Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Detecting eCommerce Fraud with Neo4j and Linkurious

Last year, the global eCommerce market represented $1.9 trillions. As the market expands worldwide, the opportunity for fraud keeps growing with fraudsters constantly refining their tactics to outsmart anti-fraud frameworks. From chargeback fraud to re-shipping scam or identity fraud, numerous types of fraud can impact your organization. While collecting data is essential to enable real-time risk assessment, many organizations don’t have the necessary tools to find the insights needed to block fraud attempts.

Neo4j and Linkurious offer a solution to tackle the eCommerce fraud challenge. Their combined technologies provide a 360° overview of organization’s data and allow real-time analysis and detection of eCommerce fraud patterns and activities.

In this webinar, you will learn about:
- The current trends of eCommerce frauds and the risks for organizations;
- The challenges of detecting fraud tentatives in real-time and the advantage of the graph approach;
- How to use Linkurious’ graph visualization and analysis software to prevent and investigate eCommerce fraud.

  • Login to see the comments

  • Be the first to like this

Detecting eCommerce Fraud with Neo4j and Linkurious

  1. 1. Detecting eCommerce fraud with Neo4j and Linkurious. SAS founded in 2013 in Paris | | @linkurious
  2. 2. Agenda and speakers. ▪ Intro ▪ Graph technologies ▪ eCommerce Fraud ▪ Detection technical challenges ▪ Benefits of using Linkurious & Neo4j ▪ Demo ▪ How it works ▪ Use case ▪ Q&A Mohsan is a Business Developer at Linkurious. He works with companies worldwide to help them find solutions to uncover hidden insights in their connected data. “ ” Elise is a Marketing Project Manager at Linkurious. She works with Linkurious' partners to cover the emerging graph technology use cases. “ ”
  3. 3. Graph visualization and analysis startup founded in 2013. 200+ customers worldwide (NASA, Cisco, French Ministry of Finances). Linkurious Enterprise and Linkurious SDK. About Linkurious.
  4. 4. Unlocking the value of graph data. A graph is a set of data recorded as entities (nodes) and relationships (edges). Graph databases like Neo4j store & process large connected graphs in real-time. Linkurious’ software helps analysts easily detect and investigate insights hidden in graph data. Node STORE ACCESS Data Graph database Linkurious ORGANIZE Edge
  5. 5. Typical use cases. Cyber-security Servers, switches, routers, applications, etc. Suspicious activity patterns, identify impact of a compromised asset. IT Operations Servers, switches, routers, applications, etc. Impact analysis, root cause analysis. Intelligence People, emails, transactions, phone call records, social. Detecting and investigating criminal or terrorist networks. AML People, transactions, watch-lists, companies, organizations. Detecting suspicious transactions, identify beneficiary owners. Fraud Claims, people, financial records, personal data. Detecting and investigating criminal networks. Life Sciences Proteins, publications, researchers, patents, topics. Understanding protein interactions, new drugs. Enterprise Architecture Servers, applications, metadata, business objectives. Data lineage, curating enterprise architecture.
  6. 6. Fraud schemes that uses internet related means (emails, websites, etc) to present fraudulent solicitations to prospective victims or to conduct fraudulent transactions.
  7. 7. A large number of fraud schemes. Friendly fraud Affiliate fraud Account takeover Identity theft Reshipping fraud Promotion abuse Phishing Merchant fraud
  8. 8. A fertile ground for eCommerce fraud. eCommerce merchants loose 1.39% of revenue to fraud in average, which accounts for billions of dollars worldwide. Juniper, ”Online payment fraud whitepaper 2016-2020”. Organized networks New technologies eCommerce growth
  9. 9. Using a five-layers approach to detect online fraud. Endpoint centric Navigation centric Channel centric Cross-channel centric Entity link analysis Layer 1 Layer 2 Layer 3 Layer 4 Layer 5 Gartner Inc, “The conceptual model of a layered approach for fraud detection” in Market Guide, Online Fraud Detection: A Layered Approach
  10. 10. Few systems react in real-time & block transactions at the point of service. Relational databases & siloed products offer no cross-channel view. Challenges with traditional fraud solutions. Closed architecture makes it hard to keep pace with new schemes.
  11. 11. Cross-channel centric layer. Combine it with external data sources & improve risk assessment. Get a cross-channel & cross-product view of your client behaviors. Model your data into a single graph & add new type of data at anytime.
  12. 12. Entity-link analysis Uncover hidden relationships in your connected data. Identify fraud rings and suspicious patterns. Analyze activities and relationships within a network of related entities.
  13. 13. Demo: Detecting eCommerce fraud with Linkurious and Neo4j. - Modeling data into a graph - Cross-channel overview of the data - Investigation of entity relationships in Linkurious - Suspicious pattern detection and analysis
  14. 14. Modeling our data into a graph.
  15. 15. Load data into Neo4j from multiple data sources. Windows / Linux / Mac, on-premise or in the cloud, supports all modern browsers. Use Linkurious Enterprise off-the-shelf interface or build your custom application with Linkurious SDK. How it works. Omnichannel data (transactional data, behavior analytics, 3rd party data, user devices data, etc...) Synchronize automatically Neo4j Graph database Linkurious Enterprise or custom application
  16. 16. Background Fraud and compliance team of an international bank. Problem Increase of credit card fraud online and difficulty to detect suspicious cases. Benefit Detection of suspicious patterns and fast investigation. Online banking fraud detection.
  17. 17. Questions?
  18. 18.
  19. 19. Sources and links. Bibliography : ● DB-Engines. Knowledge Base of Relational and NoSQL Database Management Systems. Ranking per Categories. Available: [Avril 2017] ● eMarketer, “Worldwide Retail Ecommerce Sales: The eMarketer Forecast for 2016”. Available: ● Juniper, Online payement whitepaper 2016-2020. Available : t-fraud-wp-2016.pdf ● United states Department of Justice “19 People Indicted Following Investigations”. Available: d-and-money-laundering Images: ● Istock ● Growth icon by hans draiman from the Noun Project ● Cash flow icon by rflor from the Noun Project ● Dollar notification icon by Martin Lebreton from the Noun Project