Graph Gurus Episode 37: Modeling for Kaggle COVID-19 DatasetTigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-37
In this Graph Gurus Episode, we:
-Learn how to process text and extract entities (words and phrases) as well as classes linking the entities using SciSpacy, a Natural Language Processing (NLP) tool.
-Import the output of NLP and semantically link it in TigerGraph
-Run advanced analytics queries with TigerGraph to analyze the relationships and deliver insights
Graph Gurus Episode 37: Modeling for Kaggle COVID-19 DatasetTigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-37
In this Graph Gurus Episode, we:
-Learn how to process text and extract entities (words and phrases) as well as classes linking the entities using SciSpacy, a Natural Language Processing (NLP) tool.
-Import the output of NLP and semantically link it in TigerGraph
-Run advanced analytics queries with TigerGraph to analyze the relationships and deliver insights
Graph Gurus Episode 35: No Code Graph Analytics to Get Insights from Petabyte...TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-35
By attending this webinar you will:
-Learn how to use TigerGraph’s no-code capabilities;
-Understand how TigerGraph is built for scale and performance;
-Get a deep dive into TigerGraph 3.0 feature enhancements.
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.
Using Graph Algorithms for Advanced Analytics - Part 2 CentralityTigerGraph
What does finding the best location for a warehouse/office/retail store have in common with finding the most influential person in a referral network? Answer: they are both Centrality problems and can be solved with graph algorithms.
Increasing Revenue and Loyalty with Real-Time Product RecommendationTigerGraph
Learn how TigerGraph's real-time deep link analytics powered by a highly scalable graph database, combined with machine learning, is allowing eCommerce companies to:
-Identify what a customer is looking for at a particular business moment
-Recommend related items that will support customer needs and increase value of the final order
-Recommend other items that align with the customer preferences, likes and dislikes to increase click-through rate and order value
Graph Gurus 24: How to Build Innovative Applications with TigerGraph CloudTigerGraph
This Graph Gurus episode walks you through the development of a simple application based on the TigerGraph Cloud Customer 360 Starter Kit. Specifically, we will:
-Share the use case for the Customer 360 Starter Kit.
-Walk you through a step-by-step tutorial based on the sample dataset, the prepackaged GSQL queries, the GraphStudio UI flow from the Starter Kit, and the integration process with a simple front-end application.
-Demonstrate the end-to-end full stack application development based on TigerGraph Cloud.
Commercial Analytics at Scale in Pharma: From Hackathon to MVP with Azure Dat...Databricks
GSK are a science-led global healthcare company with a special purpose: to help people do more, feel better, live longer.
We have three global businesses that discover, develop and manufacture innovative pharmaceutical medicines, vaccines and consumer healthcare products.
In this talk i will share our experience in the Pharmaceutical business delivering commercial analytics going from hackathon to MVP.
From the initial ideas and business discussions through delivery of a hackathon as an accelerator, on to building an MVP. Using the Azure cloud platform and Databricks to rapidly ingest data and prototype.
I will touch on the challenges, opportunities and learning points of the process we went through to deliver commercial analytics at scale in Pharma.
When it comes to dealing with large, complex, and disparate data sets, traditional database technologies are unable to keep pace with the rich analytics necessary to power today’s data-driven applications. Graph analytics databases are becoming the underlying infrastructure for AI and machine learning. These databases allow users to ask complex questions across complex data, which is not always practical or even possible at scale using other approaches. They also enable faster insights against massive data sets when combined with pattern recognition, statistical analysis, and AI/ machine learning. And in the case of standards-based graph databases, they connect with popular visualization tools like Graphileon, allowing users to easily explore their data stores and quickly build compelling graph-based applications.
In this webinar, data analytics gurus Sathish Thyagarajan and Steve Sarsfield introduce AnzoGraph™, our graph OLAP database, demonstrate the different types of analyses you can perform with it and how it complements Neo4j, AWS Neptune and other OLTP systems. Finally, they’ll show how you can get it up and running on your laptop in about 5 minutes.
