This document discusses forensic data analytics and its use in fraud risk management. It begins with an agenda on key analytics trends, "big data thinking," and examples of anti-fraud use cases like employee and vendor transaction risk scoring. The document then covers the forensic data analytics landscape, increasing regulatory focus on advanced monitoring, and how analytics can help improve compliance efficiency. It discusses challenges of forensic data analytics and integrating visualization into risk management platforms. The document concludes with success factors for deploying forensic data analytics like focusing on priority projects and gaining leadership support.
Negotiation Strategies: Using Game Theory and Decision Tree Analysis to Deter...brucelb
A detailed case study of how to use Negotiation Strategies, an application of Game Theory and Decision Tree Analysis to develop an optimum strategy for negotiating a settlement in litigation. We demonstrate a process that can: identify and assess negotiation risks; know whether th current Negotiation Strategy will fail in time to change it;
and execute the most effective strategy to get the best possible outcome.
AI powered Decision Making in Banks - How Banks today are using Advanced analytics in credit Decisioning, enhancing customer life time value, lower operating costs and stronger customer acquisition
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
Negotiation Strategies: Using Game Theory and Decision Tree Analysis to Deter...brucelb
A detailed case study of how to use Negotiation Strategies, an application of Game Theory and Decision Tree Analysis to develop an optimum strategy for negotiating a settlement in litigation. We demonstrate a process that can: identify and assess negotiation risks; know whether th current Negotiation Strategy will fail in time to change it;
and execute the most effective strategy to get the best possible outcome.
AI powered Decision Making in Banks - How Banks today are using Advanced analytics in credit Decisioning, enhancing customer life time value, lower operating costs and stronger customer acquisition
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
In this session we will discuss the business case for a proactive, real-time fraud prevention strategy which enables you to maximize revenue opportunities whilst minimizing fraud. During the session we will create a fraud management check list which combines People, Processes and Technology, underpinned by data, analysis and tailored rules.
TechConnex Big Data Series - Big Data in BankingAndre Langevin
TechConnex is an industry forum for Canadian IT executives. This presentation from the fall of 2015 provides a survey of Hadoop adoption in the Canadian banking industry. Most adoption is driven by BCBS-239 implementation projects. The talk provides a broader risk systems perspective on Hadoop and discusses challenges and opportunities around the technology.
In this presentation Juan M. Huerta talks about big data adoption process at Citi, realising the technical value of big data and global solutions. Huerta goes on to talk about following a hybrid approach, and the future of analytics, expensive algorithms applied to large datasets. With Citi using these approaches in hopes of getting even wider global recognition.
Wondering how to bring services to your clients in real time – and on their preferred device? Need to automate your financial supply chain, including risk and compliance functions, and move to a pay for performance model?
Learn about use cases from within the big data ecosystem, ranging from AML compliance, trade lifecycle, fraud detection and digital transformation, and introduce their risk data aggregation and compliance initiative. Find out how you can best leverage Open Enterprise Hadoop to achieve these goals.
Towards the Next Generation Financial Crimes Platform - How Data, Analytics, ...Molly Alexander
Towards the Next Generation Financial Crimes Platform - How Data, Analytics, & ML Are Transforming the Fight Against Fraud, AML & Cybersecurity -Nadeem Asghar
Microdecision Making in Financial Services - Greg Lamp @ PAPIs ConnectPAPIs.io
Fintech startups are taking business away from traditional institutions like banks, exchanges, and brokerages. One of the reasons that these startups are able to compete with $30B+ behemoths like Credit Suisse and Goldman Sachs is their advanced decision making capabilities. By leveraging new data sources and better predictive analytics, companies like Ferratum Bank can make more accurate decisions in a fraction of the time.
This talk will cover:
Types of decisions you can automate
Challenges in building predictive, financial apps
First-hand, real-world examples
Greg Lamp is the co-Founder and CTO of Yhat. In this role, Greg leads development of Yhat's core products and infrastructure and is the principal architect of the company's cloud and on-premise enterprise software applications. Greg was previously a product manager at OnDeck, a fintech startup in New York and before that an analyst at comScore. Greg is a graduate of the University of Virginia.
