graph analytics use cases

Graph analytics can identify such indirect relationships across multiple types of entities and relationships (addresses, co-owned companies, IP addresses, phone numbers, transactions, etc.). It’s true that social media applications remain natural users of graph databases and analytics. Knowledge Graphs. Additional use cases for graph databases. This requires exploring what the client or transaction are connected to. As a Data Scientist, you should be able to solve problems in an efficient manner and Graphs provide a mechanism to do that in cases where the data is arranged in a specific way. Don’t worry if yours is not listed – we have experience with many projects beyond those listed here, and we’d love to hear about your project and how we can help. This repository shows the uses cases from all the participants of the Knowledge Graph Construction Community Group. using the neo4s app from splunkbase. In this article we will provide a series of examples where graph analytics can be used to fight back against money laundering. Finally, if you have the requirement to push on with your graph, you will probably choose a graph store or graph database like Neo4j that you can connect back to Splunk, e.g. As a result, Graphistry is ideal for a variety of investigations and use cases. Using a graph of phone calls of 600M users they create graph features used in a machine learning model classifying a phone call as being a scam call or non-scam call in real-time. This can be a very cumbersome process when the data is scattered across different tabs. They let you apply pattern recognition, classification, statistical analysis, and machine learning to these models, which enables more efficient analysis at scale against massive amounts of data. Sometimes that requires following a long chain of ownership relationships by taking into account relevant ownership thresholds. However, existing graph analytics pipelines compose graph-parallel and data-parallel systems, leading to extensive data movement and duplication and a complicated programming model. How to include my own use case in the KG-Construction CM? However, these companies are also among the least able to take advantage of cloud-based graph offerings, such as TigerGraph Cloud, due to stringent data regulations. Machine learning technologyis now more accessible than ever to businesses. This metric measures the importance of a node in a graph as a function of the importance of its neighbors. To overcome these obstacles, you need a connected data technology – a graph database. If we were to look only at Jaime Benson and his direct connections, nothing suspicious would appear. Why are the recommendations on Amazon.com always so spot-on? The client may be part of a larger criminal ring or the transaction may be part of a bigger scheme. Use Cases of Graph Computation. The Graph database reveals the complex and hidden relationships between separate data sets, allows you to analyze them, to further improve your business processes, and make smarter business decisions, faster. The problem: Round-tripping is the process where funds are returned after being transferred to an entity, shell company, financial instruments, location, or a person that has lower regulatory standards or obligations – giving the impression that the funds have derived from a clean source and thus completing a round trip.The more entities involved in the round trip, the easier it is to miss a link in the chain. This research provides technical professionals dealing with data and analytics an overview of graph database use cases and their architecture. Tiger Graph showed how they worked with China Mobile to detect phone-based scams using real-time graph analytics. How’s it possible that LinkedIn can show all your 1st, 2nd, and 3rd -degree connections, and the mutual contacts with your 2nd level contacts in real-time. John Smith is connected to 4 individuals. So enough use cases. The optimal setup for any of these use cases is: Graphlytic for graph visualization, modeling, and analytics with the Neo4j graph database for storing data in a graph structure. It would give the wide view of customers based upon what entities (nodes) they belong to and what properties they have and the relationships (edges) between … Linkurious Enterprise leverages graph analytics to help compliance teams uncover complex schemes and rely on a more holistic picture of their clients for their investigations. The money has been moved without the recipient having to do a single large deposit. As criminals become more and more sophisticated in how they launder money, so too do the tools that help fight back against them. You can learn more about some of the more common ones below. Manually looking at the ownership structure of each company to identify its UBOs and repeating the process until there are no new UBOs is a time consuming and error-prone process. The most common use case for graph databases are analytic. Eigen Vector Centrality . For example, you could ask 3 of your neighbors to each deposit $4,999 and then wire you the money. Share. How graph analytics can help: If a company is owned by a single shareholder, things are easy. Graphs can be used to detect disasters such as hurricanes, earthquakes, tsunami, forest fires and volcanoes so as to provide warnings to alert people. The Spark + AI Summit Europe 