This document discusses how to build next generation fraud solutions using Neo4j graph database technology. It begins by outlining the challenges of fraud and how traditional relational databases are inadequate for detecting complex fraud patterns. It then describes how graph databases like Neo4j can provide a 360-degree view of connected customer and transaction data to enable real-time fraud detection. Examples of fraud use cases where Neo4j has been successfully applied are also provided, followed by an overview of how to architect a fraud solution leveraging Neo4j's graph capabilities.
Detecting Opportunities and Threats with Complex Event Processing: Case St...Tim Bass
Detecting Opportunities and Threats with Complex Event Processing: Case Studies in Predictive Customer Interaction Management and Fraud Detection, February 27, 2007 FINAL DRAFT 2, 8th Annual Japan\'s International Banking & Securities System Forum, Tim Bass, CISSP, Principal Global Architect, Director
Build Intelligent Fraud Prevention with Machine Learning and GraphsNeo4j
See how financial services, banking and retail are using graph-enhanced machine learning to thwart fraud. Fraudsters are becoming increasingly sophisticated, organized and adaptive; traditional, rule-based solutions are not broad or nimble enough to deal with this reality. This session will cover several demonstrations and real-world technical examples including preventing credit card fraud, identifying money laundering and reducing false positives.
CGI's Steve Starace, SVP & BU Leader, U.S. Northeast explains how CGI’s solutions and services are addressing clients’ top priorities in the banking industry.
Detecting Opportunities and Threats with Complex Event Processing: Case St...Tim Bass
Detecting Opportunities and Threats with Complex Event Processing: Case Studies in Predictive Customer Interaction Management and Fraud Detection, February 27, 2007 FINAL DRAFT 2, 8th Annual Japan\'s International Banking & Securities System Forum, Tim Bass, CISSP, Principal Global Architect, Director
Build Intelligent Fraud Prevention with Machine Learning and GraphsNeo4j
See how financial services, banking and retail are using graph-enhanced machine learning to thwart fraud. Fraudsters are becoming increasingly sophisticated, organized and adaptive; traditional, rule-based solutions are not broad or nimble enough to deal with this reality. This session will cover several demonstrations and real-world technical examples including preventing credit card fraud, identifying money laundering and reducing false positives.
CGI's Steve Starace, SVP & BU Leader, U.S. Northeast explains how CGI’s solutions and services are addressing clients’ top priorities in the banking industry.
Credit Card Fraud Detection Using ML In DatabricksDatabricks
In the Credit Card Companies, illegitimate credit card usage is a serious problem which results in a need to accurately detect fraudulent transactions vs non-fraudulent transactions. All organizations can be hugely impacted by fraud and fraudulent activities, especially those in financial services. The threat can originate from internal or external, but the effects can be devastating – including loss of consumer confidence, incarceration for those involved, even up to downfall of a corporation. Despite regular fraud prevention measures, these are constantly being put to the test in an attempt to beat the system.
Fraud detection is a task of predicting whether a card has been used by the cardholder. One of the methods to recognize fraud card usage is to leverage Machine Learning (ML) models. In order to more dynamically detect fraudulent transactions, one can train ML models on a set of dataset including credit card transaction information as well as card and demographic information of the owner of the account. This will be our goal of the project while leveraging Databricks.
Graph Gurus Episode 34: Graph Databases are Changing the Fraud Detection and ...TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-34
During this webinar we:
-Examine how graph analytics can lower the total cost of fraud;
-Describe how graph analytics can improve credit card fraud detection;
-Explore the application of graph analytics to an anti-money laundering use case.
Identity Fraud Protection Using Big Data Analytics - StampedeCon 2015StampedeCon
Presented at StampedeCon 2015: As technology evolves, consumers are able to do more and more things in a remote setting—banking, shopping, communication, you name it. The more enabled we are, the more fraud is possible. As individuals use their identities to apply for goods and services – credit, loans, wireless phones, mortgages, etc. – certain patterns emerge. ID Analytics, a LifeLock company, quantitatively evaluates billions of data points, in real time, to understand identity risk. The algorithms behind our analysis come from the state-of-the-art machine learning community.
