as insurance scams and banking fraud rings.
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GraphDay Stockholm - Fraud Prevention
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- 2. A Graph IsConnected Data
- 4. Neo4j solves challengesfor some of the most powerful 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. This shift ishappening! Why?
- 6. Operational Databases Applications X Pre-computed Queries (Data Warehouse/ RDBMS) Real-time & Dynamic Queries (Graph Database) Why Graphs Now? We need something that is: • Flexible to change • Scalable to many problems • Intuitive to understand • Instantly responsive
- 7. Main Strengths Main Risks • Widespread competenceand familiarity • Integration with wide range of tools • “Analytical questions in transactional time” • Flexible and scalable to future needs • New tools require new competence • Misunderstandings can result in tool being wrongly applied • Incapable of meeting flexibility and performance demands • Lacks game changer success stories — status quo is risky in disruptive times Ways of Thinking About Data Pre-computed Queries (Data Warehouse / RDBMS) Real-time & Dynamic Queries (Graph Database)
- 8. A Graph Example: FraudPrevention
- 9. Fraud Detection & Prevention Types of Fraud • Retail Banking Fraud • Insurance Fraud Identity types •Stolen • Fake • Synthetic Types of Analysis • Discrete • Connected
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- 11. • Tens of billions of dollars lost every year by U.S. Banks.(1) • 25% of total consumer credit charge-offs in the United States.(2) •10% to 20% of unsecured bad debt at leading U.S. and European banks is misclassified, and is actually first-party fraud.(3) First-Party Fraud Impact (1) Experian: http://www.experian.com/assets/decision-analytics/white-papers/first-partyfraud-wp.pdf (2) Experian: http://www.experian.com/assets/decision-analytics/white-papers/first-partyfraud-wp.pdf (3) Business Insider: http://www.businessinsider.com/how-to-use-social-networks-in-the-fight-against-first-party-fraud-2011-3
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- 22. Doing Connected Analysis is Challenging • Large amounts of data and relationships must be processed • New data and relationships are continually being added •Fraud rings must be uncovered in real-time to prevent fraud
- 23. Adopting a Pattern-orientedMindset Search-oriented • Good when you know exactly what you’re looking for • Primarily based on explicit search criteria Pattern-oriented • Good when you want to suggest what might fit • Primarily based on implicit information, often many “hops” away
- 24. The Case ForInnovation with Graphs During past 20 years, society has become hyperconnected. We considered how regular people tend to think and reason, and modeled Neo4j to match that. Neo4j allows you to naturally map together the data that matters to you in a graph — like a mind map! … Graph structure scales to many problems, and is highly flexible to change. … Unlock the business value of connections and relationships in data. … How can we discover insights hidden behind the complexity? … Traditional technology does not handle complexity well. … Information-wise, gone from a small town to a metropolis. Complexity has exploded. … Need for better insights driven by competition and disruption. There is a shift happening now. The world’s largest companies rely on Neo4j. The competitive advantage is real. … In uncertain times, many consider the risks of change, but what are the risks of not adapting? … Get in the driver’s seat. Be the bringer of innovation. … The Problem The Solution The Future
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