Risk Analytics Using Knowledge Graphs / FIBO with Deep LearningCambridge Semantics
This EDM Council webinar, sponsored by Cambridge Semantics Inc. and featuring FI Consulting, explores the challenges common to a risk analytics pipeline, application of graph analytics to mortgage loan data and use cases in adjacent areas including customer service, collections, fraud and AML.
At Data-centric Architecture Forum 2020 Thomas Cook, our Sales Director of AnzoGraph DB, gave his presentation "Knowledge Graph for Machine Learning and Data Science". These are his slides.
Wizard Driven AI Anomaly Detection with Databricks in AzureDatabricks
Fraud is prevalent in every industry, and growing at an increasing rate, as the volume of transactions increases with automation. The National Healthcare Anti-Fraud Association estimates $350B of fraudulent spending. Forbes estimates $25B spending by US banks on anti-money laundering compliance. At the same time as fraud and anomaly detection use cases are booming, the skills gap of expert data scientists available to perform fraud detection is widening.
The Kavi Global team will present a cloud native, wizard-driven AI anomaly detection solution, enabling Citizen Data Scientists to easily create anomaly detection models to automatically flag Collective, Contextual, and Point anomalies, at the transaction level, as well as collusion between actors. Unsupervised methods (Distribution, Clustering, Association, Sequencing, Historical Occurrence, Custom Rules) and supervised (Random Forest, Neural Network) models are executed in Apache Spark on Databricks.
An innovative aggregation framework converts probabilistic fraud scores and their probabilities into a meaningful and actionable prioritized list of suspicious (a statistical outlier) and potentially fraudulent transaction to be investigated from a business point of view. The AI Anomaly Detection models improve over time using Human-in-the-Loop feedback methods to label data for supervised modeling.
Finally, The Kavi team overviews the Anomaly Lifecycle: from statistical outlier to validated business fraud for reclaim and business process changes to long term prevention strategies using proactive audits upstream at the time of estimate to prevent revenue leakage. Two client success stories will be presented acros Pharmaceutical Rx and Transportation industries.
How Graphs Continue to Revolutionize The Prevention of Financial Crime & Frau...Connected Data World
Financial crime prevention is something that affects everyone in one way or another. From the Deutsche Banks of the world to small and medium online merchants, regulations for anti-money laundering, know your customer, and customer due diligence apply.
Failing to comply with such regulations can bring on substantial fines. Even more importantly, it can hurt the bottom line and reputation of businesses, having far-reaching side effects. Complying with such regulations, and actively cracking down on financial crime, however, is not easy.
Cross-referencing interconnected data across various datasets, and trying to apply detection rules and to discover patterns in the data is complicated. It takes expertise, effort, and the right technology to be able to do this efficiently.
A natural and efficient way of looking for patterns and applying rules in troves of interconnected data is to model and view that data as a graph. By modeling data as a graph, and applying graph-based algorithms such as PageRank or Centrality, traversing paths, discovering connections and getting insights becomes possible.
Graphs and graph databases are the fastest-growing area of data management technology for a number of reasons. One of the reasons is because they are a perfect match for use cases involving interconnected data.
Queries that would be very complicated to express and very slow to execute using relational databases or other NoSQL database technology, are feasible using graph databases. With the rise in complexity of modern financial markets, financial crimes require going 4 to 11 levels deep into the account – payment graph: this requires a different solution than either relational or NoSQL databases.
How are organizations such as Alibaba, OpenCorporates, and Visa using graph database technology to not just stay on top of regulation, but be one step ahead in the race against financial crime?
Is it possible to do this in real time?
What do graph query languages have to do with this?
Graph Gurus Episode 35: No Code Graph Analytics to Get Insights from Petabyte...TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-35
By attending this webinar you will:
-Learn how to use TigerGraph’s no-code capabilities;
-Understand how TigerGraph is built for scale and performance;
-Get a deep dive into TigerGraph 3.0 feature enhancements.
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.
Using Graph Algorithms for Advanced Analytics - Part 2 CentralityTigerGraph
What does finding the best location for a warehouse/office/retail store have in common with finding the most influential person in a referral network? Answer: they are both Centrality problems and can be solved with graph algorithms.