The advent of ‘big data’ has completely changed the way businesses can harness the information about customers to make powerful business decisions. Data could be of any type – campaign information, customer demographics, individual transaction behavior, interactions on social networks, web usage, or satisfaction surveys etc. BRIDGEi2i has the ability and experience to mine this wealth of unstructured and structured information to help businesses identify prospects, target them through the right channel, maximize cross sell and up-sell opportunities and thereby enhance the life time value of customer relationships. To know more visit: http://www.bridgei2i.com/customer-intelligence.html
Patents are a good information resource for obtaining IoT (Internet of Things) technology development status. IOT big data analytics is becoming important to process unimaginably large amounts of information and data that are obtained by the sensor embedded interconnected IoT devices. The typical IoT big data analytics is Hadoop, an open-source software framework that supports data-intensive distributed applications, and the running of applications on large clusters of commodity hardware. Hadoop, that is based on the architectural framework MapReduce, collects both structured data and unstructured data, processes the collected data set in a distributed network cluster in parallel, and extracts valuable information from the processed data set within a short time. Followings illustrate some examples of patents that provide current status of the IoT big data analytics technology development.
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.
Preventing Tax Evasion & Benefits Fraud Through Predictive AnalyticsCapgemini
Today's tax and welfare agencies are increasingly facing new and sophisticated methods of tax evasion and welfare fraud. Increasing digitization means that fraudsters are becoming faster and new types of fraud, such as ID theft, are growing.
However, with more and better data available, agencies now have the ability to sharpen their insights at higher speeds.
Capgemini’s TROUVE solution, powered by SAS, helps Tax & Welfare agencies harness digital to achieve better, faster and cheaper compliance results.
Presented by Capgemini's Ian Pretty at SAS Analytics 2014.
10 WealthTech podcasts every wealth advisor should listen toIBM Analytics
Listen to this “Finance in Focus” podcast series to hear a cast of interesting experts discuss how the wealth management industry is adapting to new and emerging technologies that include robo-advisors, blockchain, analytics, and cognitive. Over the course of 10 episodes, hosts Rob Stanich and Alex Baghdjian are joined by wealth management experts to discuss behavior financing, DOL fiduciary rule, social media marketing, account aggregation, millennials, surveillance, and regulations.
In this session we will discuss the business case for a proactive, real-time fraud prevention strategy which enables you to maximize revenue opportunities whilst minimizing fraud. During the session we will create a fraud management check list which combines People, Processes and Technology, underpinned by data, analysis and tailored rules.
TechConnex Big Data Series - Big Data in BankingAndre Langevin
TechConnex is an industry forum for Canadian IT executives. This presentation from the fall of 2015 provides a survey of Hadoop adoption in the Canadian banking industry. Most adoption is driven by BCBS-239 implementation projects. The talk provides a broader risk systems perspective on Hadoop and discusses challenges and opportunities around the technology.
In this presentation Juan M. Huerta talks about big data adoption process at Citi, realising the technical value of big data and global solutions. Huerta goes on to talk about following a hybrid approach, and the future of analytics, expensive algorithms applied to large datasets. With Citi using these approaches in hopes of getting even wider global recognition.
Wondering how to bring services to your clients in real time – and on their preferred device? Need to automate your financial supply chain, including risk and compliance functions, and move to a pay for performance model?
Learn about use cases from within the big data ecosystem, ranging from AML compliance, trade lifecycle, fraud detection and digital transformation, and introduce their risk data aggregation and compliance initiative. Find out how you can best leverage Open Enterprise Hadoop to achieve these goals.
Towards the Next Generation Financial Crimes Platform - How Data, Analytics, ...Molly Alexander
Towards the Next Generation Financial Crimes Platform - How Data, Analytics, & ML Are Transforming the Fight Against Fraud, AML & Cybersecurity -Nadeem Asghar
Microdecision Making in Financial Services - Greg Lamp @ PAPIs ConnectPAPIs.io
Fintech startups are taking business away from traditional institutions like banks, exchanges, and brokerages. One of the reasons that these startups are able to compete with $30B+ behemoths like Credit Suisse and Goldman Sachs is their advanced decision making capabilities. By leveraging new data sources and better predictive analytics, companies like Ferratum Bank can make more accurate decisions in a fraction of the time.
