Next Generation Fraud Solutions with Neo4j
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GraphTour Tel Aviv Stefan Kolmar, Neo4j
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Next Generation Fraud Solutions with Neo4j
- 1. Next Generation Solutions built on Neo4j Stefan Kolmar, VP Field Engineering Feb 2018
- 2. TEL AVIV, 13 FEBRUARY, 2018 Agenda ● Solutions using Neo4j ● Fraud ● Recommendations ● Conclusions
- 3. TEL AVIV, 13 FEBRUARY, 2018 Solutions: new mindset required?
- 4. TEL AVIV, 13 FEBRUARY, 2018 Solutions: new mindset Yesterday: - Static Applications - Designed to fulfill current requirements - Performance Constraints - Domain experts versus IT experts Tomorrow: - Flexible Applications - Designed to fulfill tomorrows requirements - Performance is not limiting - Domain experts work hand in hand with IT experts
- 5. TEL AVIV, 13 FEBRUARY, 2018 Evolution using Neo4j Neo4j Platform Graph Transactions Graph Analytics Data Integration Development & Admin Analytics Tooling Drivers & APIs Discovery & Visualization Developers Admins Applications Business Users Data Analysts Data Scientists 3rd Party Tools “The Graph Advantage” Domain know-how Professional Services PS Packages Graph Based Solution
- 6. TEL AVIV, 13 FEBRUARY, 2018 Evolution using Neo4j Neo4j Platform Graph Transactions Graph Analytics Data Integration Development & Admin Analytics Tooling Drivers & APIs Discovery & Visualization Developers Admins Applications Business Users Data Analysts Data Scientists 3rd Party Tools “The Graph Advantage” Domain know-how Professional Services PS Packages Graph Based Solution Neo4j enables Graph Based Solutions with a need for: - Agility - Intuitiveness - High Performance to support connected data scenarios - Scalable on traversing through connected data
- 7. TEL AVIV, 13 FEBRUARY, 2018 Look at this data…
- 8. TEL AVIV, 13 FEBRUARY, 2018 Swap glasses…
- 9. TEL AVIV, 13 FEBRUARY, 2018 … now look at it again, this time as a graph
- 10. Speed: Real time query enabled Graph Based Solutions Enables Up-Sell / Cross-sell Key Features Added Value 360 degree view on data Using data Connections as a value Intuitive: Supports Business Needs Flexible: enabled for additional requirements Finding patterns within the data Detect anomalies Prevent rather than detect Enables conversation across Functions Comply to regulations What-if Analysis Telco OSS GDPR Fraud Telco BSS Recomm endations MDM Resource efficient
- 11. TEL AVIV, 13 FEBRUARY, 2018 Fraud Detection Building Powerful Solutions to prevent Fraud Based on Neo4j
- 12. TEL AVIV, 13 FEBRUARY, 2018 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
- 13. TEL AVIV, 13 FEBRUARY, 2018 Who Are Today’s Fraudsters?
- 14. TEL AVIV, 13 FEBRUARY, 2018 Organized in groups Synthetic Identities Stolen Identities Who Are Today’s Fraudsters? Hijacked Devices
- 15. TEL AVIV, 13 FEBRUARY, 2018 “Don’t consider traditional technology adequate to keep up with criminal trends” Market Guide for Online Fraud Detection, April 27, 2015
- 16. TEL AVIV, 13 FEBRUARY, 2018 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
- 17. TEL AVIV, 13 FEBRUARY, 2018 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
- 18. TEL AVIV, 13 FEBRUARY, 2018 INVESTIGATE Revolving Debt Number of Accounts INVESTIGATE Normal behavior Fraud Detection With Discrete Analysis
- 19. TEL AVIV, 13 FEBRUARY, 2018 Revolving Debt Number of Accounts Normal behavior Fraud Detection With Connected Analysis Fraudulent pattern
- 20. TEL AVIV, 13 FEBRUARY, 2018 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.
