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SPS-201 - IBM SPSS MBPDA - IBM SPSS Modeler Business - BrainDump Information

Vendor Name : IBM
Exam Code : SPS-201
Exam Name : IBM SPSS MBPDA - IBM SPSS Modeler Business
Questions and Answers : 25 Q & A
Updated On : November 16, 2018
PDF Download Mirror : SPS-201 Braindumps
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SPS-201 exam Dumps Source : IBM SPSS MBPDA - IBM SPSS Modeler Business

Test Code : SPS-201
Test Name : IBM SPSS MBPDA - IBM SPSS Modeler Business
Vendor Name : IBM
Q&A : 25 Real Questions

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IBM IBM SPSS MBPDA -

Monster Merger: IBM Buys SPSS For Approx. $1.2 Billion In cash Deal | killexams.com Real Questions and Pass4sure dumps

IBM is purchasing analytics application and solutions issuer SPSS in an all money transaction at a price of $50/share – a 42 p.c premium to Monday’s closing price of $35.09 on Nasdaq – leading to a complete cash consideration within the merger of about $1.2 billion. The acquisition is field to SPSS shareholder approval, regulatory clearances and other closing circumstances, and is anticipated to close later within the 2nd half of 2009.

big Blue noted the acquisition of the publicly-held Chicago enterprise become anticipated to enhance its tips-agenda initiative, which helps agencies take counsel and switch it right into a strategic asset. IBM shares fell 67 cent to $116.ninety six in pre-market trading, while SPSS shares jumped 41 % to $forty nine.59.

A message from SPSS Chairman, CEO & President Jack Noonan:

We and IBM view this as a highly-complementary movement from each a expertise and a market place viewpoint.

We’re excited about this settlement and the tremendous possibilities that SPSS and IBM can achieve collectively in carrying on with to steer an trade that we helped shape. The depth and breadth of IBM’s supplies, its customer and market attain can only boost our ability to handle the growing market for Predictive Analytics.

In becoming a member of with IBM, we will enhance Predictive Analytics as a aggressive potential for agencies and organizations global. We see this as a transformative event to be able to accelerate the adoption of Predictive Analytics.

Between now and the acquisition shut, we will continue to guide our purchasers and partners in commonplace vogue. existing contracts will remain in impact, account relationships and support infrastructure will remain unchanged.

Predictive analytics utility captures and analyzes data about people’s attributes, attitudes and behaviors to benefit a full figuring out of expected future behaviors, so corporations can make smarter selections for stronger company results.

IBM says it is going to proceed to guide and raise SPSS applied sciences whereas allowing clients to take potential of its personal product portfolio. SPSS will develop into a part of the tips management division in the software neighborhood enterprise unit, led by means of Ambuj Goyal, established manager, IBM information administration.

update – In other news: IBM also obtained Ounce Labs, a provider of enterprise supply code security testing and intends to combine it into its Rational application business. fiscal phrases have been now not disclosed.


IBM is rushing up SPSS with Apache Spark | killexams.com Real Questions and Pass4sure dumps

IBM these days introduced that it is enhancing a number of of its existing application products with the Apache Spark open-source data processing engine, including the SPSS predictive analytics utility.

IBM bought SPSS for $1.2 billion in 2009. SPSS itself all started at Stanford college in 1968 and is known in facts classes at universities.

IBM is especially incorporating Spark into SPSS Modeler and SPSS Analytic Server, a spokesman informed VentureBeat in an electronic mail.

Spark is also being applied to IBM’s BigInsights, Streams, and DataWorks utility, in response to an announcement.

The Sparkification of IBM comes after big Blue announced an important commitment to the open-source software in June. at the time, IBM introduced Spark as a carrier on its Bluemix cloud; that carrier is now commonly available.