2019 showcased some use cases of Graph Analytics. Avoiding this sort of duplication is a complex problem in an IT system with different silos. In his recent Strata Santa Clara talk and book, Neo Technology’s founder and CEO Emil Eifrem listed other uses cases for graph databases and analytics: Use Cases . I wanted to learn more about graph analytics and explore some specific use cases where the use of graph analytics can lead to new customer, product, campaign, and operational insights. If an account has done fraud in past, it is highly probable that the connected accounts are also susceptible to fraud. Make sure you choose the right graph database for your project. Github users: Option 1 (recommendable): Make a fork of the repository to your own personal account. When that happens, instead of a round trip, a compliance analyst would see two distinct money flows and fail to identify the money laundering risk. About Us ... pushed computing to a tipping point. Without more information, it’s important to explore as many leads as possible. Gradoop is an open source (ALv2) research framework for scalable graph analytics built on top of Apache Flink.It offers a graph data model which extends the widespread property graph model by the concept of logical graphs and further provides operators that can be applied on single logical graphs and collections of logical graphs. Understanding that Jaime Benson is associated to a high risk individual requires following his relationships with 2 companies and 2 addresses. © 2020 Neo4j, Inc. Atech is owned at 25% by Acme, at 60% by Invest Inc. and at 15% by Global Corp. Acme is itself owned at 95% by John Smith and at 5% by Paul Simon. In this case, we transformed property transactions into a Knowledge Graph that contains buyers, sellers, brokers, financial institutions etc. Here are some of the more common use cases for graph analytics. Big Data and Advanced Analytics - 16 Use Cases from McKinsey Chief Marketing & Sales Officer Forum Graphlytic can be used as: A) Graphlytic product ordered by the end-customers where standard product features and various support levels are available. 5 Graph Analytics Use Cases. The problem: Financial institutions are tasked with screening their clients to identify their potential ties with politically exposed persons (PEPs) or individuals and organizations that are in sanctions lists (such as the lists published by the Office of Foreign Assets Control). A lot of anti-money laundering use cases require identifying suspicious connections whereas graph analytics is designed to analyze complex connections from big data at scale. Money flows via different operations through Hooli Ltd and Globex Corp which is based in a tax haven. Click here for Part II. Here are the top five use cases of graph database technologies: TABLE OF CONTENTS Introduction 1 Fraud Detection 2 Real-Time Recommendations 4 Master Data Management 6 Network & IT Operations 8 Identity & Access Management 10 Conclusion 12 “Stop merely collecting data points, and start connecting them.” 2 neo4.com The Top 5 Use Cases of Graph Databases Use Case #1: Fraud … In Finance Perspective: Another use case would be to capture fraud using these family IDs. Anti-money laundering (AML) and graph analytics is a match made in heaven. The financial industry is using graph analytics to address a variety of use cases. How graph analytics can help: Graph analytics can help detect rings of clients interconnected across personally identifiable information such as an address, a phone number, a date of birth, an IP address, etc. The problem: What if you’re a drug dealer with a lot of cash that you would like to deposit in a bank account that you control? Formally, A Graph is a pair of sets. But graphs and graph databases provide relationship models. Use Cases Graphlytic can be used on any graph data. A lot of anti-money laundering use cases require identifying suspicious connections whereas graph analytics is designed to analyze complex connections from big data at scale. 10 Graph Analytics Market, By Vertical (Page No. There are a whole class of analytics that make use of network properties (i.e., how closely x is connected to y, what the shortest route is from a to b). These persons represent high money laundering risks. –> Up next: AML Compliance: New technologies to fight back against money laundering networks, Tags: aml, anti money laundering, big data, connected data, graph analytics, […] the relationships within your data from an obstacle into an asset. The result can also be visualized. What if indeed the risky entity wants to engage in money laundering? This requires opening a first tab with the person’s transactions and their recipients. This is a natural use case because a network topology looks like a graph. Another … Most graph databases focus on low level data: transactions, communications, and the like. 3 John Smiths from 3 different databases all share the same address and phone number. Graphs at Spark+AI Summit Europe 2019. TigerGraph delivers the power of a scalable graph database and analytics platform to everyone -- including non-technical users. These are individuals that own a client, or on behalf of whom, a transaction is made. Neo4j®, Neo Technology®, Cypher®, Neo4j® Bloom™ and Neo4j® Aura™ are registered trademarks Hope you learn as much about graph analytics as I have! TigerGraph invited