- 21. Preventing Fraud Networks of People Processes and Transactions Ownership E.g. e-commerce Fraud, AML E.g. detecting fraud rings, finding connections and shortest paths E.g. AML, tax fraud, legal entities Data connections assist the business by identifying patterns
- 22. TEL AVIV, 13 FEBRUARY, 2018 The Power of Cypher Fraud Ring: MATCH ring = (suspect:AccountHolder)-[*]->(contactInformation)<-[*..5]-(:AccountHolder)-[*]->(suspect) RETURN ring
- 23. TEL AVIV, 13 FEBRUARY, 2018 Top Tier Electronic Payment Services Case studyApply to AML regulations Challenge • Needed to apply to AML regulation • Unability to provide reports out of RDBMS leading systems Transactions fragmented and transfered „from rings to rings“ • Neo4j is used to store and report on transaction over previous 24 months • Business Users / Fraud Analysts are enabled to investigate data and detect patters Use of Neo4j • Complies to Regulations • Neo4j also enabled the company to detect potential AML usage early and act against them “We have been unable to detect AML fraud patterns in the SQL based operational systems. Graphs and Graph visualisation is a key enabler technology.” – Top Tier Payment Service Result/Outcome
- 24. TEL AVIV, 13 FEBRUARY, 2018 What about Machine Learning?
- 25. TEL AVIV, 13 FEBRUARY, 2018 What about Machine Learning? Neo4j is an enabler technology: • Automized detection of Fraud patterns via Cypher • Detecting Paths • Graph Algorithms (eg Centrality, Community) • Algorithms as background tasks -> mark corresponding nodes • Automatically cancel Business Transactions • Score identified patterns and weigh • ….
- 26. TEL AVIV, 13 FEBRUARY, 2018 Why Graph is Superior for Fraud DetectionFraud Requirement Traditional Approaches Neo4j Approach Find connected data patterns over unlimited amount of „hops“ Complex queries with hundreds of join tables Simple single query traverses all enterprise systems Real-time acting on incoming events in ever changing formats for potential fraud Limitations inherited from SQL Database Schema Schema free database enables to connect any nodes with each other Effort required to add new data and systems Days to weeks to rewrite schema and queries Draw new data connections on the spot Time to deployment Months to years Weeks to months Response time to Fraud requests Minutes to hours per query Milliseconds per query Form of Fraud Incidents / Investigations Text reports that are not visual and prove very little Visuals patterns and the path to follow through your system Bottom line Long, ineffective and expensive Easy, fast and affordable
- 27. TEL AVIV, 13 FEBRUARY, 2018 How Neo4j fits into your environment
- 28. TEL AVIV, 13 FEBRUARY, 2018 Money Transferring Purchases Bank Services Relational database Develop Patterns Data Science-team + Good for Discrete Analysis – No Holistic View of Data-Relationships – Slow query speed for connections
- 29. TEL AVIV, 13 FEBRUARY, 2018 Money Transferring Purchases Bank Services Relational database Data Lake + Good for Map Reduce + Good for Analytical Workloads – No holistic view – Non-operational workloads – Weeks-to-months processes Develop Patterns Data Science-team Merchant Data Credit Score Data Other 3rd Party Data
- 30. TEL AVIV, 13 FEBRUARY, 2018 Money Transferring Purchases Bank Services Neo4j powers 360° view of transactions in real-time Neo4j Cluster SENSE Transaction stream RESPOND Alerts & notification LOAD RELEVANT DATA Relational database Data Lake Visualization UI Fine Tune Patterns Develop Patterns Data Science-team Merchant Data Credit Score Data Other 3rd Party Data
- 31. TEL AVIV, 13 FEBRUARY, 2018 Money Transferring Purchases Bank Services Neo4j powers 360° view of transactions in real-time Neo4j Cluster SENSE Transaction stream RESPOND Alerts & notification LOAD RELEVANT DATA Relational database Data Lake Visualization UI Fine Tune Patterns Develop Patterns Data Science-team Merchant Data Credit Score Data Other 3rd Party Data Data-set used to explore new insights
- 32. TEL AVIV, 13 FEBRUARY, 2018 Example Fraud Solution Architecture
