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A00-240 SAS Statistical commerce Analysis SAS9: Regression and Model

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A00-240 exam Dumps Source : SAS Statistical commerce Analysis SAS9: Regression and Model

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SASInstitute SASInstitute SAS Statistical Business

SAS named a leader in multimodal predictive analytics and computer learning solutions with the aid of independent research firm | killexams.com existent Questions and Pass4sure dumps

CARY, N.C., Sept. 5, 2018 /PRNewswire/ -- SAS has been named a pacesetter within the Forrester Wave™: Multimodal Predictive Analytics and desktop gaining scholarship of solutions, Q3 2018. The report mentioned "SAS builds the primary really multimodal PAML solution," calling out that what may look to live not viable isn't for SAS.

"extremely skilled information scientists and analytical professionals are in short deliver as agencies fight to find solutions to advanced company issues," famed Lorry Hardt, synthetic Intelligence and machine learning Strategist at SAS. "developed for pace and ease, SAS machine learning tackle address utter of the steps necessary for records scientists to company users alike to circle raw facts into actionable insights. considering computing device learning is a cornerstone know-how for synthetic intelligence, this SAS edge is specifically constructive as further and further groups embody the vigour and the covenant of AI."

SAS® visual data Mining and computing device researching presents clients a lone platform to remedy complicated analytical problems. Combining records education visualization superior analytics and model deployment, it unifies the total desktop discovering technique, from records entry/transformation and instruction to scoring, in a lone atmosphere. working on the SAS® Viya® engine, SAS visible records Mining and computer learning comprises the newest statistical, computer discovering, profound getting to know and text evaluation algorithms that accelerate structured and unstructured records explorations, whereas additionally assisting customary open source languages.

The document states: "[SAS] visible records Mining and desktop getting to know platform lets you travel round-commute between visual desktop gaining scholarship of pipelines and code, and presents integrated points for computerized desktop studying. utter of here is packaged in a slick visible, (veteran SAS clients fill in mind), unified environment that comprises a growing set of well-concept via visible tools for creating fashions – even profound neural networks."

SAS ranked maximum within the existing offering and market presence categories and received among the optimum scores in the strategy class.

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About SASSAS is the chief in analytics. via innovative software and services, SAS empowers and evokes clients around the globe to radically change records into intelligence. SAS gives you THE vigor to understand®.

SAS and utter other SAS Institute Inc. product or provider names are registered logos or trademarks of SAS Institute Inc. in the us of a and different nations. ® suggests country registration. other manufacturer and product names are emblems of their respective agencies.  Copyright © 2018 SAS Institute Inc. utter rights reserved.

 

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SAS Honored for Analytics Innovation | killexams.com existent Questions and Pass4sure dumps

CARY, N.C.--(enterprise WIRE)--massive data from enterprise, mobile and social platforms is overwhelming those making an attempt to glean solutions from the overload. offering ingenious textual content analytics and excessive-performance analytics, SAS helps corporations squeeze value from huge statistics – turning details about consumers, efficiency and financials into meaningful tips. For management in enterprise analytics, SAS is lauded via KMWorld as a corporation that matters in skills management.

“SAS’ excellence within the analytics arena is indicative of its approach to its total portfolio of solutions,” said Hugh McKellar, Editor-in-Chief, KMWorld. “SAS demonstrates an excellent dedication to assist valued clientele harvest their collective intelligence for fact-primarily based selections that resolve issues and capture opportunities.”

floor vital unstructured records with SAS®text Analytics

Deriving significant, legitimate tips from the starting to live range, quantity and velocity of statistics is essential to succeeding in social media marketplaces. herbal language processing and advanced statistical modeling in SAS text Analytics travel a long artery past ordinary search to immediately verify relevance and find hard-to-spot tendencies, patterns and sentiments. the usage of SAS text Analytics, producers respond to warranty claims and proper defense considerations, fitness corporations call and mitigate outbreaks to tender protection to residents, and sellers mine social media to enrich items and pride.

seriously change suggestions overload into commerce cost with SAS®excessive-efficiency Analytics

huge statistics influences distinct company levels. high-efficiency Analytics from SAS includes SAS In-Database, SAS Grid Computing and SAS In-reminiscence Analytics to unravel advanced company problems. as an example, high-efficiency Analytics from SAS helps monetary functions companies determine mortgage default possibility to in the reduction of default charges and cost-offs, and achieve excellence in default management. To manufacture confident consumers secure maximum return from facts and IT investments, SAS backs its technologies with a replete purview of consulting, practicing and aid features.