developers of every skill level to develop tools and demonstrations for the TigerGraph connected data analytics platform. A lot of fraud use cases require identifying suspicious connections whereas graph analytics is designed to analyze complex connections from big data at scale. The dots represent actual people in a social network. The rapid transformation of computing of all kinds, but especially for analytics and prediction, put to rest the prevailing model of managing from scarcity, a lingering gestalt that has been around since computing was invented. Home; About. With all of these possibilities in mind, I hope you have enough hints to get started with a new type of analytics use cases that you can now tackle with Splunk. The topology defines what nodes are used in the media graph, and how they are connected within the media graph. Limor is a technical writer and editor at Agile SEO, a boutique digital marketing agency focused on technology and SaaS markets. E is a set of edges. Deposits are made in 4 bank accounts, moved to intermediary accounts, and then combined to a single bank account. Jaime Benson is indirectly connected to Guanghua Zheng (an individual listed on OFAC’s Counter Narcotics Trafficking Sanctions list). Graph Analytics and Knowledge Graphs Facilitate Scientific Research for COVID-19 State of the art in analytics and AI can help address some of the most pressing issues in scientific research. Graphs are powerful at representing complex interconnections, and graph data modeling is very effective and flexible when the number and depth of relationships increase exponentially. Cut the manual analyst research time. Disaster Detection System . This 2 part blog series on graph analytics pulls from a number of very useful sources, which I reference at the end. Azure Cosmos DB is Microsoft’s globally distributed database service. In this situation, the UBOs of Atech are Acme, Invest Inc. and John Smith. Graph Database Use Cases When Connected Data Matters Most Today’s most pressing data challenges center around connections, not just discrete data. Then you’d need to open the same sort of tab for each recipient. But graphs and graph databases provide relationship models. A graph database like Neo4j is a lot more than a data store. There is a query language for graphs, GraphQL, which is somewhat similar to SQL and easy to learn, provide the query builder understands the structure of the graph. How graph analytics can help: Graph analytics facilitates the dynamic exploration of relationships within a large dataset. Learn the fundamentals of graph databases and how connected data transforms business. Chances are though that it’s not the case. Unlimited scalability, granular security and operational agility. Skip to main content About menu. Fraud Detection and Analytics Enhancement Human eye captures fraud schemes within graph visualization blazingly fast! This looks like synthetic identities controlled by a single group or person. Amy Hodler, graph analytics and AI program manager at Neo4j, said early use cases involve improving the way data is ingested into the AI training tools in a process called feature engineering. How graph analytics can help: Graph analytics allows you to turn the playing history into flows of money across players. Anti-money laundering (AML) and graph analytics is a match made in heaven. Graph Databases will probably not replace your operational applications; they will likely complement your MDM and data governance applications. In this article we will provide a series of examples where graph analytics can be used to fight back against fraud. They let you apply pattern recognition, classification, statistical analysis, and machine learning to these models, which enables more efficient analysis at scale against massive amounts of data. Gradoop: Distributed Graph Analytics on Hadoop. Gartner describes graph as one of the most important data and analytics trends that will change your business and estimates that the graph market will grow 100% annually from 2019 to 2022. The problem: Financial institutions are tasked with identifying UBOs. ... fraud detection, anti-money laundering, customer intelligence, risk analytics, product and service recommendations, and machine learning. Financial Industry Use Cases for Graph Analytics Oracle May 28, 2020 Ryota Yamanaka and Melli Annamalai, Graph Product Management Gautam Pisharam, Oracle Solutions Engineer Hub Graph Database Use-Cases. … Use Case: Fraud Analytics A powerful application of Knowledge Graphs is the transformation of transactional data into a social/entity view. May 6, 2018 May 6, 2018 Oracle Community Oracle. Get much deeper. Four widely used types of graph analytics include path analysis, connectivity analysis, community analysis and centrality analysis: Path analysis: This type of analysis can be used to determine the shortest distance between two nodes in a graph, for example. Concepts of graph databases from a relational developer’s point of view. Graph Database Use Case: Credit Card Fraud. So, it makes sense to model it that way. The following use cases give a perspective into graph computation and further scope to implement other solutions using graphs. Here are some other use cases proposed by DataStax and others: Customer 360. The cases in which graph analytics can be beneficial in helping to detect money laundering efforts