In this talk, we’ll describe the modes of identity fraud with examples of some fraud rings that we have observed along with details of the data structures and big data algorithms we use to catch identity fraud.
The use of data science and machine learning in the investment industry is increasing. Financial firms are using artificial intelligence (AI) and machine learning to augment traditional investment decision making.
In this workshop, we aim to bring clarity on how AI and machine learning are revolutionizing financial services. We will introduce key concepts and, through examples and case studies, will illustrate the role of machine learning, data science techniques, and AI in the investment industry.
Agenda:
In Part 1, we will discuss key trends in AI and machine learning in the financial services industry, including the key use cases, challenges, and best practices.
In Part 2, we will illustrate two case studies where AI and machine learning techniques are applied in financial services.
Case studies:
Sentiment Analysis Using Natural Language Processing in Finance
In this case study, we will demonstrate the use of natural language processing techniques to analyze EDGAR call earnings transcripts that could be used to generate sentiment analysis scores using the Amazon Comprehend, IBM Watson, Google, and Azure APIs (application programming interfaces). We will illustrate how these scores can be used to augment traditional quantitative research and for trading decisions.
Credit Risk Decision Making Using Lending Club Data
In this case study, we will use the Lending Club data set to build a credit risk model using
machine learning techniques.
Analytics in banking preview deck - june 2013Everest Group
This report provides a comprehensive understanding of the analytics services industry with focus on banking domain. Analytics adoption in the banking industry is covered in depth, exploring various aspects such as market size, key drivers, recent analytics initiatives, and challenges. The report also analyses the trends in analytics deals for various banking subverticals (cards, retail, commercial, and lending) and evaluates analytics capabilities of 20+ service providers in the banking space
How Big Data and Predictive Analytics are revolutionizing AML and Financial C...DataWorks Summit
Banks, Payment Providers and capital markets firms are under intense regulatory mandate to process huge amounts of transaction-related data from both traditional and non-traditional sources. Compliance teams need to constantly analyze data-in-motion (wires, fund transfers, banking transactions) and data-at-rest (years worth of historical data) for actionable intelligence required for Suspicious Activity Reports—to discover illegal activity and provide detailed reporting to authorities. Annual estimates of global money laundering flows ranging anywhere from $ 1 trillion to 2 trillion – almost 5% of global GDP. Almost all of this is laundered via Retail & Merchant Banks, Payment Networks, Securities & Futures firms, Casino Services & Clubs etc – which explains why annual AML related fines on Banking organizations run into the billions and are increasing every year. However, the number of SARs (Suspicious Activity Reports) filed by banking institutions are much higher as a category as compared to the numbers filed by these other businesses. In this presentation we will discuss the business imperatives, value drivers and the woeful inadequacy of current technology architectures and approaches in tackling AML. We will then pivot to a deepdive around Big Data and Predictive Analytics in how they can ease and solve these vexing challenges that Banking executives are grappling with globally.
Fraud Analytics with Machine Learning and Big Data Engineering for TelecomSudarson Roy Pratihar
Presentation of a successful project executed on telecom fraud analytics @ 3rd International conference for businees analytics and intelligence, Indian Institute of Management Bangalore
Learn how financial institutions are betting on the Big Data and Artificial Intelligence through APIs that help banks to define products, segmenting customers and detect possible fraud. Throughout this ebook we offer a review of the APIs bank data aggregation. More information in http://bbva.info/2t1NEv7
The first workshop earlier in the week at New York University, exploring #Analytics on a #DLT #Blockchain platform and the intersection of DLT and #AI.