Inclusion during this checklist of 100 corporations is in response to the influence and breadth of innovations as well because the cost offered to the capabilities management trade as an entire. This advice was announced utter the artery through Predictive Analytics World and text Analytics World in San Francisco where SAS is featuring March 4-10.

About SAS

SAS is the chief in commerce analytics utility and functions, and the largest impartial seller in the company intelligence market. through innovative options, SAS helps customers at greater than fifty five,000 websites augment efficiency and carry cost with the aid of making more desirable choices quicker. since 1976 SAS has been giving valued clientele utter over THE power to understand®. SAS and utter different SAS Institute Inc. product or provider names are registered trademarks or logos of SAS Institute Inc. in the united states of america and other international locations. ® indicates united states registration. other manufacturer and product names are trademarks of their respective groups. Copyright © 2012 SAS Institute Inc. utter rights reserved.

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Oil and gasoline facts Monetization Market - global trade analysis, measurement, Share, boom, tendencies, and Forecast 2018 - 2026 | killexams.com existent Questions and Pass4sure dumps

LONDON, Oct. 10, 2018 /PRNewswire/ -- Oil and GasData Monetization Market: OverviewThe oil and gasoline statistics monetization market document offers evaluation of the oil and gasoline statistics monetization market for the duration 2016 –2026, wherein the years from 2018 to 2026 is the forecast length and 2017 is regarded as the ground 12 months.The record covers utter the principal tendencies and technologies taking Part in a major function in the boom of the oil and gasoline information monetization market over the forecast period.

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It highlights the drivers, restraints, and alternatives anticipated to affect the market boom utter the artery through this length. The witness at offers a holistic point of view on the market's augment when it comes to income (in US$ Mn) throughout distinctive geographical regions and nations namely the U.S., Canada, U.ok., Germany, France, China, Japan, India, Australia, GCC, South Africa, and Brazil.

The market overview section of the file demonstrates market dynamics such because the drivers, restraints, and alternatives that repercussion the existing nature and future popularity of this market, key trends, regulations and policies, oil and gasoline CAPEX analysis, oil and gas IT spending analysis, ecosystem evaluation, Porter's five drive analysis, and PESTEL evaluation of the market.A market attractiveness analysis has been offered for every segment in the report, with a purpose to provide an intensive figuring out of the standard state of affairs in the oil and fuel data monetization market.

The file besides gives an contour of a variety of concepts adopted by key players available in the market.

global Oil and gasoline facts Monetization Market: Scope of the ReportThe records monetization in oil and gas market is segmented according to method, element, oil agencies, utility, E&P lifecycle, and location. The market has two strategies of monetization viz., direct and oblique. by component, the market is segmented as facts-as-a-service, professional functions, and platform/utility.The oil and gasoline information monetization market is besides segmented in response to upstream oil organizations into national Oil corporations (NOCs), independent Oil companies (IOCs), country wide facts Repository (NDR), and oil and fuel carrier agencies. extra, via software, the market is segmented into upstream (standard, unconventional), midstream, and downstream. Upstream application segment is additionally categorized through E&P lifecycle into exploration, development, and construction segments. The record offers in-depth segment evaluation of the international and country stage oil and fuel information monetization market, thereby providing advantageous insights at the macro as well as micro stages.

The document besides offers the competitive landscape for the oil and gas facts monetization market, thereby checklist out utter of the essential players based on their geographic presence, market elegance, and recent key tendencies.The competitive landscape component to the record gives an contour concerning the market partake of a few key gamers for the 12 months 2017.