- 33. Neo4j Database Cluster Data Visualization Neo4j APOC Fraud Detection Algorithms Management Dashboard Neo4j Bolt Driver Data Ingest Mgmt. … Customer Data Sources / Systems / Applications Legend: Neo4j Provided Components Custom built Neo4j/Customer Customer/SI Fraud Reports Real Time Alerts Batch Data Buffering (Queue) Real-Time Neo4j BrowserAdmin UI UI for Fraud Analysis System Specific Adapters / Scripts / Connecters Fraud Analysts Admin / SuperuserFraud Analysts Fraud Analysts
- 34. TEL AVIV, 13 FEBRUARY, 2018 Neo4j powered Fraud Solution Characteristic Benefit for Fraud Solution • Agility • Constant catch up with fraudster techniques supported • Enabled for Future Requirements • Solution can be built iteratively • Fast implementation cycles • Schema free DB supports “connect anything” • Intuitiveness • Enable Fraud Analysts to use Technology • Using visualization to detect pattern • Drilling into suspicious patterns • Speed • Unlimited number of traversals to detect complex connections within the data • Response time enables fraud prevention • Leverage Data Connections • 360 degree customer view enabled / provided • Scalability • Hardware efficiency with real-time patterns • TCO/ROI • Adding on top of existing infrastructure protects investments
- 35. TEL AVIV, 13 FEBRUARY, 2018 Recommendation EnginesBuilding Powerful Recommendation Engines With Neo4j
- 36. TEL AVIV, 13 FEBRUARY, 2018 “If you liked this, you might like that…” Powerful, real-time, recommendations and personalization engines have become fundamental for creating superior user experience and commercial success in retail
- 37. TEL AVIV, 13 FEBRUARY, 2018 Creating Relevance in an Ocean of Possibilities
- 38. TEL AVIV, 13 FEBRUARY, 2018 How Graph Based Recommendations Transformed the Consumer Web People Graph “People you may know” Disruptor: Facebook Industry: Media Ad-business Disruptor: Amazon Industry: Retail People & Products “Other people also bought” People & Content “You might also like” Disruptor: Netflix Industry: Broadcasting Media
- 39. TEL AVIV, 13 FEBRUARY, 2018 Today Recommendation Engines are At the Core of Digitization in Retail Product Recommendations Effective product recommendation algorithms has become the new standard in online retail — directly affecting revenue streams and the shopping experience. Logistics/Delivery Routing recommendations allows companies to save money on routing and delivery, and provide better and faster service. Promotion recommendations Building powerful personalized promotion engines is another area within retail that requires input from multiple data sources, and real-time, session based queries, which is an ideal task to solve with Neo4j.
- 40. TEL AVIV, 13 FEBRUARY, 2018 ... and Recommendation Engines are at the core of: Content Recommendations Content recommendation algorithms are the basis to use portals providing value added content — directly affecting the behaviour of the users and have them stay on the web page Fraud Taking timely action based on patterns / recommendations you find inside connected data . May require input from multiple data sources, and real-time, session based queries, which is an ideal task to solve with Neo4j. Social Networks Building powerful personalized engines to recommend new contacts, friends, based on patterns, preferences, status „friends-of-friends“ taking advantage of the value of connected data
- 41. TEL AVIV, 13 FEBRUARY, 2018 Why Graph Based Recommendation Engines? • Increase revenue • Create Higher Engagement • Mitigate RiskValue • Real-Time capabilities • Ability to use the most recent transaction data • Flexibility to incorporate new data sourcesPerformance
- 42. TEL AVIV, 13 FEBRUARY, 2018 The Impact of Bad Recommendations Characteristic Impact for Recommendations Examples • “Batch Oriented Recommendation” • Unable to react on real-time changes • Unable to fulfill real-time needs • Recommending “out-of-stock” products • Content recommendation, eg news: the latest news are the most important ones • Lack of Performance • Recommendation slow down the user interaction • Recommendation alternatives limited • Delayed response time lead to customer dissatisfaction • Recommend just the obvious (“similarities”) and inability to recommend more complex scenarios (Account specific and product specific and buying history and …) • Limited by Data Connections • Recommendations are limited by number of hops • Inability to recommend more complex correlations (eg product hierarchies and dependencies) • No complex recommendation algorithms supported • Missing Feedback Loop • Inability to react on Feedback • Customer never picks Top 3 recommendations • Recommendations are getting meaningless • No Graph Algorithm support • Limitations on Machine Learning approaches • “Centrality” for Products to be recommend can be essential