IBM Solutions Connect 2013 - Getting started with Big DataIBM Software India
You've heard of Big Data for sure. But what are the implications of this for your organisation? Can your organisation leverage Big Data too? If you decide to go ahead with your Big Data implementation where do you start? If these questions sound familiar to you then you've stumbled upon the right presentation. Go through the presentation to:
a. Learn more on Big data
b. How Big data can help you outperform in your marketplace.
c. How to proactively manage security and risk
d. How to create IT agility to underpin the business
Also, learn about IBM's superior Big Data technologies and how they are helping today's organisations take smarter decisions and actions.
Detecting eCommerce Fraud with Neo4j and LinkuriousNeo4j
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.
Credit Card Fraud Detection Using ML In DatabricksDatabricks
In the Credit Card Companies, illegitimate credit card usage is a serious problem which results in a need to accurately detect fraudulent transactions vs non-fraudulent transactions. All organizations can be hugely impacted by fraud and fraudulent activities, especially those in financial services. The threat can originate from internal or external, but the effects can be devastating – including loss of consumer confidence, incarceration for those involved, even up to downfall of a corporation. Despite regular fraud prevention measures, these are constantly being put to the test in an attempt to beat the system.
Fraud detection is a task of predicting whether a card has been used by the cardholder. One of the methods to recognize fraud card usage is to leverage Machine Learning (ML) models. In order to more dynamically detect fraudulent transactions, one can train ML models on a set of dataset including credit card transaction information as well as card and demographic information of the owner of the account. This will be our goal of the project while leveraging Databricks.
Graph Gurus Episode 34: Graph Databases are Changing the Fraud Detection and ...TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-34
During this webinar we:
-Examine how graph analytics can lower the total cost of fraud;
-Describe how graph analytics can improve credit card fraud detection;
-Explore the application of graph analytics to an anti-money laundering use case.
Identity Fraud Protection Using Big Data Analytics - StampedeCon 2015StampedeCon
Presented at StampedeCon 2015: As technology evolves, consumers are able to do more and more things in a remote setting—banking, shopping, communication, you name it. The more enabled we are, the more fraud is possible. As individuals use their identities to apply for goods and services – credit, loans, wireless phones, mortgages, etc. – certain patterns emerge. ID Analytics, a LifeLock company, quantitatively evaluates billions of data points, in real time, to understand identity risk. The algorithms behind our analysis come from the state-of-the-art machine learning community.
In this talk, we’ll describe the modes of identity fraud with examples of some fraud rings that we have observed along with details of the data structures and big data algorithms we use to catch identity fraud.
The use of data science and machine learning in the investment industry is increasing. Financial firms are using artificial intelligence (AI) and machine learning to augment traditional investment decision making.
In this workshop, we aim to bring clarity on how AI and machine learning are revolutionizing financial services. We will introduce key concepts and, through examples and case studies, will illustrate the role of machine learning, data science techniques, and AI in the investment industry.
Agenda:
In Part 1, we will discuss key trends in AI and machine learning in the financial services industry, including the key use cases, challenges, and best practices.
In Part 2, we will illustrate two case studies where AI and machine learning techniques are applied in financial services.
Case studies:
Sentiment Analysis Using Natural Language Processing in Finance
In this case study, we will demonstrate the use of natural language processing techniques to analyze EDGAR call earnings transcripts that could be used to generate sentiment analysis scores using the Amazon Comprehend, IBM Watson, Google, and Azure APIs (application programming interfaces). We will illustrate how these scores can be used to augment traditional quantitative research and for trading decisions.
Credit Risk Decision Making Using Lending Club Data
In this case study, we will use the Lending Club data set to build a credit risk model using
machine learning techniques.