Oil and gasoline statistics monetization market information estimates are the effect of their in-depth secondary analysis, simple interviews, and in-house knowledgeable panel stories.These market estimates had been analyzed by using taking into consideration the fill an effect on of distinctive political, social, economic, technological, and prison elements along with the present market dynamics affecting the market increase.

moreover, the key takeaways section provided on the conclusion of the aggressive landscape Part would succor the operating corporations to manufacture the superior moves during this market.

This report provides utter the fundamental tips required to abide in mind the essential thing trends in the oil and gasoline data monetization market, and growth tendencies of every section and location.It besides contains primary overview, income breakdown through geography, product portfolio, revenue,SWOT analysis, approach,and developmentsunder the company profile section.

additionally, the file provides insights regarding tendencies and their fill an repercussion on available on the market. in addition, Porter's five Forces analysis explains the five forces, specifically consumers bargaining vigour, suppliers bargaining energy, desultory of recent entrants, threat of substitutes, and diploma of competition in themarket.This file besides gives a complete ecosystem evaluation of the oil and gas data monetization market.

global Oil and GasData Monetization Market: analysis MethodologyThe analysis methodology is a faultless combination of simple analysis, secondary research, and expert panel studies. Secondary research sources reminiscent of annual reviews, commerce web sites, broker reports, monetary studies, SEC filings and investor presentations, countrywide executive files, inner and exterior proprietary databases, statistical databases, crucial patent and regulatory databases, market reviews, govt publications, statistical databases, World bank databaseand industry white papers are referred.

basic research includes telephonic interviews, electronic mail interactions, and face-to-face interviews for specified and impartial experiences on the oil and gas records monetization market,throughout geographies.primary interviews are constantly conducted on an ongoing basis with industry expertsand individuals as a artery to secure latest market insights and validate the latest information and analysis.

simple interviews offerfirst-hand tips on essential elements akin to market developments, market measurement, competitive panorama,increase traits and outlook and so on.These components succor to validate and reinforce secondary analysis findings and besides aid to augment the analysis crew's edge and market understanding.

in addition, the statistics amassed and analyzed from secondary and first analysis is again discussed and examined through their skilled panel.

international Oil and GasData Monetization Market: aggressive DynamicsHalliburton, Schlumberger confined, Informatica agency, SAP SE, Oracle organization, Accenture plc, IBM organisation, EMC supplier, Microsoft commerce enterprise, Tata Consultancy capabilities Ltd., Datawatch, Drillinginfo, Hitachi Vantara commerce enterprise, Hortonworks, Inc., Capgemini SE, Newgen software, Inc., Cloudera, Inc., Cisco software, Inc., SAS Institute, Inc., MapR technologies, Inc., Palantir options, and OSIsoft LLC, Infosys constrained and NETSCOUT are some of thekey playersthathave been profiled during this analyze. details comparable to financials, enterprise suggestions, key opponents, fresh developments,and other such strategic information pertaining to these gamers havebeen provided as Part of commerce profiling.

The international oil and gasdata monetization market is segmented as beneath:

international Oil and fuel data Monetization Market, by using MethodDirect records MonetizationIndirect facts Monetization

international Oil and fuel statistics Monetization Market, by means of ComponentData-as-a-serviceProfessional ServicesSoftware/Platform

international Oil and gasoline Direct data Monetization Market, with the aid of ApplicationUpstreamConventionalUnconventionalMidstreamDownstream

global Oil and fuel roundabout statistics Monetization Market, by means of ApplicationUpstreamConventionalUnconventionalMidstreamDownstreamGlobal Upstream Oil and gasoline Direct information Monetization Market, by using E&P Lifecycle

ExplorationDevelopmentProductionGlobal Upstream Oil and gasoline oblique data Monetization Market, by using E&P Lifecycle