Increasing Revenue and Loyalty with Real-Time Product RecommendationTigerGraph
Learn how TigerGraph's real-time deep link analytics powered by a highly scalable graph database, combined with machine learning, is allowing eCommerce companies to:
-Identify what a customer is looking for at a particular business moment
-Recommend related items that will support customer needs and increase value of the final order
-Recommend other items that align with the customer preferences, likes and dislikes to increase click-through rate and order value
Graph Gurus 24: How to Build Innovative Applications with TigerGraph CloudTigerGraph
This Graph Gurus episode walks you through the development of a simple application based on the TigerGraph Cloud Customer 360 Starter Kit. Specifically, we will:
-Share the use case for the Customer 360 Starter Kit.
-Walk you through a step-by-step tutorial based on the sample dataset, the prepackaged GSQL queries, the GraphStudio UI flow from the Starter Kit, and the integration process with a simple front-end application.
-Demonstrate the end-to-end full stack application development based on TigerGraph Cloud.
Commercial Analytics at Scale in Pharma: From Hackathon to MVP with Azure Dat...Databricks
GSK are a science-led global healthcare company with a special purpose: to help people do more, feel better, live longer.
We have three global businesses that discover, develop and manufacture innovative pharmaceutical medicines, vaccines and consumer healthcare products.
In this talk i will share our experience in the Pharmaceutical business delivering commercial analytics going from hackathon to MVP.
From the initial ideas and business discussions through delivery of a hackathon as an accelerator, on to building an MVP. Using the Azure cloud platform and Databricks to rapidly ingest data and prototype.
I will touch on the challenges, opportunities and learning points of the process we went through to deliver commercial analytics at scale in Pharma.
When it comes to dealing with large, complex, and disparate data sets, traditional database technologies are unable to keep pace with the rich analytics necessary to power today’s data-driven applications. Graph analytics databases are becoming the underlying infrastructure for AI and machine learning. These databases allow users to ask complex questions across complex data, which is not always practical or even possible at scale using other approaches. They also enable faster insights against massive data sets when combined with pattern recognition, statistical analysis, and AI/ machine learning. And in the case of standards-based graph databases, they connect with popular visualization tools like Graphileon, allowing users to easily explore their data stores and quickly build compelling graph-based applications.
In this webinar, data analytics gurus Sathish Thyagarajan and Steve Sarsfield introduce AnzoGraph™, our graph OLAP database, demonstrate the different types of analyses you can perform with it and how it complements Neo4j, AWS Neptune and other OLTP systems. Finally, they’ll show how you can get it up and running on your laptop in about 5 minutes.
Risk Analytics Using Knowledge Graphs / FIBO with Deep LearningCambridge Semantics
This EDM Council webinar, sponsored by Cambridge Semantics Inc. and featuring FI Consulting, explores the challenges common to a risk analytics pipeline, application of graph analytics to mortgage loan data and use cases in adjacent areas including customer service, collections, fraud and AML.
At Data-centric Architecture Forum 2020 Thomas Cook, our Sales Director of AnzoGraph DB, gave his presentation "Knowledge Graph for Machine Learning and Data Science". These are his slides.
Wizard Driven AI Anomaly Detection with Databricks in AzureDatabricks
Fraud is prevalent in every industry, and growing at an increasing rate, as the volume of transactions increases with automation. The National Healthcare Anti-Fraud Association estimates $350B of fraudulent spending. Forbes estimates $25B spending by US banks on anti-money laundering compliance. At the same time as fraud and anomaly detection use cases are booming, the skills gap of expert data scientists available to perform fraud detection is widening.
The Kavi Global team will present a cloud native, wizard-driven AI anomaly detection solution, enabling Citizen Data Scientists to easily create anomaly detection models to automatically flag Collective, Contextual, and Point anomalies, at the transaction level, as well as collusion between actors. Unsupervised methods (Distribution, Clustering, Association, Sequencing, Historical Occurrence, Custom Rules) and supervised (Random Forest, Neural Network) models are executed in Apache Spark on Databricks.
An innovative aggregation framework converts probabilistic fraud scores and their probabilities into a meaningful and actionable prioritized list of suspicious (a statistical outlier) and potentially fraudulent transaction to be investigated from a business point of view. The AI Anomaly Detection models improve over time using Human-in-the-Loop feedback methods to label data for supervised modeling.