Analytics in banking preview deck - june 2013Everest Group
This report provides a comprehensive understanding of the analytics services industry with focus on banking domain. Analytics adoption in the banking industry is covered in depth, exploring various aspects such as market size, key drivers, recent analytics initiatives, and challenges. The report also analyses the trends in analytics deals for various banking subverticals (cards, retail, commercial, and lending) and evaluates analytics capabilities of 20+ service providers in the banking space
How Big Data and Predictive Analytics are revolutionizing AML and Financial C...DataWorks Summit
Banks, Payment Providers and capital markets firms are under intense regulatory mandate to process huge amounts of transaction-related data from both traditional and non-traditional sources. Compliance teams need to constantly analyze data-in-motion (wires, fund transfers, banking transactions) and data-at-rest (years worth of historical data) for actionable intelligence required for Suspicious Activity Reports—to discover illegal activity and provide detailed reporting to authorities. Annual estimates of global money laundering flows ranging anywhere from $ 1 trillion to 2 trillion – almost 5% of global GDP. Almost all of this is laundered via Retail & Merchant Banks, Payment Networks, Securities & Futures firms, Casino Services & Clubs etc – which explains why annual AML related fines on Banking organizations run into the billions and are increasing every year. However, the number of SARs (Suspicious Activity Reports) filed by banking institutions are much higher as a category as compared to the numbers filed by these other businesses. In this presentation we will discuss the business imperatives, value drivers and the woeful inadequacy of current technology architectures and approaches in tackling AML. We will then pivot to a deepdive around Big Data and Predictive Analytics in how they can ease and solve these vexing challenges that Banking executives are grappling with globally.
Fraud Analytics with Machine Learning and Big Data Engineering for TelecomSudarson Roy Pratihar
Presentation of a successful project executed on telecom fraud analytics @ 3rd International conference for businees analytics and intelligence, Indian Institute of Management Bangalore
Learn how financial institutions are betting on the Big Data and Artificial Intelligence through APIs that help banks to define products, segmenting customers and detect possible fraud. Throughout this ebook we offer a review of the APIs bank data aggregation. More information in http://bbva.info/2t1NEv7
The first workshop earlier in the week at New York University, exploring #Analytics on a #DLT #Blockchain platform and the intersection of DLT and #AI.
IBM Solutions Connect 2013 - Getting started with Big DataIBM Software India
You've heard of Big Data for sure. But what are the implications of this for your organisation? Can your organisation leverage Big Data too? If you decide to go ahead with your Big Data implementation where do you start? If these questions sound familiar to you then you've stumbled upon the right presentation. Go through the presentation to:
a. Learn more on Big data
b. How Big data can help you outperform in your marketplace.
c. How to proactively manage security and risk
d. How to create IT agility to underpin the business
Also, learn about IBM's superior Big Data technologies and how they are helping today's organisations take smarter decisions and actions.
Detecting eCommerce Fraud with Neo4j and LinkuriousNeo4j
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.
AI, ML and Graph Algorithms: Real Life Use Cases with Neo4jIvan Zoratti
I gave this presentation at DataOps 19 in Barcelona.
You will find information about Neo4j and how to use it with Graph Algorithms for Machine Learning and Artificial Intelligence.
Big Data kennen sehr viele IT-Experten, wenigstens haben Sie eine Vorstellung davon. In der Praxis arbeiten damit in Deutschland derzeit nur wenige. Dabei bringt Big Data ein ganz neues Momentum in moderne Softwarelösungen und ist im Kontext der Mobil-, Cloud- und Social-Veränderungen nicht wegzudenken. Big Data macht Software intelligent und damit auf eine ganz neue Art für die Benutzer erlebbar. Mit Big Data entstehen neue Softwarearchitekturen, weil Informationen völlig anders verarbeitet werden - nämlich schneller, differenzierter und oft mit dem Ziel, Schlüsse zu ziehen und Vorhersagen zu treffen.
In diesem Vortrag wird erläutert, wie moderne Softwarearchitekturen gestaltet werden, sodass Sie Big Data Paradigmen erfolgreich umsetzen und welche Vorteile sich für die zunehmend mobilen Softwarelösungen ergeben. Wir werfen zudem einen Blick auf die Potentiale und Optionen in Branchen wie Banken, Versicherung oder Handel.