ExplorationDevelopmentProductionGlobal Upstream Oil and fuel Direct records Monetization Market, by using Oil corporations

countrywide Oil groups (NOCs)unbiased Oil corporations (IOCs)country wide data Repositories (NDRs)Oil and gas service CompaniesGlobal Upstream Oil and gas roundabout records Monetization Market, through Oil organizations

national Oil agencies (NOCs)unbiased Oil groups (IOCs)country wide facts Repositories (NDRs)Oil and gas carrier corporations

international Oil and gas facts Monetization Market, by means of CountryThe united statesCanadaRest of North AmericaThe U.KGermanyFranceRest of EuropeIndiaChinaJapanRest of APACGCC CountriesSouth AfricaRest of MEABrazilRest of South america

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For greater suggestions:Sarah SmithResearch marketing consultant at Reportbuyer.comEmail: sarah@reportbuyer.comTel: +1 (718) 213 4904Website: www.reportbuyer.com

Cision View normal content:https://www.prnewswire.com/news-releases/oil-and-gasoline-facts-monetization-market---international-business-analysis-dimension-share-growth-developments-and-forecast-2018---2026-300728694.html

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A00-240 SAS Statistical commerce Analysis SAS9: Regression and Model

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A00-240 exam Dumps Source : SAS Statistical commerce Analysis SAS9: Regression and Model

Test Code : A00-240
Test appellation : SAS Statistical commerce Analysis SAS9: Regression and Model
Vendor appellation : SASInstitute
: 65 existent Questions

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SAS Statistical commerce Analysis SAS9: Regression and Model

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SAS(R) Visual Analytics and SAS(R) Visual Statistics Integrate to succor commerce Users and Data Scientists drudgery in Tandem | killexams.com existent questions and Pass4sure dumps

DALLAS, TX--(Marketwired - April 27, 2015) - SAS GLOBAL FORUM - unusual releases from SAS ease the artery for commerce users and data scientists to collaborate. unusual integration unites SAS' approved visual data exploration software, SAS Visual Analytics, with SAS Visual Statistics, a point-and-click application for developing predictive models interactively. The updates become generally available in May.

Customers are shifting toward merging self-service data exploration and reporting with advanced analytics. Serving a wider group of users -- statisticians, data scientists, line of commerce analysts, risk managers and others -- the SAS offering makes any enterprise more nimble.

SAS Visual Statistics adds even deeper statistical analysis to existing SAS Visual Analytics capabilities. Accessed through SAS Visual Analytics' web-based, intuitive interface, the advanced tools unravel difficult problems faster for better commerce decisions. Data scientists can soundless spend SAS Visual Analytics to visualize and disseminate information across an enterprise.

Both products spend SAS In-Memory Analytics processing to dramatically truncate time to commerce insight, regardless of data size. Multiple users can concurrently resolve complex data on Hadoop clusters, relational database systems or SAS servers.

"The unusual releases reflect the converging needs of commerce professionals and data scientists, as both sets of users seek to boost their productivity," said Wayne Thompson, SAS Chief Data Scientist. "Some commerce analysts want to travel beyond descriptive analytics and execute more with predictive analytics. SAS provides an facile artery to witness into the future."

For example, users can build a regression model and then superimpose the predictions onto a geographic map with a few clicks. Data scientists can create models in SAS Visual Statistics and explore the predicted outputs in a variety of visualizations in SAS Visual Analytics, including predictive visualizations. And with back for iPhone®5 and later, and several Android smartphones, users can partake and explore interactive visualizations wherever they are.

QVC visualizes customer desires

For its Italian operation, multimedia retailer QVC selected SAS visual solutions to explore and resolve business-critical data, and then divide dynamic internal reports. SAS helps QVC:

  • Explore data in a self-service manner, even by  non-IT experts.
  • Optimize commerce processes through visualizations.
  • Analyze, predict, identify and accommodate  future trends.
  • "Combining SAS Visual Statistics and SAS Visual Analytics allows us to quickly resolve and interpret customer data and supply chain performance," said Giovanni Monopoli, Strategic Analysis and monetary Planning Manager at QVC Italy. "Today, they can easily and precisely apply advanced analytics to call the tastes and preferences of their customers. And importantly, they can then forward information to the sales force, even on a mobile device."

    Using SAS, QVC has drastically reduced time spent on reporting, and maximized interdepartmental alignment. Reports are simple and current. Data is facile to understand through custom visualizations. Users can spot otherwise hidden insights. And SAS software suggests viable actions. QVC reports that the software deployment resulted in an immediate and positive repercussion on decision making.