- 43. TEL AVIV, 13 FEBRUARY, 2018 Case Studies
- 44. TEL AVIV, 13 FEBRUARY, 2018 Case studySolving real-time recommendations for the World’s largest retailer. Challenge • In its drive to provide the best web experience for its customers, Walmart wanted to optimize its online recommendations. • Walmart recognized the challenge it faced in delivering recommendations with traditional relational database technology. • Walmart uses Neo4j to quickly query customers’ past purchases, as well as instantly capture any new interests shown in the customers’ current online visit – essential for making real-time recommendations. Use of Neo4j “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 • With Neo4j, Walmart could substitute a heavy batch process with a simple and real-time graph database. Result/Outcome
- 45. TEL AVIV, 13 FEBRUARY, 2018 Case studyeBay Now Tackles eCommerce Delivery Service Routing with Neo4j Challenge • The queries used to select the best courier for eBays routing system were simply taking too long and they needed a solution to maintain a competitive service. • The MySQL joins being used created a code base too slow and complex to maintain. • eBay is now using Neo4j’s graph database platform to redefine e-commerce, by making delivery of online and mobile orders quick and convenient. Use of Neo4j • With Neo4j eBay managed to eliminate the biggest roadblock between retailers and online shoppers: the option to have your item delivered the same day. • The schema-flexible nature of the database allowed easy extensibility, speeding up development. • Neo4j solution was more than 1000x faster than the prior MySQL Soltution. Our Neo4j solution is literally thousands of times faster than the prior MySQL solution, with queries that require 10-100 times less code. Result/Outcome – Volker Pacher, eBay
- 46. TEL AVIV, 13 FEBRUARY, 2018 Example Recommendation Solution Architecture
- 47. Neo4j Database Cluster Neo4j APOC Recommen dation Algorithms (Scheduled) Management Dashboard Neo4j Bolt Driver Data Ingest Mgmt. … Customer Data Sources / Systems / Applications Legend: Neo4j Provided Components Custom built Neo4j/Customer Customer/SI Batch Data Buffering (Queue) Real-Time Admin UI System Specific Adapters / Scripts / Connecters Admin / Superuser Apps Websites Affiliate Programs Points of sale User Interface Retail Web Shop functionality / Shipment / etc.
- 48. TEL AVIV, 13 FEBRUARY, 2018 Why Graph is Superior for Recommendation Engines Recommendation Requirement Traditional Approaches Neo4j Approach Usage of connected data over unlimited amount of „hops“ Complex queries with hundreds of join tables Simple single query traverses all enterprise systems Real-time 360 degree view on data within your System Performance limitations with increasing number of connections / hops Traversing over connections in near real-time provided Effort required to add additional data sources to support reco Days to weeks to rewrite schema and queries Minutes to draw new data connections Time to deployment Months to years Weeks to months Response time to Recommendations Minutes to hours per query Milliseconds per query Machine Learning Enablement Static Database scheme leads to static processes ML algorithms can use Graph algorithms and take advantage of connected data Bottom line Long, ineffective and expensive Easy, fast and affordable
- 49. TEL AVIV, 13 FEBRUARY, 2018 What about Machine Learning?
- 50. TEL AVIV, 13 FEBRUARY, 2018 What about Machine Learning? Neo4j is an enabler technology: • Detecting Recommendation patterns via Cypher queries • Recommendation Algorithms based on scores • Feedback loop • Learn from feedback (eg never used “friends recommendation”) and change scoring • Algorithm to automatically add relevant connections • ….