This talk will cover:
Types of decisions you can automate
Challenges in building predictive, financial apps
First-hand, real-world examples
Greg Lamp is the co-Founder and CTO of Yhat. In this role, Greg leads development of Yhat's core products and infrastructure and is the principal architect of the company's cloud and on-premise enterprise software applications. Greg was previously a product manager at OnDeck, a fintech startup in New York and before that an analyst at comScore. Greg is a graduate of the University of Virginia.
The advent of ‘big data’ has completely changed the way businesses can harness the information about customers to make powerful business decisions. Data could be of any type – campaign information, customer demographics, individual transaction behavior, interactions on social networks, web usage, or satisfaction surveys etc. BRIDGEi2i has the ability and experience to mine this wealth of unstructured and structured information to help businesses identify prospects, target them through the right channel, maximize cross sell and up-sell opportunities and thereby enhance the life time value of customer relationships. To know more visit: http://www.bridgei2i.com/customer-intelligence.html
Patents are a good information resource for obtaining IoT (Internet of Things) technology development status. IOT big data analytics is becoming important to process unimaginably large amounts of information and data that are obtained by the sensor embedded interconnected IoT devices. The typical IoT big data analytics is Hadoop, an open-source software framework that supports data-intensive distributed applications, and the running of applications on large clusters of commodity hardware. Hadoop, that is based on the architectural framework MapReduce, collects both structured data and unstructured data, processes the collected data set in a distributed network cluster in parallel, and extracts valuable information from the processed data set within a short time. Followings illustrate some examples of patents that provide current status of the IoT big data analytics technology development.
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.
Preventing Tax Evasion & Benefits Fraud Through Predictive AnalyticsCapgemini
Today's tax and welfare agencies are increasingly facing new and sophisticated methods of tax evasion and welfare fraud. Increasing digitization means that fraudsters are becoming faster and new types of fraud, such as ID theft, are growing.
However, with more and better data available, agencies now have the ability to sharpen their insights at higher speeds.
Capgemini’s TROUVE solution, powered by SAS, helps Tax & Welfare agencies harness digital to achieve better, faster and cheaper compliance results.
Presented by Capgemini's Ian Pretty at SAS Analytics 2014.
10 WealthTech podcasts every wealth advisor should listen toIBM Analytics
Listen to this “Finance in Focus” podcast series to hear a cast of interesting experts discuss how the wealth management industry is adapting to new and emerging technologies that include robo-advisors, blockchain, analytics, and cognitive. Over the course of 10 episodes, hosts Rob Stanich and Alex Baghdjian are joined by wealth management experts to discuss behavior financing, DOL fiduciary rule, social media marketing, account aggregation, millennials, surveillance, and regulations.
An Analytics Culture Drives Performance in Asia Pacific Organizations Tableau Software
In the last few years, many researchers and analysts have predicted power shifts in business intelligence and analytics world. Today, self-service analytical tools are enabling information workers everywhere identify new insights and drive business performance.
In this presentation, see what IDC Research expert and Amaysim BI Manager have to say about:
1. Why meeting the analytical needs of business users matter to organizational performance
2. What’s driving leaders in APAC enterprises towards a self-service paradigm?
3. How to encourage adoption of analytical tools in your organization
4. How leading APAC enterprises such as Amaysim are adopting self-service analytics and the benefits they’ve experienced.
Want to learn more? Check out the full webinar at http://www.tableau.com/learn/webinars/how-analytic-culture-drives-performance-asia-pacific-organizations
Cloud computing is becoming the norm. People are no longer asking why they should go to the cloud. Instead, we hear customers asking insight on what’s working and what they should be thinking about.
Bigger, faster, and cloudier: that’s where big data is headed in 2016. More people are doing more things faster with their data, but the details of how continue to evolve. Get up to speed on the latest trends in big data.
Traditional BI promises security and scale, but at what cost? Often, working with data, finding answers and sharing them can be laborious and time intensive. The rapid growth and maturation of cloud technologies offers an easier path.
With Tableau and AWS you can move your BI to the cloud and deliver the security and scale of your traditional BI, but with accessibility, flexibility, and speed. Take a closer look at the benefits of cloud BI, and how you can get started today.
In 2016, cloud technologies went mainstream. But with maturity came the realization that moving to the cloud doesn’t happen overnight. CIOs are prioritizing hosted computing and cloud data storage. But they’re approaching the shift as a gradual, multi-year journey.