IBM has made greater than 60 contributions to Spark given that the June announcement, in response to the remark.


a look at the IBM SPSS Modeler and IBM SPSS information analytics equipment | killexams.com Real Questions and Pass4sure dumps

IBM's SPSS predictive analytics equipment consist of IBM SPSS Modeler and IBM SPSS statistics. SPSS Modeler gives...

facts mining and text evaluation application, while SPSS statistics is an built-in family of items. both tools permit users to construct predictive models and execute different analytics initiatives.

The IBM SPSS Modeler aims clients who've little or no programming skills. users are offered with a drag-and-drop person interface, enabling them to build predictive fashions and operate different statistics analytics. Modeler can apply different procedures and algorithms to support the consumer find information hidden in the facts. The device can additionally aid in integrating and consolidating all types of data sets from dispersed facts sources throughout the firm.

The IBM SPSS statistics suite is an built-in set of products geared towards extra expert statistics analysts. SPSS data addresses the comprehensive analytical process, from planning to information assortment, analysis, reporting and deployment.

IBM SPSS Modeler facets

edition 18 gives right here points:

  • greater than 30 base desktop gaining knowledge of algorithms.
  • Extensions that supply endured improvements to be used with open source items, corresponding to R and Python.
  • better assist for a number of multithreaded analytical algorithms, together with Random trees, Tree-AS, Generalized Linear Engine, Linear-AS, Linear assist Vector computer and Two-Step-AS clustering.
  • The potential to run lots of Python and Spark machine studying, in addition to other Python analytics libraries natively in Modeler with out requiring the use of the Analytic Server, as was required within the outdated version.
  • SPSS Modeler bundles are deployed on premises, and SPSS Modeler Gold is available as a cloud offering. The customer entrance conclusion of SPSS Modeler runs below home windows and macOS, whereas the server part runs on Unix, Linux and windows.

    IBM SPSS Modeler provides the following versions:

  • SPSS Modeler very own: A single-person laptop product.
  • SPSS Modeler expert: A computing device product that works with IBM SPSS Analytic Server, presenting enhanced scalability and performance and enabling purposes to be used across a company.
  • SPSS Modeler top rate: This edition comprises superior algorithms and capabilities, reminiscent of text analytics, entity analytics and social network evaluation, that raise model accuracy with unstructured information.
  • SPSS Modeler Gold: This edition provides analytical resolution management, collaboration and deployment capabilities. SPSS Modeler Gold is additionally obtainable as a cloud offering.
  • IBM SPSS facts facets

    SPSS facts version 24 comprises here new features:

  • The potential to entry greater than one hundred extensions, enabling clients to take advantage of free libraries written in R, Python and SPSS syntax.
  • The IBM SPSS Extension Hub to browse, down load, replace, get rid of and generally manage extensions.
  • a massive improve to the customized Dialog Builder, enabling users to more simply build and installation their own extensions. Enhancements consist of new controls and new homes for existing controls and a few other advancements to the person interface.
  • advancements that enable users to greater readily and directly import and export records into SPPS records.
  • advancements to the custom Tables module, including new statistical performance and consumer-requested points.
  • IBM SPSS information offers here three editions (each and every with further modules):

  • SPSS information typical tools give advanced statistical tactics that aid linear and nonlinear statistical fashions, as well as predictive simulation modeling, which debts for uncertain inputs, geospatial analytics and customised tables.
  • SPSS records expert equipment aid facts education, lacking values and statistics validity, determination trees, and forecasting.
  • SPSS information top rate provides superior analytical innovations, including structural equation modeling, in-depth sampling evaluation and trying out. This bundle also includes procedures that goal direct advertising and excessive-end charts and graphs.
  • Pricing for the SPSS Modeler and SPSS information predictive analytics equipment vary counting on the bundle options, the number of users and the license period. SPSS information is now purchasable as a subscription choice or a perpetual license. IBM presents free trials of each IBM SPSS Modeler and IBM SPSS facts.

