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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 solid | killexams.com true 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 seem to live not workable 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," famous Lorry Hardt, synthetic Intelligence and machine learning Strategist at SAS. "developed for pace and ease, SAS machine learning rig address perfect of the steps necessary for records scientists to company users alike to revolve raw facts into actionable insights. considering computing device learning is a cornerstone know-how for synthetic intelligence, this SAS odds 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 separate 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 separate atmosphere. working on the SAS® Viya® engine, SAS visible records Mining and computer learning comprises the newest statistical, computer discovering, deep 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 tide round-commute between visual desktop gaining scholarship of pipelines and code, and presents integrated points for computerized desktop studying. perfect 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 deep neural networks."

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

are attempting SAS visible data Mining and computing device discovering without charge.

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 verve to understand®.

SAS and perfect 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. perfect rights reserved.

 

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SAS Honored for Analytics Innovation | killexams.com true 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 whole 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 tide a long route 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 forecast and mitigate outbreaks to offer protection to residents, and sellers mine social media to enrich items and pride.

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

huge statistics influences several company levels. high-efficiency Analytics from SAS includes SAS In-Database, SAS Grid Computing and SAS In-reminiscence Analytics to decipher advanced company problems. as an example, high-efficiency Analytics from SAS helps pecuniary functions companies determine mortgage default possibility to in the reduction of default charges and cost-offs, and achieve excellence in default management. To accomplish certain consumers glean maximum revert from facts and IT investments, SAS backs its technologies with a replete range 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 expense offered to the capabilities management trade as an entire. This news was announced perfect the route 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 trade 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 perfect over THE power to understand®. SAS and perfect 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. perfect 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 true 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 perfect the principal tendencies and technologies taking portion in a major duty 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 influence the market boom perfect the route through this length. The determine 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 yardstick status of affairs in the oil and fuel data monetization market.

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

Oil and gasoline statistics monetization market information estimates are the consequence 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 consequence 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 portion would abet the operating corporations to accomplish the superior moves during this market.

This report provides perfect the fundamental tips required to endure in intellect the essential thing trends in the oil and gasoline data monetization market, and growth tendencies of every section and location.It likewise 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 likewise 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 perfect combination of simple analysis, secondary research, and expert panel studies. Secondary research sources reminiscent of annual reviews, trade 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 route to glean 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 abet to validate and reinforce secondary analysis findings and likewise aid to augment the analysis crew's odds 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 trade enterprise, Tata Consultancy capabilities Ltd., Datawatch, Drillinginfo, Hitachi Vantara trade 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 portion of trade 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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About ReportbuyerReportbuyer is a number one industry intelligence respond that gives perfect market research reviews from properly publishers

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

Cision View regular 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 trade Analysis SAS9: Regression and Model

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

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SAS Statistical trade Analysis SAS9: Regression and Model

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SAS(R) Visual Analytics and SAS(R) Visual Statistics Integrate to abet trade Users and Data Scientists work in Tandem | killexams.com true questions and Pass4sure dumps

DALLAS, TX--(Marketwired - April 27, 2015) - SAS GLOBAL FORUM - current releases from SAS ease the route for trade users and data scientists to collaborate. current integration unites SAS' well-liked 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 trade 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 decipher difficult problems faster for better trade decisions. Data scientists can soundless expend SAS Visual Analytics to visualize and disseminate information across an enterprise.

Both products expend SAS In-Memory Analytics processing to dramatically abbreviate time to trade insight, regardless of data size. Multiple users can concurrently resolve involved data on Hadoop clusters, relational database systems or SAS servers.

"The current releases reflect the converging needs of trade professionals and data scientists, as both sets of users quest to boost their productivity," said Wayne Thompson, SAS Chief Data Scientist. "Some trade analysts want to tide beyond descriptive analytics and enact more with predictive analytics. SAS provides an easy route to determine 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 distribute dynamic internal reports. SAS helps QVC:

  • Explore data in a self-service manner, even by  non-IT experts.
  • Optimize trade 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 pecuniary Planning Manager at QVC Italy. "Today, they can easily and precisely apply advanced analytics to forecast the tastes and preferences of their customers. And importantly, they can then route 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 easy to understand through custom visualizations. Users can spot otherwise hidden insights. And SAS software suggests workable 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 trade and IT users of SAS software and solutions.

    For other perspectives on how analytics can bear better trade insights, gratify visit the SAS Insights web page.

    About SAS

    SAS is the leader in trade analytics software and services, and the largest independent vendor in the trade intelligence market. Through innovative solutions, SAS helps customers at more than 75,000 sites improve 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 perfect 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. perfect rights reserved.

    The following files are available for download:

    Image Available: http://www.marketwire.com/library/MwGo/2015/4/26/11G039585/Images/G039585IM1-727888352872.jpgImage Available: http://www.marketwire.com/library/MwGo/2015/4/26/11G039585/Images/G039585IM2-841526273290.jpg


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

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

    In the 1970s, for example, mail order companies would expend substantial data and algorithmic analytic models to determine which customers they should route 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 abet businesses dote those mail-order companies develop the required analytic tools.