Many startups and small businesses will continue to go all-in on cloud. But enterprises will find success in a slow but steady move from on-prem. Hybrid ecosystems—of data, software, and infrastructure—will be the reality for most established organizations.
As this shift to cloud progresses where are things are headed? This paper highlights the top cloud trends for 2017.
Business intelligence norms are evolving across the retail industry, and leading retailers are prioritizing analytics initiatives as a result. While the trend toward retail analytics isn’t new, maturing technologies and techniques are. Here are the trends that will shape retail analytics in 2017.
REanalyze: What is your EVP Data Saying? - October 2014 VolunteerMatch BPNVolunteerMatch
If you weren't able to join us in Detroit for the 2014 VolunteerMatch Client Summit, don't worry! We're bringing you an encore presentation from one of the most popular sessions at the Summit!
Demonstrating your employee volunteer program's impact internally and externally is critical to its success. While the industry as a whole is still looking for ways to get beyond traditional metrics, some companies are taking it upon themselves to identify outcomes that reflect their priorities. They are also looking for new ways to quantify the engagement and impact of their employees so that they can better tell their stories to leadership, employees, nonprofit partners, and the community.
Join Jake Sanches, internal metrics and analytics guru at Palantir Technologies, to discuss VolunteerMatch's recent metrics benchmarking project. We'll review our findings and key takeaways, cover industry trends across key metric benchmarks, and discuss metrics analysis in finer detail and how it can be leveraged to drive improved programmatic and reporting approaches. Jake will also provide recommendations and demonstrate examples of ways to increase your use and presentation of data in your communications.
Guest Speaker:
Jake Sanches
Palantir Technologies
The presentation unifies business value creation and preservation objectives within one framework suitable for use by, and accessible to, all departments of all organizations in all industry sectors. GRC still focuses too much on preserving trust and social capital and not enough on developing them. The entire premise of OCEG's GRC initiative is too narrowly focused and is therefore incomplete. To use a sports analogy, you can't win a football game with defense alone. Offensive business practices develop trust and build social capital, encourage risk taking, facilitate collaboration, and stimulate innovation. These elements remain inadequately addressed by the GRC approach to achieving its Principled Performance objectives.
An interesting slideshow about 10 strange forensic cases that puzzled the pros . For more information about Forensic Science visit: http://www.excite.com/education/criminal-justice/forensics
“Many clients have asked me to assess and recommend fraud detection and mitigation companies. This deck is intended to provide clients an objective and independent point of view and commentary on the known players in the digital ad fraud space.”
Riesgo Risk Management\'s Fraud Management solution is a cost effective means of implementing a Fraud management system that detects, prevents and mitigates fraud. It has adaptors that may sit on servers and trigger alerts to the Fraud Management dashboard.
How to prepare your business for 2020. Meet your new customer in the Customer Revolution in 2020. This presentation was from Jessika Phillips at Social Media Week Lima- #SMWL16
LexisNexis® Risk Solutions commissioned the Fraud Mitigation Study to uncover fraud trends and patterns. 800 fraud mitigation professionals from insurance, financial services, retail, government, healthcare and communications took part in the survey.
Data Privacy Program – a customized solution for the new EU General Regulatio...IAB Bulgaria
Data Privacy Program – a customized solution for the new EU General Regulation on Data Protection, Maria Maxim, Senior Manager – Fraud Investigation & Dispute Service, Ernst&Young
Enterprises are faced by information overload. Big data appears as an opportunity, but has no relevance until enterprises can put it in context of their activities, processes, and organizations, Applying MDM principles to Big Data is therefore an opportunity that enterprises should target.
This presentation covers the following topics :
- what is MDM and Information Management
- what is Big Data and what are the use cases
- why and how Big Data can take advantage of MDM ? why and how MDM can take advantage of Big Data ?
Fortify Your Enterprise with IBM Smarter Counter-Fraud SolutionsPerficient, Inc.
Organizations lose an estimated five percent of annual revenues to fraud, totaling nearly $1 trillion in the U.S. alone. Cyber criminals are more organized and better equipped than ever, and continue to evolve their strategies in order to undermine even the strongest protections.
We continue to hear about major security breaches across all industries, but what is being done to fix the problem? There must be a tight interlock between risk, security, fraud and financial crimes management. Current solutions are proving inadequate as point solutions and a corporate silo mentality directly contribute to the risk of fraudulent activities going undetected.