    subsequent Steps

    Why the term unstructured records is a misnomer

    How large data is changing statistics modeling recommendations

    large statistics programs pose new challenges to statistics governance

    linked supplies View more


    Killexams.com SPS-201 Dumps and Real Questions

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    SPS-201 exam Dumps Source : IBM SPSS MBPDA - IBM SPSS Modeler Business

    Test Code : SPS-201
    Test Name : IBM SPSS MBPDA - IBM SPSS Modeler Business
    Vendor Name : IBM
    Q&A : 25 Real Questions

    Get those SPS-201 Q&A, put together and chillout!
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    Observed these maximum SPS-201 Questions in real test that I passed.
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    Nice to hear that dumps of SPS-201 exam are available.
    Wow..OMG, I simply handed my SPS-201 cert with 97 percent score i was uncertain on how top the take a look at materialbecame. I practiced with your on line test simulator, and studied the material and after taking the test i used to be glad i found you guys at the internet, YAHOO!! thanks Very plenty! Philippines


    SPS-201 certification examination instruction got to be this smooth.
    Thanks to killexams.com this site gave me the tools and confidence I needed to crack the SPS-201. The site has valuable information that will help you to achieve success in SPS-201 guide. In turn I came to know about the SPS-201 preparation software. This software is outlining each topic and put question in random order just like the test. You can get score also that will help you to assess yourself on different parameters. Wonderful


    I got SPS-201 licensed in 2 days coaching.
    This is absolutely the achievement of killexams.com, now not mine. Very person pleasant SPS-201 exam simulator and authentic SPS-201 QAs.


    Do not spill big amount at SPS-201 courses, checkout those questions.
    Passed the SPS-201 examination with 99% marks. Excellent! Considering best 15 days coaching time. All credit score goes to the Question & Answer by using killexams. Its fantastic material made preparation so easy that I ought to even recognize the hard subjects cozy. Thanks loads, killexams.Com for supplying us such an clean and powerful have a look at guide. Hope your crew hold on creating more of such publications for other IT certification tests.


    What is needed to study for SPS-201 examination?
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    Comparing the leading big data analytics software options | killexams.com real questions and Pass4sure dumps

    There are many vendors selling products classified as big data analytics software. However, it's challenging to...

    differentiate these products based on functionality alone, as many of the tools share similar features and capabilities. Additionally, some of the tools exhibit extremely subtle differences.

    That being said, your key differentiating factors will likely focus on balancing ease of use, algorithmic sophistication and price in relation to your organization's capability and level of maturity in analytics.

    In this article, we examine products from nine big data analytics software vendors: Alteryx Inc., IBM, KNIME AG, Microsoft, Oracle, RapidMiner Inc., SAP, SAS Institute Inc. and Teradata Corp. Some of these vendors provide more than one tool. See the "Leading vendors of big data analytics software" sidebar below for more details about their specific product offerings.

    These vendors represent different facets of the big data analytics market. Let's compare and contrast the ways that these products meet the business needs of user organizations.

    Analyst expertise and skills 

    Some data analytics tools are targeted to novice users, some are targeted to expert data analysts and some are engineered to appeal to both types of users.

    Products such as IBM SPSS Modeler, RapidMiner's tools, Oracle Advanced Analytics and the Automated Analytics version of SAP BusinessObjects Predictive Analytics are generally designed to enable users with a limited background in statistics or data analysis to analyze data, develop analytical models and design analytics workflows with little or no coding.

    While each vendor wraps its core analytics components with an intuitive user interface to guide the analyst's progress in data preparation, analysis, and then model design and validation, the approach taken may differ, especially when comparing a stand-alone product, such as RapidMiner, with one that's a component of a larger suite, such as the Oracle product.

    Tools such as IBM SPSS Statistics, KNIME Analytics Platform, the Expert Analytics module of SAP BusinessObjects Predictive Analytics, Microsoft R and the Teradata Aster Analytics platform provide the more sophisticated functionality that expert users expect. Oracle R Advanced Analytics for Hadoop (ORAAH), one of the components in the Oracle Big Data Software Connectors suite, provides an R interface for manipulating Hadoop Distributed File System data and writing mapper and reducer functions in R. This flexibility may be appealing to more advanced data scientists.