To view recording of this webinar please use below URL:
http://wso2.com/library/webinars/2016/06/analytics-in-your-enterprise/
Big data spans many fields and brings together technologies like distributed systems, machine learning, statistics and Internet of Things (IoT). It has now become a multi-billion dollar industry with use cases ranging from targeted advertising and fraud detection to product recommendations and market surveys.
Some use cases such as urban planning can be slower (done in batch mode), while others such as the stock market needs results in milliseconds (done is a streaming fashion). Different technologies are used for each case; MapReduce for batch analytics, complex event processing for real-time analytics and machine learning for predictive analytics. Furthermore, the type of analysis ranges from basic statistics to complicated prediction models.
This webinar will discuss the big data landscape including
Concepts, use cases and technologies
Capabilities and applications of the WSO2 analytics platform
WSO2 Data Analytics Server
WSO2 Complex Event Processor
WSO2 Machine Learner
Big Data Monetisation
PSD were pleased to host a breakfast at the Royal Horseguards Hotel discussing the subject of what companies can do with their data to monetise it and bring the debate to the CEO's office.
Leading the discussion, and presenting his portfolio of work in this area was Mike Fishwick.
Mike has recently led the Business Insights programme at Telefonica Digital, and has an almost unique viewpoint on the application of data science in this area.
Attending were technology leaders from a broad range of sectors all of whom are investigating what they do with the ever increasing torrent of data that they are managing.
For the latest IT & Business Change jobs & salary survey information, go to:
http://www.psdgroup.com/information_technology.aspx
Chris Eldridge - MD
Data is both our most valuable asset and our biggest ongoing challenge. As data grows in volume, variety and complexity, across applications, clouds and siloed systems, traditional ways of working with data no longer work.
Unlike traditional databases, which arrange data in rows, columns and tables, Neo4j has a flexible structure defined by stored relationships between data records.
We'll discuss the primary use cases for graph databases
Explore the properties of Neo4j that make those use cases possible
Look into the visualisation of graphs
Introduce how to write queries.
Webinar, 23 July 2020
Speakers: David Menninger, SVP and Research Director, Ventana Research + Joanna Schloss, Analytics, Data and Information Management Subject Matter Expert, Confluent
Can your organization react to customer events as they occur?
Can your organization detect anomalies before they cause problems?
Can your organization process streaming data in real time?
Real time and event-driven architectures are emerging as key components in developing streaming applications. Nearly half of organizations consider it essential to process event data within seconds of its occurrence. Yet less than one third are satisfied with their ability to do so today. In this webinar featuring Dave Menninger of Ventana Research, learn from the firm’s benchmark research about what streaming data is and why it is important. Joanna Schloss also joins to discuss how event-streaming platforms deliver real time actionability on data as it arrives into the business. Join us to hear how other organizations are managing streaming data and how you can adopt and deploy real time processing capabilities.
In this webinar you will:
-Get valuable market research data about how other organizations are managing streaming data
-Learn how real time processing is a key component of a digital transformation strategy
-Hear real world use cases of streaming data in action
-Review architectural approaches for adding real time, streaming data capabilities to your applications
Watch the recording: https://videos.confluent.io/watch/AoXiYayC1s23awqJBcQvPZ?
EVOLVING PATTERNS IN BIG DATA - NEIL AVERYBig Data Week
Neil has a long history in large scale, distributed computing, ranging from architecting large scale risk-analytic systems, running startups, to data-heavy weather analysis using data-grids. He is a certified Datastax Solution Architect with Cassandra, Spark and Solr and was previously a Principle Architect at Thoughtworks where he lead several large scale projects in Retail, Logistic and other interesting fields.