Our webinar covered:
-How IBM’s Smarter Counter Fraud initiative can help public and private organizations prevent, identify and investigate fraudulent activities
-Real-world use cases including how one financial institution stopped $1M in fraud in the first week after implementing a counter-fraud solution
-Perficient’s multi-tiered approach to help guide successful business outcomes
It’s time to stop the bad guys with IBM Smarter Counter Fraud and Perficient – learn how now!
#IBMInsight session presentation "Mitigate Risk, Combat Fraud and Financial Crimes"
The Issue of fraud, challenges, fighting fraud as an enterprise endeavor, IBM Smarter counter fraud framework and IBM Counter Fraud business services
More at ibm.biz/BdEPRH
Big Data Analytics Fraud Detection and Risk Management in Fintech.pdfSmartinfologiks
Big data analytics is crucial for fraud detection and prevention as well as risk management. As per the Association of Certified Fraud Exmainers’ Reports to the Nations, organizations proactively using data monitoring can minimize their fraud losses by an average of about 54% and identify scams in half the time.
Big data analytics is alternating the patterns in which companies prevent fraud. AI, machine learning, and data mining tech stacks help counteract the hydra of fraud attempts affecting more than 3 billion identities each year.
Penser Consulting answers the key questions:
- What is big data, and why does it matter?
- How can big data drive business decisions?
- How can you build data analytics capabilities in your organisation?
EY India's forensic data analytic models are developed to identify variances in data sets, which may impact an organization’s profit and loss statement. Check out the evolution of forensic data analytics.
Evolution of Forensic Data Analytics - EY IndiaNishantSisodiya
EY India's forensic data analytic models are developed to identify variances in data sets, which may impact an organization’s profit and loss statement. Check out the evolution of forensic data analytics.
Forensic Technology & Discovery Services: The Intelligent Connection - EY Indiasathish kriishnan
EY’s Forensic Technology & Discovery Services (FTDS) practice provides a wide range of eDiscovery, data analytics and cyber breach investigation and response management capabilities, on a global basis.
Evolution of Forensic Data Analytics - EY IndiaNishantSisodiya
EY India's forensic data analytic models are developed to identify variances in data sets, which may impact an organization’s profit and loss statement. Check out the evolution of forensic data analytics.
EY India's forensic data analytic models are developed to identify variances in data sets, which may impact an organization’s profit and loss statement. Check out the evolution of forensic data analytics.
Evolution of Forensic Data Analytics - EY IndiaNina Yadav
EY India's forensic data analytic models are developed to identify variances in data sets, which may impact an organization’s profit and loss statement. Check out the evolution of forensic data analytics.
Evolution of Forensic Data Analytics - EY Indiaaparnatikekar4
EY India's forensic data analytic models are developed to identify variances in data sets, which may impact an organization’s profit and loss statement. Check out the evolution of forensic data analytics.
Organizations continue to struggle to connect the dots and extract meaningful insight from the growing volume and variety of data in Hadoop.
Our Solution: Data Refinement, Entity Resolution and Analysis: Novetta Entity Analytics unifies the data scattered across your systems to give you a single unified view of the people, organizations, locations, and other entities or “things” and their relationships in your enterprise. By revealing the real-world networks, behaviors, and trends of the entities and relationships that exist within corporate data repositories and data silos, you can connect the dots to do completely new things such as enhance the customer experience, do more targeted marketing and reduce the risk of fraud. Novetta Entity Analytics makes Hadoop data useful to anyone using an adaptive process to unify all types of data – regardless of schema – and allows analysts to look at and connect their data in entirely new ways.
The Benefits:
- Accelerate operational insights by constructing complete 360 degree views of a customer, organization, location, product, event, at any volume from any source whether structured or unstructured
- Improve customer service and retention by identifying dissatisfied customers and service problems found in call details, transactions and other volumes of interaction data and documents
- Increase revenues by creating unified customer profiles and relationships to products and services improving cross-sell/up-sell opportunities
- Detect threat and fraud by connecting the dots between people, organizations and events across data sources including transactional details
-Lower costs by solving large complex data integration and management problems using a predictable, linearly scalable platform
OUR DIFFERENTIATORS
Understands unstructured content in context
Uncovers relationships
Finds the signal within the noise
Enabling Governed Data Access with Tableau Data Server Tableau Software
Data Server is one of the most powerful tools within Tableau Server to promote security, governance, data exploration, and collaboration—all while hiding the complexity of your data architecture from business users. It allows you to centrally manage live connections or extracted data sets as well as database drivers. At the same time, Data Server enables business users to have trust and confidence that they are using the right data so they can explore it the way they want and discover new insights that drive business value. Learn how Data Server helps IT become a stronger business enabler with governed data access.