    The interactive elements in SAS Visual Analytics now extend to Excel, PowerPoint and Microsoft Office products, enabling better storytelling with data. Any changes in a report are updated in the Office application.

    Today's announcement was made at SAS Global Forum, the world's largest gathering of SAS users, attended by more than 5,000 commerce and IT users of SAS software and solutions.

    For other perspectives on how analytics can produce better commerce insights, tickle visit the SAS Insights web page.

    About SAS

    SAS is the leader in commerce analytics software and services, and the largest independent vendor in the commerce intelligence market. Through innovative solutions, SAS helps customers at more than 75,000 sites ameliorate performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world THE POWER TO KNOW®.

    SAS and utter other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. Copyright © 2015 SAS Institute Inc. utter rights reserved.

    The following files are available for download:

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    ‘Dubai authorities are promising access to a wide array of data by 2021’ | killexams.com existent questions and Pass4sure dumps

    Big data and the predictive analytics models built around it were impacting their lives long before they were even conscious of it, operating nondescriptly in the background.

    In the 1970s, for example, mail order companies would spend great data and algorithmic analytic models to determine which customers they should forward their costly and limited supply of mail order catalogues to in order to extract the maximum yield. If you received a mail-order catalogue, you were probably the target of a well-mapped statistical model.

    In 1976, tech entrepreneur James Goodnight set up Statistical Analysis System (SAS) in Cary, North Carolina, where it is soundless headquartered, to succor businesses fancy those mail-order companies develop the required analytic tools.

    “When they started 40 years ago, they needed to educate customers on the requisite to spend algorithms for decision making,”says Shafik Khashouf, vice president, professional services and delivery, South Europe, Middle East and Africa at SAS.

    “Customers back then were quite resistant to the conception of being aided by computers
or even outsourcing their decisions to algorithms. This has now completely changed. They don’t requisite to educate the market anymore. The market is hungry and in desperate requisite of AI and great data solutions.”

    Read: How AI can live a net generator of jobs in the GCC

    How exactly execute firms fancy SAS leverage those copious amounts of raw data into real- world solutions for companies? Khashouf explains that a life cycle of data-to-value typically goes through three stages: The data management stage, the discovery stage and lastly the deployment phase.

    “At the data stage they succor manage the data to provide optimal data quality. At the discovery side they provide software and methods to draw insights from the data.
At the terminal stage they deploy models to draw predictions from the data you’ve trained the system on,” he says.

    The data stage is crucial because SAS can provide its decades-worth of accumulated bailiwick scholarship and software tools to succor companies determine what data they should live collecting (and what they can filter out) in order to achieve their goals.

    The discovery side traditionally used simplistic linear models or linear regression models to draw insights from the data
at hand. Not any longer, though. That’s where companies fancy SAS fill stepped in by integrating what Khashouf refers to as “explainable AI” models into the mix.

    Here’s an specimen of it in a real-world scenario. Let’s deem credit scoring, which used to live a purely statistical model. If your application is rejected, you are entitled to an explanation from the bank as to why you fill been rejected.

    But often, the worn linear models could
not provide those explanations because the decision-making process was inadvertently programmed to arbitrarily reject an application based on maybe a person’s gender or nationality.

    Enter AI, which is far more sophisticated than linear systems and correlates data across multiple data points. “AI provides judgement codes as to why you were rejected in order to manufacture confident that the algorithms didn’t reliance on biased reasons for those rejections.”

    Sticking with the specimen of the credit application, a factor fancy your nationality will no longer live the most essential criteria in determining your creditworthiness even if the most number of defaulters at the bank you are applying to for credit are indeed your fellow nationals.

    “We provide technologies that succor you to not only draw insight from that data but to interpret how the AI is learning from the data to reach those decisions,” says Khashouf.

    The AI systems in station today delivers not just a decision, but are able to interpret how
it got there – something that linear models could never do. “We requisite models to provide explainability. This is where SAS is heavily invested in at the moment.”