continues to grow. Sweden +46 171 480 113 The World’s Fastest and Most Scalable Graph Platform. All rights reserved. Detecting synthetic identities can help stop criminals before they commit financial crime. LEARN MORE Start in minutes, build in hours and deploy in days with the industry’s first and only distributed graph database-as-a-service. This type of analysis can be translated into a single graph query to automate the process. For this use case, you can use graph representation by creating a graph from transitions between entities as well as entities that share some information, including the email addresses, passwords, addresses, and more. Advanced analytics in graph allows a system to process a payment while understanding how a transaction is connected to different datasets. Azure Cosmos DB is the first globally distributed database service in the market today to offer comprehensive service level agreementsencompassing throughput, latency, availability, and consistency. Cecile Ronca has received a substantial amount of money from a group of 5 players. The problem: Criminals that want to defraud a bank or launder money typically try to cover their tracks. Linkurious SAS © 2013-2020. Next, we describe technical updates and advances in Graphs, as presented at relevant conferences during the course of 2019. Detecting accomplices becomes faster. Login or Join to gain access to the Neo4j portal. The goal of the GraphX project is to unify graph-parallel and data-parallel computation in one system with a single composable API. The problem: Online gambling offers an opportunity to transfer money discreetly. In this blog, I want to delve deeper by looking at special types of graphs called Directed Acyclic Graphs (DAGs) and their applications. Chances are that a lot of effort will go into hiding the relationships to escape enhanced due diligence. Not only do graph databases enable succinct data connectivity, they also provide users with a faster path to accurate data analytics. How graph analytics can help: The relationships between a client and risky entities can be as simple as a client and a politically exposed person sharing the same address. People usually associate this term with SalesForce, but it can be implemented as a graph database for anyone. Learn more Here are the top use cases for graph databases. The Top 5 Use Cases of Graph Databases Use Case #1: Fraud Detection By putting checks into place and associating them with the appropriate event triggers, such schemes can be uncovered before they are able to inflict significant damage. Going to a bank with a sports bag and depositing the money is not a viable option. This research provides technical professionals dealing with data and analytics an overview of graph database use cases and their architecture. G = (V,E). LEARN MORE; VIDEOS. Adding more people and more layers in the scheme will make it possible to launder more money and further conceal the situation. Money laundering efforts don’t stand a chance when financial institutions are equipped with graph analytics. Graphathon 2020 was a global challenge created to encourage creativity and showcase graph innovation. "A graph database allows you to add new relationships as you go along." To learn more, please […]. Facebook; LinkedIn; Twitter; Google Plus; Email; Comment ; According to Ernst and Young, $8.2 billion a year is lost to the marketing, advertising, and media industries through fraudulent impressions, infringed content, and malvertising. The bank will be obligated to file a Suspicious Activity Report (SAR) if the transaction is above $10,000. Graphs are powerful at representing complex interconnections, and graph data modeling is very effective and flexible when the number and depth of relationships increase exponentially. Who is a person connected to directly and indirectly via financial transactions? US: 1-855-636-4532 Live Video Analytics on IoT Edge enables you to manage media graphs via two concepts – "graph topology" and "graph instance". For example, researchers at the University of California, San Francisco, have developed Het.io , a tool that structures biomedical information to highlight connections. Graphs have become a powerful tool in the finance industry as a means of detecting fraud. How can an online gambling operation identify the groups within its community that are engaging in money laundering? Let us start with a simple graph class written in Python to start up our exploits with code. Maybe the John Smith that exists in the retail bank database has a different ID than the John Smith of the consumer credit database or of the John Smith in the company owners database that you bought, despite the fact that these 3 individuals are actually the same person. Money laundering efforts don’t stand a chance when financial institutions are equipped with graph analytics. Learn about Databricks solutions use cases from cybersecurity analytics to deep learning to just-in-time data warehousing. As such, graph analytics is good for certain use cases (but not for all use cases, relational database are still good on many other use cases): As you can see, the preceding diagram depicts a huge social network (though the preceding diagram might just be depicting a network of a few friends only). The problem: In an ideal world, each individual or company in your databases would be unique. Graph