How a Data-Driven Culture Improves Organizational Performance Tableau Software
In the last few years, many researchers and analysts have predicted power shifts in business intelligence and analytics world. Today, self-service analytical tools are enabling information workers everywhere identify new insights and drive business performance.
In this slideshare, learn from IDC research and Amaysim BI Manager about:
Why meeting the analytical needs of business users matter to organizational performance
What’s driving leaders in APAC enterprises towards a self-service paradigm?
How to encourage adoption of analytical tools in your organization
How leading Asia Pacific enterprises such as Amaysim are adopting self-service analytics and the benefits they’ve experienced.
This slideshare came from a full webinar delivered by Tableau. You can the full length webinar at http://www.tableau.com/learn/webinars/how-analytic-culture-drives-performance-asia-pacific-organizations
Every year around this time a group of us at Tableau try to slow down and take a look around. We take some time to talk about what’s happening in the market—what’s new, what’s surprising, what’s meaningful. And what a time to be in the world of data and analytics! Smart new platforms are launched seemingly every month. Organizations are starting to see the benefits of broadly empowering people with data. People are using data in ways that were science fiction just a couple of years ago.
It’s always a great discussion. It’s this discussion that drives our Top 10 Trends in Business Intelligence for 2015.
Tableau Drive, Uma nova metodologia para implantações corporativasTableau Software
O Tableau Drive é uma metodologia para expandir a analítica de autoatendimento. O Drive é baseado em práticas recomendadas de implantações empresariais bem-sucedidas. A metodologia é baseada em métodos iterativos e ágeis que são mais rápidos e mais eficazes que a implantação tradicional em ciclos longos. Um marco da abordagem é um novo modelo de parceria entre o negócio e TI.
A Metodologia do Drive está disponível gratuitamente. Algumas organizações optam por executar o Drive elas mesmas; outras recorrem aos Tableau Services ou Tableau Partners para obter ajuda de especialistas.
Tableau Drive는 셀프 서비스 분석을 확장하기 위한 방법론입니다. Drive는 성공적인 엔터프라이즈 배포 우수 사례를 기반으로 만들어졌습니다. Drive방법론은 주기가 긴 기존 배포에 비해 빠르고 효과적일 뿐만 아니라 반복적이며 대응력이 뛰어난 방법을 사용합니다. 이 접근 방식의 근본에는 비즈니스와 IT 간의 새로운 파트너쉽 모델이 있습니다.
Drive 방법론은 무료로 사용할 수 있습니다. 조직에 따라 직접 Drive를 실행할 수도 있고 Tableau Service 또는 Tableau 파트너에게 문의하여 전문가의 도움을 받을 수 있습니다.
Tableau Drive, Une méthodologie innovante pour les déploiements en entrepriseTableau Software
Tableau Drive est une méthodologie de déploiement de l’analytique en libre-service. Drive est basé sur les meilleures pratiques observées lors de déploiements en entreprise réussis. La méthodologie repose sur des méthodes agiles et itératives, plus rapides et plus efficaces que les déploiements traditionnels à cycle plus long. Un nouveau modèle de partenariat entre utilisateurs métier et services informatiques constitue la pierre angulaire de cette approche.
La méthodologie Drive est disponible gratuitement. Certaines organisations préfèrent exécuter Drive elles-mêmes ; d’autres choisissent de solliciter l’aide de spécialistes via les services Tableau ou les partenaires Tableau.
Tableau Drive, Una nueva metodología para implementaciones empresarialesTableau Software
Tableau Drive es una metodología para ampliar el análisis de autogestión. Drive se basa en prácticas recomendadas de implementaciones empresariales exitosas. La metodología se apoya en métodos iterativos y ágiles que resultan más rápidos y eficaces que la implementación de ciclos largos tradicional. Una piedra angular de este enfoque es un modelo nuevo que proviene de una asociación entre el negocio y la TI.