    Alteryx and SAS Enterprise Miner offer functionality adapted to the user's level of expertise, and essentially fall into both categories. Alteryx has added improvements to data profiling to help data scientists better understand their data sources. Overall, SAS Enterprise Miner and IBM's SPSS tools stand out when it comes to supporting more advanced analytical techniques and model scoring, as well as a broader array of analysis functions, including neural networks, association analysis and visualization capabilities.

    Analytical diversity

    Depending on the use case and application, your organization's users will be required to support different types of analytics capabilities that will use specific types of modeling, such as regression, clustering, segmentation, behavior modeling and decision trees.

    While this has resulted in broad support for the various forms of analytical modeling at a high level, some vendors have invested decades of work into tweaking different versions of their algorithms and adding more sophisticated functionality. It's important to understand which models are most relevant to your business problems and to evaluate the products in terms of how they best serve your users' business needs.

    It's important to understand which models are most relevant to your business problems and to evaluate the products in terms of how they best serve your users' business needs.

    The more mature and higher-end -- and, accordingly, higher-priced -- tools will exhibit the greatest analytical breadth. Oracle Data Miner includes an array of well-known machine learning approaches to support clustering, predictive mining and text mining. Both editions of IBM's SPSS product provide a diverse set of analytical techniques and models. And SAS Enterprise Miner supports many algorithms and techniques, including decision trees, time series, neural networks, linear and logistic regression, sequence and web path analysis, market basket analysis, and link analysis.

    The newer generation -- and, in some cases, lower-priced -- products support different models, but perhaps with a narrower range of algorithmic sophistication.

    The model inventory in Alteryx Analytics Gallery includes such capabilities as regression analysis, decision trees, association rule analysis, classification and time series analysis. KNIME includes methods for text mining, image mining and time series analysis, and also integrates machine learning algorithms from other open source projects, such as Weka and JFreeChart.

    Another aspect of analytical diversity is integration with programming languages and statistical tools, such as R, for incorporating existing libraries, as well as user-defined functionality. In fact, integration with R could be considered an increasingly critical differentiator.

    Alteryx Designer, Microsoft R, SAS Enterprise Miner, Teradata Aster Analytics, Oracle's ORAAH and KNIME's Analytics Platform all interface and support integration with R. Several of the vendors, including IBM, Oracle, Microsoft, RapidMiner and SAP, provide a growing library of extensions to R and Python, enabling users to take advantage of free libraries.

    Scope of the data to be analyzed

    There are multiple facets of the scope of the data to be analyzed, including the issue of structured vs. unstructured information, as well as access to conventional on-premises databases and data warehouses, cloud-based data sources, and data managed in big data platforms, such as Hadoop.

    However, there are varying degrees of support for data managed within less-conventional data lakes -- either managed within Hadoop or in another NoSQL data management system intended to provide horizontal scaling. The factors for distinguishing among the products must be based on your organization's specific requirements for accessing and processing data volumes and data variety.

    In recognition of the growing diversity of input sources and the variety of underlying systems used to house those data sets, another set of emerging features that is being adopted by these vendors involves data accessibility. IBM, RapidMiner, Alteryx, Oracle and Microsoft have all improved their tools' data import, export and connectivity capabilities. These enhancements should enable users to access a more comprehensive list of data sources while simplifying and speeding up the process of loading data into the products.

    Support for scalability and high performance

    The need for scalable performance is driven by your organization's data volumes and appetite for analysis. Smaller organizations with less data may be able to tolerate products that don't have performance characteristics that scale with the available resources, such as the entry-level versions of the lower-end tools, including RapidMiner, KNIME, Microsoft R Open and Alteryx Designer, which can run on desktop systems and don't require additional server components.