Databases - Analytical Use Cases What is a graph database? Azure Cosmos DB is a global distributed, multi-model database that is used in a wide r… handle complex and connected access control structures, Visualizing Fraud with Neo4j and GRANDstack, Updated GraphAcademy Course: Using a Machine Learning Workflow for Link Prediction, Understanding Graphs and Graph Data Science. Fraud detection is one of the most powerful use cases for graph databases right now, Panetta said. People usually associate this term with SalesForce, but it can be implemented as a graph database for anyone. In this use case, we’ll look more specifically at Case Correlation. By: Sherry Tiao. in Use case Anti-money laundering (AML) and graph analytics is a match made in heaven. UK: +44 20 3868 3223 Financial Industry Use Cases for Graph Analytics Oracle May 28, 2020 Ryota Yamanaka and Melli Annamalai, Graph Product Management Gautam Pisharam, Oracle Solutions Engineer Hub A critical thing to assess in this case is whether this single suspicious situation is isolated or not. of Neo4j, Inc. All other marks are owned by their respective companies. Deploy Neo4j on the cloud platform of your choice. How graph analytics can help: Graph analytics is perfect to detect such complex patterns even within billions of transactions. As criminals become more and more sophisticated in how they launder money, so too do the tools that help fight back against them. Fraud and anomalies. Knowledge Graph Construction Use Cases. Graph analytics finds patterns among the relationships between nodes. The last flavor of centrality that we will be exploring is known as the Eigen Vector Centrality. Well, th… Although graph theory has been around for centuries, graph databases began their rise to popularity relatively recently. These 3 individuals are all sharing common information. Oracle Graph Analytics Architecture Scalable and Persistent Storage Graph Storage Management Graph Analytics In-memory Analytic Engine Blueprints & SolrCloud / Lucene Property Graph Support on Apache HBase, Oracle NoSQL or Oracle 12.2 REST Web Service Python, Perl, PHP, Ruby, Javascript, … Java APIs A synthetic identity is a fake identity that mixes real or fake information (such as a real social security number or a fake name) that does not belong to a single real person. In this use case, we’ll look more specifically at Case Correlation. Analytics cookies. It then becomes possible to look within this graph of transactions for suspicious patterns such as a player who is always on the receiving end of money flows from players who never play with anyone else. Learn more. Explore and Learn Neo4j with the Neo4j Sandbox. Traditional approaches to fraud detection rely on simple checklists. Detecting such patterns is more complex than simply checking whether a series of transactions on an account match a certain threshold. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. Additional use cases for graph databases. The service is designed to allow customers to elastically (and independently) scale throughput and storage across any number of geographical regions. Some use cases and tools in data science conference. Machine Learning. using the neo4s app from splunkbase. In my previous blog, I announced the launch of Capgemini’s UK Graph Guild and introduced the notion of basic graphs and a number of simple use-cases. It gets tricky when the number of ownership layers increases. Examples and use cases include pricing flexibility, customer preference management, credit risk analysis, fraud protection, and discount targeting. graph analytics can be used to fight back against money laundering. Some use case examples of Graph tech in action. V is the set of vertices. Some industry-specific use cases will draw from multiple areas areas of graph use case taxonomy. Amy Hodler, graph analytics and AI program manager at Neo4j, said early use cases involve improving the way data is ingested into the AI training tools in a process called feature engineering. - 73) 10.1 Introduction She specializes in big data analytics. Then, a “next best action” is offered up; in the case of fraud, this may be “reject payment” while marking a user with a “zero” trust score. To learn more, feel free to contact us! Graph Databases, as a technology, should be used where appropriate to your analytics use cases. Another use-case of this metric is to detect and monitor possible bottlenecks or hot-spots in computer networks or flow networks. 9.5.1 Ability of Graph Analytics to Identify Shortest and Safest Routes to Drive Its Adoption in Route Optimization Application 9.6 Fraud Detection 9.6.1 Ability to Detect Real-Time Fraud Patterns to Drive the Adoption of Graph Analytics in Fraud Detection 9.7 Others . The Data Science and Analytics field has also used Graphs to model various structures and problems. In this article we will provide a series of examples where, Identification of Politically Exposed Persons and sanctions screening. AML Compliance: New technologies to fight back against money laundering networks, The technology behind the FinCEN files investigation, Anti-money laundering & graph analytics: a match made in heaven. Did you know that also Google’s original search ranking is based on a Graph algorithm called “Pagerank”? With this in mind, our editors have compiled this list of the most common graph database use cases you