Finally, The Kavi team overviews the Anomaly Lifecycle: from statistical outlier to validated business fraud for reclaim and business process changes to long term prevention strategies using proactive audits upstream at the time of estimate to prevent revenue leakage. Two client success stories will be presented acros Pharmaceutical Rx and Transportation industries.
How Graphs Continue to Revolutionize The Prevention of Financial Crime & Frau...Connected Data World
Financial crime prevention is something that affects everyone in one way or another. From the Deutsche Banks of the world to small and medium online merchants, regulations for anti-money laundering, know your customer, and customer due diligence apply.
Failing to comply with such regulations can bring on substantial fines. Even more importantly, it can hurt the bottom line and reputation of businesses, having far-reaching side effects. Complying with such regulations, and actively cracking down on financial crime, however, is not easy.
Cross-referencing interconnected data across various datasets, and trying to apply detection rules and to discover patterns in the data is complicated. It takes expertise, effort, and the right technology to be able to do this efficiently.
A natural and efficient way of looking for patterns and applying rules in troves of interconnected data is to model and view that data as a graph. By modeling data as a graph, and applying graph-based algorithms such as PageRank or Centrality, traversing paths, discovering connections and getting insights becomes possible.
Graphs and graph databases are the fastest-growing area of data management technology for a number of reasons. One of the reasons is because they are a perfect match for use cases involving interconnected data.
Queries that would be very complicated to express and very slow to execute using relational databases or other NoSQL database technology, are feasible using graph databases. With the rise in complexity of modern financial markets, financial crimes require going 4 to 11 levels deep into the account – payment graph: this requires a different solution than either relational or NoSQL databases.
How are organizations such as Alibaba, OpenCorporates, and Visa using graph database technology to not just stay on top of regulation, but be one step ahead in the race against financial crime?
Is it possible to do this in real time?
What do graph query languages have to do with this?
TDWI 17 Munich - Are enterprises ready for the 4th industrial revolution? - S...Santiago Cabrera-Naranjo
To get ready, companies will require cross-functional teams with the right software and discipline to enable data scientists, engineers, product managers, and domain experts to all work together to create a continuous cycle that drives value to the business.
Engineering with Open Source - Hyonjee JooTwo Sigma
Engineering systems using open source solutions can be a powerful way to leverage existing technology. However, not all open source solutions are made or supported equally, and it’s important to choose what you use carefully. In this talk, we’ll walk through building a metrics system for a high performance data platform, taking a look at some of the important factors to consider when choosing what open source offerings to use.
Big Data, Big Thinking: Untapped OpportunitiesSAP Technology
In this webinar factsheet, SAP’s Rohit Nagarajan and Suni Verma from Ernst & Young explore Big Data in India, adoption patterns across the globe, and how you can embark on your own Big Data journey.
Digital Foundations to Transform Customer Experiences Through Process Optimiz...Jared Hill
The Urban Affairs Coalition (UAC) was looking to streamline their processes to save their nonprofit clients time and money, allowing them to have a greater impact. See how Lime Consulting Group helped (UAC) build a roadmap and a business case to gain the consensus they needed internally to secure funding to get started. By mapping business processes in Signavio, UAC was able to standardize the way they do business today, so that they can automate with the use of technology in the future.
BIG Data & Hadoop Applications in FinanceSkillspeed
Explore the applications of BIG Data & Hadoop in Finance via Skillspeed.
BIG Data & Hadoop in Finance is a key differentiator, especially in terms of generating greater investment insights. They are used by companies & professionals for risk assessment, fraud detection & forecasting trends in financial markets.
To get more details regarding BIG Data & Hadoop, please visit - www.SkillSpeed.com
Why Everything You Know About bigdata Is A LieSunil Ranka
As a big data technologist, you can bet that you have heard it all: every crazy claim, myth, and outright lie about what big data is and what it isn't that you can imagine, and probably a few that you can't.If your company has a big data initiative or is considering one, you should be aware of these false statements and the reasons why they are wrong.