- 51. TEL AVIV, 13 FEBRUARY, 2018 Neo4j powered Recommendation Engine Characteristic Benefit for Recommendation Solution • Agility • Constant learning of recommendations given feedback enabled • Enabled for Future Requirements • Solution can be built iteratively • Fast implementation cycles • Schema free DB supports “connect anything” • Intuitiveness • Enable Business Analysts to use technology • All channels and data sources can be easily connected • Speed • Unlimited number of traversals to detect potential recommendations • Response time enables fraud prevention • Leverage Data Connections • 360 degree customer view enabled / provided • Scalability • Hardware efficiency with real-time patterns • TCO/ROI • Adding on top of existing infrastructure protects investments
- 52. TEL AVIV, 13 FEBRUARY, 2018 Conclusion (graphs)-[:ARE]-> (everywhere) and (Solutions)-[:NEED]-> (graphs)
- 53. TEL AVIV, 13 FEBRUARY, 2018 Who can help? Neo4j Platform Graph Transactions Graph Analytics Data Integration Development & Admin Analytics Tooling Drivers & APIs Discovery & Visualization Developers Admins Applications Business Users Data Analysts Data Scientists 3rd Party Tools “The Graph Advantage” Domain know-how Professional Services PS Packages Graph Based Solution Professional Services: - Extend and leverage Domain Expertise - Best Practices - Using Building Blocks - Don’t “re-invent the wheel” - Speed up development and deployment - Access to Neo4j infrastructure (Development, Support, Product management)
- 54. Next Generation Solutions built on Neo4j Stefan Kolmar, VP Field Engineering Feb 2018
Editor's Notes
- Good afternoon, Welcome to the talk “Next Generation Solutions built on Neo4j” where I would like to show you some possibilities how to take advantage of Graphs building Solutions. My name is Stefan Kolmar and I’m running the Field Engineering for Neo4j in EMEA and APAC
- For the agenda I would like to start with some general remarks how Neo4j fits building next generation solutions. Then I would like to get a bit more specific on the advantages for Fraud and Recommendations And end with conclusions
- Question is: with all changes in technology, new abilities to work with data and the ever increasind amount of data: is a new thinking required?
- I would say: „Yes“ Yestredays application have been quite often static and have been designed to fulfill the current requirements. Performance access the data was a limiting factor – quite often there was a diconnect between domain experts and the ones who implement it afterwards: the IT experts Click what we need tomorrow is flexible applications, designed to fulfill ever changing requirements. If you use the right technology and you use it accordingly, performance is not a limiting factor any more. Thhe goal is that domain experts work hand in hand with IT experts.
- How can we get to those solutions? Let me first highlight the evolution using Neo4: With Neo4j as the graph database, we are building the Graph Platform as an entire infrastructure. Together with 3rd party tools you can build what I would call the graph advantage – an ecosystem based on graph to be used. If you know use this Ecosystem with the Donain know how skillset, use Professional Services and potential Professional Services Packages, you can build up a graph based solution
- Neo4j enables Graph Based Solutions with a need for: Agility -- constantly changing requirements Intuitiveness – so thhat everybody in your organsation can understand and influence the soluton High Performance to support connected data scenarios Scalable on traversing through connected data rather than building ad-hoc sql queries
- Is there a new thhinking required? YES: if you look at these data, what do you see?
- If you know swap glasses …
- Structural patterns emerge you’ll immediately see CLUSTERS RINGS BETWEENERS You SEE it, but WAY MORE IMPORTANTLY, you can query it!
- Some categories of solutions we have seen and we have worked on with customers are in Telco OSS and BSS GDPR architectures as foundations for solutions, Recommendations, Fraud And MDM Key features of these solutions are
- Just to set the stage… We’re not going to give you the complete market analysis on Fraud, you guys probably know this better than us. But what you can say, is that it’s a significant cost of business, and it’s getting increasingly complex.
- Who are today’s fraudsters? Well, I’m sorry to say, but often they are more sophisticated than most systems one would have to prevent them. The biggest fraudsters don’t operate as lone anomalies today….
- Who are today’s fraudsters? Instead of an individual, fraudsters are typically organized in groups - also called Fraud rings Instead of using their own identify, they manufacture identities - also called synthetic identities They also perpetrate fraud using stolen identities and hijacked devices
- Its obvious that traditional technologies which were aimed at individuals and their behavior are inadequate to detect and prevent sophisticated fraud rings. So why is that?
- Let’s look at what traditional Fraud Detection looks like. Endpoint-centric solution that analyzes the characteristics of the PC, mobile or telephony device used to access the enterprise system. 2) Navigation- and network-centric solution that analyzes the navigation of a session. - usually by IP address and user ID, to see if it looks anomalous relative to normal user or peer group behavior 3) — User- or entity-centric solution, in which transactions are compared to what is expected of the user or entity. To support identity prong, this layer also includes integration of external and internal data to help vet an identity, especially in a risky transaction (such as a new account application), or verify a suspect authentication or high-risk transaction.