need to know. Your email address will not be published. The more matches you find the better the problem might be solved by a … If your business faces problems that fall into this space, the use of graph-oriented technology can significantly enhance your analytics team's efficiency. A now classic example of using graph analytics to identify networks of relationships is the International Consortium of Investigative Journalists (ICIJ) research on Panama Papers. Maybe even though our 3 John Smiths have different IDs they all share the same date of birth, the same address and the same phone number. The end result is that the money has been transferred to your accomplice. A graph topology enables you to define the blueprint of a graph, with parameters as placeholders for values. In this case, detecting such a situation is easy. You can then bet money on hands and lose on purpose. One approach consists in breaking down your transaction in order to bypass your bank’s control systems. If so, chances are that they are indeed the same person. Finally, if you have the requirement to push on with your graph, you will probably choose a graph store or graph database like Neo4j that you can connect back to Splunk, e.g. Unveil sophisticated fraud and criminal patterns much easier. The use cases for graph analytics are diverse: social networks, transportation routes, autonomous vehicles, cyber security, criminal networks, fraud detection, health research, epidemiology, and so forth. Terms | Privacy | Sitemap. Banks and healthcare companies have some of the most compelling use cases for graph analytics, including anti-money laundering (AML) and drug discovery. The answer is: because LinkedIn organizes its entire contact network of 660+ million users with a graph! Here are some other use cases proposed by DataStax and others: Customer 360. The Spark + AI Summit Europe 2019 showcased some use cases of Graph Analytics. We've looked at how graph analytics has progressed through the years, but in this installment, we examine some concrete use cases for graph technology in this department. Graph data stores can efficiently model, explore and query data with complex interrelationships across data silos, but there is a lot of hype around them. Journalism. Despite advances in anti-fraud technology, such as the use of embedded chips in cards, fraud can still occur in a number of ways. That money can then be accessed by the individual who directly controls Hooli Ltd and indirectly controls Globex Corp. Graph analytics applications exist in journalist, telecom, social networks, finance and operations. Maybe they’re the same person? Achieve significant savings. The problem: Sometimes a tip or a detection system may flag a client or a transaction as suspicious. The cases in which graph analytics can be beneficial in helping to detect money laundering efforts continues to grow. Graph Database Use Cases Fraud Detection Business events and customer data, such as new accounts, loan applications and credit card transactions can be modelled in a graph … What are its use cases? Compliance analysts can look into the overall ownership graph to identify where a problematic UBO sits, for example. A lot of anti-money laundering use cases require identifying suspicious connections whereas graph analytics is designed to analyze complex connections from big data at scale. But there are a growing number of applications outside the “social” realm. Most common use cases are listed below. Graph algorithms are the driving force behind the next generation of AI and machine learning, powering many industry use cases. How graph analytics can help: Graph analytics is perfect to detect such complex patterns across billions of entities and relationships. And, the Graph database is adopted for ever more use-cases and applications as organizations continue implementing the Graph technology. We use analytics cookies to understand how you use our websites so we can make them better, e.g. One approach is to create fake identities to commit their wrongdoings. The information you provide will be used in accordance with the terms of our privacy policy. Sit at a poker table where your accomplice is also present. France: +33 (0) 1 73 23 56 07. Graph data stores can efficiently model, explore and query data with complex interrelationships across data silos, but there is a lot of hype around them. She has over 10 years’ experience writing technical articles and documentation for various audiences, including technical on-site content, software documentation, and dev guides. Our powerful graphical user interface integrates all the phases of graph analytics into one easy-to-use application. It’s possible to explore and visualize who and what a client is connected to across information as diverse as an address, a phone number, an email, transactions, etc. For this use case, you can use graph representation by creating a graph from transitions between entities as well as entities that share some information, … This is particularly apparent in cases of performance loss due to branch divergence, when a subset of threads follows a different code path than the others as a result of a conditional instruction. How graph analytics can help: Graph analytics can detect common links across different entities to help identify potential duplicates.

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