Protect Your Revenue Streams: Big Data & Analytics in TaxCapgemini
The game has changed since the onset of the financial crisis. Governments aiming to reduce budget deficits can only deliver so much through spending cuts. It is now even more vital that tax agencies ensure individuals and businesses pay the tax they owe, and that welfare fraud and error are minimised. Pretty will explain how he helps tax and welfare agencies tackle noncompliance, evasion and error. He will share client stories where billions of euros were saved, generating a return of at least 25 times the original investment.
By Ian Pretty,
Vice President, Global Tax & Welfare Leader
Scaling up business value with real-time operational graph analyticsConnected Data World
Graph-based solutions have been in the market for over a decade with deployments in financial services, healthcare, retail, and manufacturing. The graph technology of the past limited them to simple queries (1 or 2 hops), modest data sizes, or slow response times, which limited their value.
A new generation of fast, scalable graph databases, led by TigerGraph, is opening up a new world of business insight and performance. Join us, as we explore some new exciting use cases powered by native parallel graph database with storage and computation capability for each node:
A large financial services payment provider is using graph-based pattern detection (7 to 11 hop queries) to detect more fraud and money laundering in real time, handling peak volume of 256,000 transactions per second.
IceKredit, an innovative FinTech is transforming the near-prime and sub-prime credit market in United States, China and South Asian countries with customer 360 analytics for credit approval and ongoing monitoring.
A biotech and pharmaceutical giant is building a prescriber and patient 360 graph and using multi-hop exploratory and analytic queries to understand the most efficient ways of launching a new drug for maximum return.
Wish.com is delivering real-time personalized recommendations to increase eCommerce revenue.
Riding and Capitalizing the Next Wave of Information TechnologyGoutama Bachtiar
Delivered in guest lecture session for International Business Accounting Program, Faculty of Business and Management, Petra Christian University, Surabaya, East Java, Indonesia.
SuperWeek 2016 - Garbage In Garbage Out: Data Quality in a TMS WorldSimo Ahava
Slides from my talk at the SuperWeek analytics conference. The focus was on organization transformation necessary to improve data quality, especially when using a tag management solution like Google Tag Manager.
Tous les projets stratégiques ont besoin de gestion et de surveillance, et les questions qui se posent souvent:
1. Comment puis-je visualiser la santé de ma plate-forme?
2. Comment puis-je analyser les données de mes applications? 3. Comment puis-je visualiser la performance de l'entreprise et la part des objectifs atteints?
Dans cette présentation nous allons voir comment nous pouvons répondre à ces questions et donner des tableaux de bord spécifiques fondés sur un ensemble de produits open source qui permettent de centraliser et d'analyser des données de votre SI en temps réel.
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
6. 6 of the Top
10 Global
Banks Use
TigerGraph Merchant Analytics:
Transaction
sequencing to
detect geolocation
proximity.
Credit Card Fraud
Is applicant
connected to
potential
fraudsters?
Trade
Surveillance: Are
employees
following the
rules?
Impact Analysis
Communities or
Clusters
impacted by
the fraud rings
Credit Scoring
Real-time credit
scoring to help
recommend
offers best suited
to customer
profiles?
6
Wealth
Management:
What Accounts,
HNI to target for
stocks or life
change events.
12. . | GRAPHAIWORLD.COM |
Semantics of the Use Case
12
Receive Transaction
Does TransactionUses
Device
UsesPI
User
Device
Transaction
Payment Instrument
22. Pagantis Delivers Faster Consumer Finance
Services with TigerGraph on AWS
Business Challenge
Pagantis must assess credit worthiness and fraud risk in real-time for
customers to allow them to pay for their purchase in monthly
installments. Risk assessment with relational databases was taking too
long, delaying the time for loan approvals.
Solution
• Real-time calculation of customer’s credit rating using their
current activities as well as all available historical data
• A scalable, high-performance system to deliver insights into
complex relationship-based workflows for credit scoring, fraud
detection, recommendation engines and risk analysis
Business Benefits
Pagantis can now offer a faster and seamless consumer finance
solution for the eCommerce merchants throughout Italy, France and
Spain.
Press Release - https://info.tigergraph.com/pagantis-tigergraph
22