    “When they started 40 years ago, they needed to educate customers on the exigency to expend 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 belief of being aided by computers
or even outsourcing their decisions to algorithms. This has now completely changed. They don’t exigency to educate the market anymore. The market is hungry and in desperate exigency of AI and substantial data solutions.”

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

    How exactly enact firms dote 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 abet manage the data to provide optimal data quality. At the discovery facet 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 province scholarship and software tools to abet companies determine what data they should live collecting (and what they can filter out) in order to achieve their goals.

    The discovery facet 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 dote SAS fill stepped in by integrating what Khashouf refers to as “explainable AI” models into the mix.

    Here’s an case of it in a real-world scenario. Let’s respect 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 venerable 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 understanding codes as to why you were rejected in order to accomplish certain that the algorithms didn’t reckon on biased reasons for those rejections.”

    Sticking with the case of the credit application, a factor dote 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 abet you to not only draw insight from that data but to interpret how the AI is learning from the data to attain those decisions,” says Khashouf.

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

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

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

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

    This growth in the substantial data analytics province can live attributed to the fact that it is being used by companies spread across sectors. In the marketing field, substantial 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 abet it formulate policies on taxation, status benefits, and so on.

    Here in the Middle East, Khashouf says that every client they’ve interacted with already has an analytics programme in region 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 likewise to abet them build their competencies, update their skill sets and abet them change their organisational culture so that they can reckon more on data-driven decision making rather than gut feeling. We’re helping them to develop their human capital.”

    With data scandals dote Cambridge Analytica soundless fresh, has SAS and other companies dote 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 work with customers to draw intelligence on the data that they fill collected. We’re not in the trade of selling the insight from that data to other providers, aka the Facebook model. There has been a current regulation that has been enacted recently – for example, the common Data Protection Regulation (GDPR) in the EU – to address data privacy.”

    Interestingly, Khashouf says that these current government regulations aren’t constricting SAS’s trade model, but instead opening up current trade 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 likewise encouraging open access to data. “The UAE government has realised the benefits of substantial 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 easy to celebrate the deeper integration of AI into the data analytics business, there are earnest ethical concerns around it. “For example, when you’re driving a car and you suddenly exigency to choose 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 originate 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 accomplish decisions for you.” Companies dote SAS that find themselves in the middle of the AI ethics debate are doing perfect that they can to mitigate the ill effects of the technology.

    “During the deployment facet they accomplish certain 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 likewise seen what happens to those companies that reckon 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 region 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 true questions and Pass4sure dumps

    Fraud detection is a challenging problem. The fact is that fraudulent transactions are rare; they depict a very little fraction of activity within an organization. The challenge is that a little percentage of activity can quickly revolve into substantial dollar losses without the prerogative tools and systems in place. Criminals are crafty. As traditional fraud schemes fail to pay off, fraudsters fill learned to change their tactics. The top-notch news is that with advances in machine learning, systems can learn, adjust and uncover emerging patterns for preventing fraud.

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

    Machine learning is perfect the rage now. Most vendors title they fill some form 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 spell we’ve been resting on their laurels. In fact, it’s quite the opposite.

    Machine learning is a captious portion of the fraud detection toolkit. Here’s what you’ll exigency to glean 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 building machine learning systems. The adage that more data equals better models is staunch when it comes to fraud detection. Practitioners exigency their machine learning platform to scale as data and complexity increase. While academic tools often work 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 separate machine learning algorithm or fashion that works. Success comes from the aptitude 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 current and novel ways, dote combining a variety of supervised and unsupervised methods in one system to live more efficacious than any separate fashion alone. 

    There is a creative aspect or “art” to machine learning for fraud detection. It’s the application of machine learning in current and novel ways, dote combining a variety of supervised and unsupervised methods in one system to live more efficacious than any separate fashion 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 hasten in true 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 define 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 lead the fraud investigation process.

    Ongoing monitoring

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

    A top-notch 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 histrionic changes influence operations and the bottom line.

    What about the repercussion on your customers?

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

    This pecuniary institution wanted to modernize its rule-based fraud detection system and strike a equilibrium between oversight and customer service. To enact this it worked with SAS to implement a machine learning-based fraud detection solution that takes odds 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 pecuniary 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 aptitude 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 constituent 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 captious 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 ersatz intelligence and machine learning in the fight against pecuniary crimes. In this ISMG report, David Stewart, Director of Pre-Sales for the Global Security Intelligence practice at SAS, offers tips to abet divide fact from market hype when reviewing current 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 current and unseen data. The machine learns and adapts as outcomes and current 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 duty or instruction set that can forecast whether fraud is present by applying it to current examples. Common supervised machine learning methods include 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 duty that describes the structure of the data. This route the model flags anything that doesn’t fit the model as an anomaly. To train an unsupervised model, you simply present it data and the model attempts to infer a duty or instruction set that describes the underlying structure and dimensions of the data. This duty or instruction set can then live applied to current and unseen data.

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

    And onto ersatz intelligence

    We’ve approach a long route from statistical analysis to machine learning, but the momentum of machine learning and ersatz 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 trade leaders felt about AI’s potential, how they expend it today and contrivance to expend it in the future, and what challenges they face.

    New enhancements. Powerful capabilities. Innovative results.



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