    “At the discovery phase, SAS puts into station supervised and unsupervised methods to manufacture predictions of what is being learnt out of the data,” says Khashouf. This ensures there’s a equipoise struck and that the AI models are doing what they’re designed to do.

    Read: How adopting AI can succor GCC firms to boost profits

    Worldwide, the International Data Corporation (IDC) expects spending on great data and commerce analytics to reach $220bn by 2020. In the Middle East and Africa, expenditure on great data analytics (BDA) is expected to reach $3.2bn in the selfsame age with double-digit growth thereafter.

    This growth in the great data analytics bailiwick can live attributed to the fact that it is being used by companies spread across sectors. In the marketing field, great data analytics is used to understand behaviour based on a customer’s transactional data, life stage, loyalty factors, and more.

    In the healthcare sector, AI and machine learning are used to track patient records and insinuate health plans, as well as indexing of insurance information and even detect healthcare fraud.

    In the public sector, governments are leaning towards what is being termed as evidence-based policymaking in which governments takes the outcomes received from data subjected to AI models to succor it formulate policies on taxation, state benefits, and so on.

    Here in the Middle East, Khashouf says that every client they’ve interacted with already has an analytics programme in station or in the process of acquiring one. “We’ve been working with their customers in this region not just to sell software to them, but besides to succor them build their competencies, update their skill sets and succor them change their organisational culture so that they can reliance more on data-driven decision making rather than gut feeling. We’re helping them to develop their human capital.”

    With data scandals fancy Cambridge Analytica soundless fresh, has SAS and other companies fancy it faced a ‘data backlash’ of any kind? Khashouf reiterates that SAS doesn’t undertake data harvesting itself but works with data already gathered by companies.

    “We fill not experienced data backlash because they drudgery with customers to draw intelligence on the data that they fill collected. We’re not in the commerce of selling the insight from that data to other providers, aka the Facebook model. There has been a unusual regulation that has been enacted recently – for example, the general Data Protection Regulation (GDPR) in the EU – to address data privacy.”

    Interestingly, Khashouf says that these unusual government regulations aren’t constricting SAS’s commerce model, but instead opening up unusual commerce opportunities.

    Read: How AI will create the digital oilfields of the future in the GCC

    “We are now providing software services to companies centred on customer data protection. They are helping them obtain the stipulated levels of consent from customers during their data collection and to live in a position to provide their customers with ‘the prerogative to live forgotten’ service too.”

    As well as regulating the industry, governments from the EU to the UAE are besides encouraging open access to data. “The UAE government has realised the benefits of great data access. Dubai authorities are promising access to a wide array of data by 2021 to both individuals and businesses too. This data will undoubtedly drive the Smart Dubai 2021 initiative and accelerate the leader’s decision to live the happiest and smartest city in the world.”

    While it’s facile to celebrate the deeper integration of AI into the data analytics business, there are sober ethical concerns around it. “For example, when you’re driving a car and you suddenly requisite to settle whether to hit a pedestrian or crash into the curb, it is you who is making that decision,” says Khashouf.
”But in an autonomous car where it is AI controlling the car, who really is making – and therefore liable – for that decision?
Is it the programmer, the company that’s implemented that programme or is it you for not overriding the system?”

    “There is a lot of debate around where AI should initiate and where it should end. Should it serve as a decision back system and leave the ultimate decisions to humans? The line
is being blurred very quickly between what
is decision back and where the system is being allowed to manufacture decisions for you.” Companies fancy SAS that find themselves in the middle of the AI ethics debate are doing utter that they can to mitigate the ill effects of the technology.

    “During the deployment side they manufacture confident that the predictions made by AI are safe, auditable and traceable. They test them with a wide variety of random situations so that they understand the first order, second order and third order implications of the decisions being made.”

    And after over four decades in the business, SAS has had ringside seats to the evolution of the data industry. “Access to cheaper storage and faster computing power, for example, has changed their industry to something completely different to what it was even three years ago,” explains the VP.

    By working across an international set of clients, SAS has besides seen what happens to those companies that reliance on data analytics models and those who don’t.