La metodología de Drive es encuentra disponible de manera gratuita. Algunas organizaciones ejecutarán Drive por cuenta propia; otras consultarán a los servicios de Tableau o a socios de Tableau para obtener ayuda experta.
Tableau Drive, Die neue Methode für Bereitstellungen in UnternehmenTableau Software
Tableau Drive dient als Methode zur Verbreitung einer Self-Service-Analysekultur in Organisationen. Drive basiert auf Best Practices, die bei Unternehmen bereits erfolgreich umgesetzt wurden. Die Methode stützt sich auf iterative, agile Praktiken, die schneller und effektiver sind als herkömmliche Self-Service-Analysen über lange Zeiträume. Grundpfeiler dieses Ansatzes ist ein neues Partnerschaftsmodell zwischen Geschäfts- und IT-Abteilung.
Das Drive-Verfahren ist kostenlos verfügbar. Manche Unternehmen arbeiten eigenständig mit Drive, andere suchen fachliche Hilfe bei Tableau-Services oder wenden sich an einen Tableau-Partner.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
tapal brand analysis PPT slide for comptetive data
Big Risks Requires Big Data Thinking
1. Forensic Data Analytics
2015
Big risks requires big data thinking
Forensic data analytics use cases
Vincent Walden
Partner, EY
November 17, 2015
2. Page 2
Agenda
► Key analytics trends in fraud risk management
► “Big data thinking”
► Anti-fraud use case examples:
► Employee and vendor transaction risk scoring
► Payment stream analysis
► Text mining and dashboards to find potentially improper payments
► Social media analytics
► Email analytics, emotive tone and Fraud Triangle Analytics
► Cyber monitoring and events
► Integrating visualization into your risk management
platform
3. Page 3
The forensic data analytics landscape
► The regulators are upping their game
► Be ready - the regulators are investing in advanced monitoring
technology
► Big risks requires “big data” thinking
► New approaches to counter fraud and compliance monitoring,
beyond simple rules-based tests
► Compliance fatigue? Analytics can help
► Analytics can help improve efficiency and program effectiveness to
help compliance functions audit and monitor smarter – saving both
time and valuable resources
4. Page 4
Upping their game: SEC priorities around
forensic data analytics
-U.S. SEC Chair Mary Jo White, prepared testimony
before the Senate Appropriations Subcommittee,
May 14, 2014
5. Page 5
FDA business landscape
Data analytics is continued focus area in guidance
COSO: Internal Controls Integrated
Framework
1. Principal #8: Fraud Risk Assessment (COSO 2013)
2. New guidance coming in December 2015 will have
significant focus on the use of proactive forensics
data analytics
ACFE Report to the Nation on Occupational Fraud
1. For those companies with proactive data analytics in place, the
cost per fraud incident was 59.7% lower (roughly $100,000
lower per incident) than those companies not using proactive
data analytics – more than any other control listed in the
survey.
2. Further, the median duration of fraud based on the presence of
proactive data analytics was half the time at 12 months vs 24
months.
See 2014 ACFE Report the Nations on Occupational Fraud, Figures 37 and 38
6. Page 6
Forensic data analytics maturity model
Beyond traditional “rules-based queries” – consider all four quadrants
False Positive Rate
High Low
Structured
Data
Detection Rate
Low High
Unstructured
Data
“Traditional” rules-Based Queries &
Analytics
Matching, Grouping, Ordering,
Joining, Filtering
Statistical-Based Analysis
Anomaly Detection, Clustering
Risk Ranking
Traditional Keyword Searching
Keyword Search
Data Visualization & Text Mining
Data visualization, drill-down into
data, text mining
8. Page 8
Definition of Big Data
Gartner: Big Data is high volume,
velocity and variety information assets
that demand cost-effective, innovative
forms of information processing for
enhanced insight and
decision making.