Operationalizing Data Science: The Right Architecture and ToolsVMware Tanzu
In part one of this two-part series, you learned some of the common reasons enterprises struggle to turn insights into actions as well as a strategy for overcoming these challenges to successfully operationalize data science. In part two, it’s time to fill in the architectural and technological details of that strategy.
Pivotal Data Scientist Megha Agarwal will share the key ingredients to successfully put data science models in production and use them to drive actions in real-time. In this webinar, you will learn:
- Adopting extreme programming practices for data science
- Importance of working in a balanced team
- How to put and maintain machine learning models in production
- End-to-end pipeline design
Presenter: Megha Agarwal, Data Scientist
Atelier - Architecture d’applications de Graphes - GraphSummit ParisNeo4j
Atelier - Architecture d’applications de Graphes
Participez à cet atelier pratique animé par des experts de Neo4j qui vous guideront pour découvrir l’intelligence contextuelle. En utilisant un jeu de données réel, nous construirons étape par étape une solution de graphes ; de la construction du modèle de données de graphes à l’exécution de requêtes et à la visualisation des données. L’approche sera applicable à de multiples cas d’usages et industries.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...Neo4j
Romain CAMPOURCY – Architecte Solution, Sopra Steria
Patrick MEYER – Architecte IA Groupe, Sopra Steria
La Génération de Récupération Augmentée (RAG) permet la réponse à des questions d’utilisateur sur un domaine métier à l’aide de grands modèles de langage. Cette technique fonctionne correctement lorsque la documentation est simple mais trouve des limitations dès que les sources sont complexes. Au travers d’un projet que nous avons réalisé, nous vous présenterons l’approche GraphRAG, une nouvelle approche qui utilise une base Neo4j générée pour améliorer la compréhension des documents et la synthèse d’informations. Cette méthode surpasse l’approche RAG en fournissant des réponses plus holistiques et précises.
ADEO - Knowledge Graph pour le e-commerce, entre challenges et opportunités ...Neo4j
Charles Gouwy, Business Product Leader, Adeo Services (Groupe Leroy Merlin)
Alors que leur Knowledge Graph est déjà intégré sur l’ensemble des expériences d’achat de leur plateforme e-commerce depuis plus de 3 ans, nous verrons quelles sont les nouvelles opportunités et challenges qui s’ouvrent encore à eux grâce à leur utilisation d’une base de donnée de graphes et l’émergence de l’IA.
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
GraphAware - Transforming policing with graph-based intelligence analysisNeo4j
Petr Matuska, Sales & Sales Engineering Lead, GraphAware
Western Australia Police Force’s adoption of Neo4j and the GraphAware Hume graph analytics platform marks a significant advancement in data-driven policing. Facing the challenges of growing volumes of valuable data scattered in disconnected silos, the organisation successfully implemented Neo4j database and Hume, consolidating data from various sources into a dynamic knowledge graph. The result was a connected view of intelligence, making it easier for analysts to solve crime faster. The partnership between Neo4j and GraphAware in this project demonstrates the transformative impact of graph technology on law enforcement’s ability to leverage growing volumes of valuable data to prevent crime and protect communities.
GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product UpdatesNeo4j
David Pond, Lead Product Manager, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Shirley Bacso, Data Architect, Ingka Digital
“Linked Metadata by Design” represents the integration of the outcomes from human collaboration, starting from the design phase of data product development. This knowledge is captured in the Data Knowledge Graph. It not only enables data products to be robust and compliant but also well-understood and effectively utilized.
Your enemies use GenAI too - staying ahead of fraud with Neo4jNeo4j
Delivered by Michael Down at Gartner Data & Analytics Summit London 2024 - Your enemies use GenAI too: Staying ahead of fraud with Neo4j.
Fraudsters exploit the latest technologies like generative AI to stay undetected. Static applications can’t adapt quickly enough. Learn why you should build flexible fraud detection apps on Neo4j’s native graph database combined with advanced data science algorithms. Uncover complex fraud patterns in real-time and shut down schemes before they cause damage.