    “Companies today are using AI to
acquire customers more intelligently. Those companies that fill this data model in station are increasing their revenues faster than their competitors. Companies that ignore the competitive edge of data will lag,” says Khashouf.

    “The benefits you can extract from AI are numerous – we’ve not even scratched the surface yet.”


    Fraud detection and machine learning: | killexams.com existent questions and Pass4sure dumps

    Fraud detection is a challenging problem. The fact is that fraudulent transactions are rare; they portray a very wee fraction of activity within an organization. The challenge is that a wee percentage of activity can quickly circle into great dollar losses without the prerogative tools and systems in place. Criminals are crafty. As traditional fraud schemes fail to pay off, fraudsters fill scholarly to change their tactics. The safe advice is that with advances in machine learning, systems can learn, adapt and uncover emerging patterns for preventing fraud.

    Most organizations soundless spend rule-based systems as their primary instrument to detect fraud. Rules can execute an excellent job of uncovering known patterns; but rules solitary aren’t very efficacious at uncovering unknown schemes, adapting to unusual fraud patterns, or handling fraudsters’ increasingly sophisticated techniques. This is where machine learning becomes necessary for fraud detection.

    Machine learning is utter the rage now. Most vendors pretension they fill some shape of machine learning, especially for fraud detection. SAS has been a pioneer in machine learning since the 1980s, when neural networks were first used to combat credit card fraud. But just because we’ve been doing it so long doesn’t matter we’ve been resting on their laurels. In fact, it’s quite the opposite.

    Machine learning is a censorious Part of the fraud detection toolkit. Here’s what you’ll requisite to secure started.

    Data!

    Data sets are only growing larger, and as the volumes increase, so does the challenge of detecting fraud. In fact, data is key when it comes to edifice machine learning systems. The adage that more data equals better models is wholehearted when it comes to fraud detection. Practitioners requisite their machine learning platform to scale as data and complexity increase. While academic tools often drudgery well with thousands of records and a few megabytes of data, real-world problems are measured in gigabytes or even terabytes of data.

    The advantages of multiplicity

    There is no lone machine learning algorithm or artery that works. Success comes from the talent to try lots of different machine learning-based methods, trying variations on them and testing them with a variety of data sets. The data scientist needs a toolkit with a variety of supervised and unsupervised methods – as well as a variety of feature engineering techniques. Finally, there is a creative aspect or “art” to machine learning for fraud detection. It’s the application of machine learning in unusual and novel ways, fancy combining a variety of supervised and unsupervised methods in one system to live more efficacious than any lone artery alone. 

    There is a creative aspect or “art” to machine learning for fraud detection. It’s the application of machine learning in unusual and novel ways, fancy combining a variety of supervised and unsupervised methods in one system to live more efficacious than any lone artery alone. Michael Ames Senior Director, Data Science and Emerging Technology SAS

    Integration into operations

    It should live obvious, but this one’s a challenge for many organizations. Once you fill a machine learning model developed, the challenge becomes integrating it with operations. If your data is in Hadoop, it makes sense that your machine learning model can live applied in Hadoop. Similarly, if your data is streaming in real-time systems, you want a machine learning engine that can dash in existent time or in stream. Portability of the model and integration of the decision logic within operational systems is paramount to stopping fraud at scale – and as it occurs at scale.

    White boxing

    Explaining what a machine learning system is doing is critical; this is often referred to as “white boxing.” Machine learning methods and models are generally black boxes. It’s very difficult (if not impossible) to interpret to analysts why they got the score or decision that they received. There are many approaches to including scorecards based on local linear approximation, generation of textual narratives and generation of graphical visualizations. These are approximations, but can provide users insight into the machine learning model and pilot the fraud investigation process.

    Ongoing monitoring

    All things change, and you must adapt over time. Ongoing monitoring of machine learning fraud detection systems is imperative for success. As populations and the underlying data shift, expected system inputs debase and therefore fill an repercussion on overall performance. This isn’t unique to machine learning systems; rule-based systems fill the selfsame challenge. But newer machine learning methods can adapt to unusual and unidentified patterns as underlying changes occur. This eliminates some, but not all, of the machine learning retraining and evaluation steps.