- What all these methods are examples of is discrete analysis, which is very effective if you know what to look for. The weaknesses though, are that discrete analysis do a poor job when you want to detect Fraud rings, when someone is using fake IP-adresses,… the list… The challenge with systems that focus on discrete analysis for fraud detection is their inability to detect fraud rings or synthetic identities as this requires additional context. Let’s take an example
- [In this simple fraud detection approach to detect credit card fraud, it is relatively easy to spot outliers. But what if the fraudster commits fraud while still exhibiting normal behavior. Well - this is exactly how fraud rings operate]
- [A fraud ring rarely strays outside the normal behavior band. Instead they operate within normal limits and commit widespread fraud. This is very hard to detect by systems that are looking for outliers or activities outside the normal band.]
- Today, financial services firms need to augment their discrete analysis capability with connected analysis. Whether it is a fraud ring or a stolen/synthetic identities, its is powerful to use a graph database
- So if you want to avoid Fraud to happen, you have a good chance to use the connections within the data, Such as for a network of People -> detecting Fraud Rings Or Detect connections within processes and transactions Or Detecting connections within ownerships To identify patterns for money laudry, tax fraud and legal entities
- Cypher is your friend: with Cypher you get the abilities to traverse and use within your connections to easily identify connections/relationships
- Who is using neo4j to identify fraud: The first customer I wanted to highlight is a top tier payment service: They were basically forced by reagulations to put systems in place to identify potential ant money laundry. Their SQL based system were unable to handle the requirements and therefore they have built up a system based on graphs Neo4j As a result they are able to group on one side several accounts connect as „senders“ of money and on the othher side thhe group of receivers. If then as an example thousands of transactions are done from a group of people in lets say tel Aviv are sending to a group of people in Columbia, it is detected and can be investigated....
- Can Neo4j also be used to support machine learning?
- Neo4j is an enabler technology: Automized detection of Fraud patterns via Cypher queries Automized detection of Fraud Rings Shortest Paths / Paths existing Using Graph Algorithms as the foundation (eg Centrality, Community) Run Pattern detecting Algorithms as background tasks and mark corresponding nodes Automatically cancel Business Transactions (eg CC) ….
- If we now compare using Neo4j with traditional approaches, we see that Neo4j supports fnding connected patterns over unlimited amounts of hops … versus traditional approaches are limited by sql join limitations Neo4j helps to support the ever changing needs of fraudsters changing the way they commit fraud by allowing to connect anything with anything versus SQL to rely on a schema with schema changes to be hard at best New data systems can be rapidly integrated With the Neo4j agility, deployment time can be greatly reduced As a bootom line: Neo4j helps you to build an easy, fast and affordable solution.
- If you see existing environments, you most often see relational databases assisting data science teams to support investigations. This is good for dicrete analysis, but does not provide a holistic view of data relationships.
- To integrate the data, this data sources are then often pushed into a data lake. This is good for Map reduce algorithms, and it is good for analytical workloads. Still provides no holistic vies and is not good for operational workloads. Typically you talk here about processes to detect fraud which takes several days, if now more.
- Now if you load the relevant data into Neo4j, you can provide a 360 degree view on the data in real-time. With appropriate visualisation you give data scientists the ability to access the data in real-time. Ecisting transactional system will be connected in a way that the transaction stream is loaded to get sense out of the transactions in conjunction with the existing connected data, and you can respond in real time with alerts and notifications.
- The data set can be used to explore new insights and find and detect patterns.
- So this is an example architecture with the building blocks you need to build a Fraud Solution based on Neo4j. In the center of this architecture you see Neo4j database as a cluster. Fraud detection algorithms can work automatically directly on the database to detect fraud patterns and derive connection. Applications on top designed for Fraud Analysts or Managers with dashboards could access the database via the Bolt driver. The Neo4j database is directly connected in batch and/or realtime with all other source databases to import the relevant data intially, and, feed detetctions from fraud investigations into othher systems. With that a CRM system can get relevant information so that as an example in a CRM system a suspicious pattern/customer is marked, or, a transaction such as a credit card transaction can be cancelled.