BT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptxNeo4j
Delivered by Sreenath Gopalakrishna, Director of Software Engineering at BT, and Dr Jim Webber, Chief Scientist at Neo4j, at Gartner Data & Analytics Summit London 2024 this presentation examines how knowledge graphs and GenAI combine in real-world solutions.
BT Group has used the Neo4j Graph Database to enable impressive digital transformation programs over the last 6 years. By re-imagining their operational support systems to adopt self-serve and data lead principles they have substantially reduced the number of applications and complexity of their operations. The result has been a substantial reduction in risk and costs while improving time to value, innovation, and process automation. Future innovation plans include the exploration of uses of EKG + Generative AI.
Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit MilanNeo4j
Look beyond the hype and unlock practical techniques to responsibly activate intelligence across your organization’s data with GenAI. Explore how to use knowledge graphs to increase accuracy, transparency, and explainability within generative AI systems. You’ll depart with hands-on experience combining relationships and LLMs for increased domain-specific context and enhanced reasoning.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
4. 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. 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. Neo4j based Solutions
Neo4j Graph Based Solutions
- Neo4j DB / Platform
- Data Integration Platform
- Blueprint Datamodel
- Blueprint Architecture
- Domain know how
- Professional Services
7. Evolution using Neo4j
Neo4j enables Graph Based
Solutions with a need for:
- Agility
- Intuitiveness
- High Performance to support
connected data scenarios
- Scalable on traversing through
connected data
8. 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
10. 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
24. 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
25. 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
27. Revolving Debt
Number of Accounts
Normal behavior
Fraud Detection With Connected Analysis
Fraudulent pattern
28. 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.
29. 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
30. The Power of Cypher
Fraud Ring:
MATCH ring = (suspect:AccountHolder)-[*]->(contactInformation)<-[*..5]-(:AccountHolder)-[*]->(suspect)
RETURN ring
31. 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
33. 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
• ….
34. 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
37. 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
38. 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
39. 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
41. 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
42. 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
44. 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)
45. How to build Next Generation Solutions
with Neo4j
Stefan Kolmar, VP Field Engineering
May 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
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
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
Of course, there are many different types of fraud — and all with their own sets of complexity.
It can be Credit Card (which we will adress a little later)
The merchants themselves can be fraudulent
Fraud rings are a huge problem, and very hard to detect and stop with traditional systems.
Insurance fraud is of course very wide spread, as well as ecommerce fraud
But perhaps the most important fraud, is the fraud we don’t know about yet…
The challenge with fraud — especially from a data-perspective, is that it is so many things at the same time. First, it’s constantly evolving. It can appear both simple and complex at the same time. Sometimes a scheme involves few or many players, it appears both digitized and in analog forms, and there’s always that sensation that fraud is always “one step ahead”.
So let’s talk briefly about Fraud from a data-modeling perspective.
Because of the complex and varied nature of Fraud, all the detection efforts we have in place stores enormous amounts of data — For example — you think about it it terms of storing every transaction being made over a period of time. For example.
And finding clear anomalies, is one, sort of traditional way to go about detecting fraud. — A credit card couldn’t be in two different locations at the same time. For example
These patterns can occur in many different ways and complexity — however, the job of the fraud prevention-team is basically to react to patterns, by first 1) detecting them, and then 2) respond — and doing all this as fast as possible.
The key to achieve this is very much a question of the underlying technology.
If you store your data like this, as you would in a relational database store — you will probably have pretty good success with your discrete analysis — finding the anomalies we talked about earlier.
However, if you’re a looking to detect and respond to patterns, this structure wont be anywhere near sufficient. Instead what you need to do is re-imagine your data in the way it’s connected.
…which of course is data modelled as a graph. And this is how data i stored and queried in a graph database like Neo4j
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
Who can help –> additional help? Move to conclusions
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