    A safe monitoring program proactively looks at the data entering the system, evaluates the machine learning model’s predictions and explanations, and alerts administrators to shifting data trends and statistics before dramatic changes affect operations and the bottom line.

    What about the repercussion on your customers?

    For one monetary institution, fighting fraudulent cases was a challenge. It had to identify nefarious transactions, but besides maintain character customer service. A vigilant fraud detection exertion cannot live intrusive to the customer by flagging – and declining – legitimate transactions.

    This monetary institution wanted to modernize its rule-based fraud detection system and strike a equipoise between oversight and customer service. To execute this it worked with SAS to implement a machine learning-based fraud detection solution that takes edge of an ensemble of neural networks to create two different fraud scores:

  • A primary fraud score, evaluating the likelihood that an account is in a fraudulent state.
  • A transactional score, evaluating the likelihood that an individual transaction is fraudulent.
  • Using this approach, the monetary institution could correctly identify near to $1 million in monthly transitions that had been erroneously identified as fraud, and was able to identify an additional $1.5 million per month in additional fraud that had gone undetected. Besides dramatically improving the company’s talent to detect fraud, the solution significantly increased customer satisfaction by reducing the friction between the company and its customers. How? By significantly improving the transaction approval process while increasing the effectiveness of fraud detection.

    Think out of the box

    Finally, successful machine learning programs fill an ingredient of ongoing experimentation. It isn’t enough to just build a machine learning model and let it crunch. Fraudsters are clever, and technology is changing fast. Having a sandbox where data scientists can freely experiment with a variety of methods, data and techniques to combat fraud has become a censorious aspect of top programs. Investments in boosting the capacity of data scientists who combat fraud fill an almost immediate payback.

    Analytics and the AML Paradigm Switch

    Financial organizations are deploying synthetic intelligence and machine learning in the fight against monetary crimes. In this ISMG report, David Stewart, Director of Pre-Sales for the Global Security Intelligence practice at SAS, offers tips to succor separate fact from market hype when reviewing unusual data analytics tools.

    Get the report

    So what exactly is machine learning?

    Simply put, machine learning automates the extraction of known and unknown patterns from data. It expresses those patterns as either a formula or instruction set that can live applied to unusual and unseen data. The machine learns and adapts as outcomes and unusual patterns are presented to it, and can live either supervised or unsupervised.

    Supervised machine learning is a class of analytic methods that attempt to learn from identified records in data; this is often referred to as labeled data. To train a supervised model, you present it both fraudulent and nonfraudulent records, and the model then attempts to infer a function or instruction set that can call whether fraud is present by applying it to unusual examples. Common supervised machine learning methods embrace logistic regression, neural networks, decision trees, gradient bosting machines, random forests of trees, back vector machines and many more.

    Unsupervised machine learning is different. Since you don’t know what data is fraudulent, you want the model to create a function that describes the structure of the data. This artery the model flags anything that doesn’t apt the model as an anomaly. To train an unsupervised model, you simply present it data and the model attempts to infer a function or instruction set that describes the underlying structure and dimensions of the data. This function or instruction set can then live applied to unusual and unseen data.

    The challenge with unsupervised methods is that it’s often difficult to assess the accuracy of the detection scheme until data has been worked and verified by hand. Common unsupervised machine learning methods embrace self-organizing maps, k-means, dbscan, kernel density estimates, one-class back vector machines, principal component analysis and many more.

    And onto synthetic intelligence

    We’ve near a long artery from statistical analysis to machine learning, but the momentum of machine learning and synthetic intelligence is gaining speed. To find out more, check out The Enterprise AI Promise, which describes a phone survey of executives from 100 organizations across Europe in banking, insurance, manufacturing, retail, government and other industries. The SAS study was conducted in 2017 to measure how commerce leaders felt about AI’s potential, how they spend it today and artery to spend it in the future, and what challenges they face.

    New enhancements. Powerful capabilities. Innovative results.



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