9. Page 9
Big data techniques for counter fraud
► Multiple data sources
► Data visualization
► Text analytics
► Payment/transaction risk scoring
► Predictive modeling – technology assisted monitoring
► Pattern & link analysis
► Flexible deployment models
18. Page 18
Email analytics: Fraud triangle analytics
Fraud Triangle Analytics: Pressure/Opportunity/Rationalization
Employee term analysis
Term hit frequency over time
21. Page 21
Surveillance monitoring: management dashboard
Risk ranking summary at the trader (employee) level
► Risk score by personnel ► Interactive dashboards
22. Page 22
Management alert screen
Trader alert initiation
► Create customized
alerts
► Transparency across multiple data sources:
trades, voice, email, chat, entertainment, etc.
23. Page 23
Trader communication review screen – text
analytics using Watson Content Analytics
► Sentiment analysis
highlighted using WCA
► Issue coding
and tagging
25. Page 25
How is fraud detected?
50% by tip or accident demonstrates the need
for improved analytics
2014 ACFE Report to the Nation on Occupational Fraud
26. Page 26
Start with the “Fraud Tree” of schemes
Fraud tree
Cash
larceny
Theft of
other assets
– inventory/
AR/
fixed assets
Revenue
recognition
Non
financial
Conflicts
of
interest
Bribery and
corruption/
FCPA
Illegal
gratuities
Bid-rigging/
procurement
Corruption Fraudulent statements
Asset misappropriation
Fake
vendor
Payroll
fraud
T&E
fraud
Theft of
data
GAAP Reserves
General focus of auditors
General focus of
internal auditors
General focus of the regulators
(opportunity for Auditors and Investigators)
27. Page 27
Today’s biggest forensic data analytics (FDA)
challenges
Source: 2014 EY Global Forensic Data Analytics Survey (www.ey.com/fdasurvey)
2%
3%
3%
4%
5%
5%
6%
6%
8%
9%
10%
10%
15%
15%
26%
0% 5% 10% 15% 20% 25% 30%
Uncertainty about the relevance of FDA in the Company
FDA producing positive results to indicate and prove any fraud or…
FDA is not prevalent to the culture
Huge volume of data to analyze
To identify fraudulent information across large data sets
Lack of human resources or manpower to operate FDA
Spreading the FDA culture across different Business Units
Difficulty in adapting FDA to comply with different regulations in…
Poor quality or lack of accuracy in the data
To prevent fraud rather than discover fraud
FDA is too expensive
Convincing senior management or the company about the benefits of…
Improving the quality of the analysis process
Challenges with combining data across various IT systems
Getting the right tools or expertise for FDA
28. Page 28
Integrating dashboards into an boarder fraud risk
management platform
Visualization: Detect
fraud within a business
process
Case Management: Assign
tasks, flag transactions and
delegate projects for review
Statistical: Apply fraud
insights and automated
alerts to take action in
real or near time –
when it matters
Pattern & Link: Uncover
hidden fraud and
relationships
Detect
Investigate
Respond
Discover
29. Page 29
An enterprise approach, based on solutions
Entity and Social
Network analytics
Predictive
analytics
Behavioral /
Geospatial
Prioritized
Incidents
Business
intelligence
Context / Text
analytics
Decision
management
Content
management
Case
management
Forensic
analysis
Beneficiaries
Legal & compliance
(including M&A)
Internal Audit
Big Data, scalable platform, delivered on desktop or mobile device
► Flexible approaches, reports and
capabilities for each beneficiary
► Changing risks requires flexible tools
► Knowing “who is who” is key to
identifying patterns & opportunities
► Reduced false positives, better ROI
► Cross enterprise view of exposures
► Expedient audits/ investigations
► Data transparency, no “black box”
Data Governance and Collaboration
Shared Services
& Finance
BU Leadership
& Corporate
Internal Sources
External Sources
Other
beneficiaries
Enterprise Platform
Security
intelligence &
Cyber
Social
media feeds
Shared svcs.
data feeds
ERP systems
Sanctions &
watchlists
News feeds &
adverse media
Internal
reports &
communications
Master &
reference data
Embedded
Intelligence
Activity
Monitoring
Dark Web
30. Page 30
Five success factors in deploying FDA
1. Focus on the low hanging fruit, the priority of the first project
matters
2. Go beyond traditional “rules-based” tests – incorporate big data
thinking
3. Communicate: share information on early successes across
departments / business units to gain broad support
4. Leadership gets it funded, but interpretation of the results by
experienced or trained professionals make the program successful
5. Enterprise-wide deployment takes time, don’t expect overnight
adoption