- So if we summarize the characteristics of a Neo4j powered Fraud Solution, we can conclude that this adds value with agility: you can constantly catch up with fraudster activities and you are enabled for future reuirements Intuitiveness: Fraud analsysts are enabled to use the technology and can use visualisation techniques to detetct patterns Speed: traversals are cheap and provide performance so thhat you can detect complex connections You can Lvereage data connections to get a 360 degeree view on customers Hardware is eficiently used with real time patterns
- Moving on with solutions and introducing a second use case, this presentation is a high level version of why you should build recommendation engines with graphs and Neo4j.
- To set the stage — Powerful, real-time, recommendations and personalization engines have become fundamental for creating superior user experience and commercial success
- In a digitized world, the reason Recommendations are so important, is because there’s an ocean of possibilities and choices — how do you create personal relevance in a library of 20 thousand movies, or personalize experience in a music app with 10 million songs, or recommend the right product to a user when your inventory consists of 250 thousand products and configurations? This has become a challenging task form a data processing perspective, especially if you’re using a traditional relational database infrastructure.
- Graph based recommendations have transformed the consumer web. Social networks rely on recommendations — people you may know — and the data network effects of this graph is also what is disrupting the world of media advertising business. Connecting people with products — the other people bought recommendation — has fundamentally transformed online retail Content recommendations now lies at the core of user experience in the world of content (i.e. streaming of movies and music) anticipate the question: which of these are using Neo4j and graph databases — no one, their recommendations are graphs, even though they might not use a graph database
- Effective product recommendation algorithms has become the new standard in online retail — directly affecting revenue streams and the shopping experience. Building powerful personalized promotion engines is another area within retail that requires input from multiple data sources, and real-time, session based queries, which is an ideal task to solve with Neo4j. Routing recommendations allows companies to save money on routing and delivery, and provide better and faster service.
- If you take big players like in the content are like youtube you expect recommendations to find relevant information to your preferences Or take social networks where recommendations of relevant connections are essential And in a way also fraud is a kind of recommendation as you suggest a fraud analsyst for potential investigations.
- Out of all of our use cases, in my opinion, retail recommendations is the easiest one to hammer out a value proposition. The reason you should build graph based recommendation engines are of course, Increase revenue (if your a retailer for example). Create higher engagement, and mitigate risk. The reason companies chose Graph Databases to build recommendation engines is for the real-time capabilities, ability to use the most recent transaction data, and have the flexibility to incorporate new data sources.
- If you see what the result of bad recommendations could be, You can state that: Batch oriented or pre-calculated recommendation make it impossible to react on real time changes and real time needs. Just think that you are recommending out-of stock products, or, in a news content recommendations you are not able to recommend the latest news which just happened. The lack of performance: if your recommendation is slow and prevents your customer continuing the shopping experience without delays, you will get a bad user experience. If the number of data connections is limited, you may not be able to recommend complec correlations Good recommendation systems will work with feedback loops, which gives the algorithm the ability to learn from feedback and provide top 3 recommendations
- Walmart uses Neo4j to quickly query customers’ past purchases, as well as instantly capture any new interests shown in the customers’ current online visit – essential for making real-time recommendations. • With Neo4j, Walmart could substitute a heavy batch process with a simple and real-time graph database.
- eBay is now using Neo4j’s graph database platform to redefine e-commerce, by making delivery of online and mobile orders quick and convenient. The MySQL joins being used created a code base too slow and complex to maintain. Our Neo4j solution is literally thousands of times faster than the prior MySQL solution, with queries that require 10-100 times less code.
- Here is a sample architecture how a recommendation Solution could look like. The heart of this is the Neo4j database. The recommendation algorithms can work diretcly on the database to derive connections
- RDBMS Cannot Handle Connected Data Non-Native Graph Technologies Break Down Traditional approaches produce tabular output that is hard to follow. In contrast, Neo4j produces simple, easily understood pictures of how personal data flows through all your systems.
- Neo4j is an enabler technology: Detecting Recommendation patterns via Cypher queries Recommendation Algorithms typically based on scores Feedback loop on recommendations Learn from feedback (eg never used “friends recommendation”) and change scoring Algorithm can automatically add relevant connections ….
- Who can help –> additional help? Move to conclusions