    Larger organizations are more likely to have a greater inventory of data sets to analyze, as well as broader communities of users. This introduces two additional requirements -- high performance and facilitation of collaboration. The adaptability of a product to high-performance architectures is a good indication of scalability, and most of the products can be adapted to the parallelism of Hadoop or employ some other means of achieving faster computation.

    All of the products do have some support for Hadoop, including IBM SPSS Modeler and SPSS Statistics; RapidMiner's commercial component Radoop, which connects the Studio front end and Server analysis engine to data stored in Hadoop; Oracle's Big Data Discovery and ORAAH tools; and KNIME's Big Data Extensions and Cluster Execution add-ins.

    IBM SPSS now also provides enhanced support for a number of multithreaded analytical algorithms that may speed performance. Teradata Aster Analytics addresses high-performance requirements through its Massively Parallel Processing architecture. SAP's Expert Analytics version of SAP BusinessObjects Predictive Analytics can execute in-memory data mining for handling large-volume data analysis efficiently. Microsoft R Server leverages its ScaleR module, a comprehensive library of big data analytics algorithms that support parallelization. Scoring algorithms implemented using SAS Enterprise Miner can be deployed and executed within a Hadoop environment.

    In addition, integration with Apache Spark appears to be of growing importance. SPSS, KNIME, Oracle, RapidMiner and SAP all provide access to Apache Spark libraries to support analytics applications that need to scale with exploding data volumes. This enables developed applications to take advantage of a high-performance cluster platform to distribute the workflow across the cluster.

    Collaboration

    As noted, the larger the organization, the more likely there will be a need to share analyses, models and applications across different groups and among many analysts. Organizations that have many analysts distributed across the enterprise may look for increased means to share models and collaborate regarding the interpretation of results.

    IBM's SPSS Modeler Gold edition provides collaboration capabilities, and RapidMiner's Server product provides support for sharing and collaboration. Alteryx Analytics Gallery provides a mechanism for sharing sophisticated analytics applications in the cloud with members of an extended organization. KNIME offers commercial extensions to support team collaboration, as well as extensions supporting operational collaboration, such as remote-scheduled execution, report generation, shared data space and a workflow repository. SAS Enterprise Miner's client-server architecture enables business users and data analysts to work collaboratively by sharing models and other work products.

    Alteryx, KNIME and Teradata Aster have added capabilities to help manage analytical workflows. Also, some of the vendors have started to look at ways to enable their tools to integrate with others that may have complementary functional sweet spots. For example, Teradata Aster now has an extension to integrate with KNIME that enables users to leverage the KNIME workflow editor and incorporate Aster Analytics functions into those workflows.

    Vendor size and product integration

    Vendors can be compared in terms of their size. One might compare and contrast what could be referred to as the mega-vendors, whose big data analytics tools are just one product among a massive portfolio of tools. If you work for a larger organization that typically negotiates site-wide, enterprise licenses for the full suite of a vendor's tools from a mega-vendor such as IBM, SAS, SAP or Oracle may be a reasonable choice.

    The large vendors sell big data analytics tools that are a part of a much larger tool ecosystem. Presumably, the products from a mega-vendor will be at least somewhat integrated and intended to work together. In addition, some people feel more comfortable with bigger vendors, with an expectation of stability and consistent customer service. On the other hand, you may only be able to acquire these big data analytics tools as part of a much larger software licensing arrangement.

    Smaller vendors, such as KNIME, Alteryx and RapidMiner, have revenues that are generally based on licensing and support for a small number of big data analytics products. A smaller vendor may provide closer contact with their product management and innovation teams, and you may be able to influence the direction of the product roadmap or enhanced functionality.

    A smaller vendor might also be more flexible in terms of price and the features included in the licensing arrangement. You must realize, however, that working with a smaller vendor does present some risk in terms of stability, the resources available for support and the possibility that the company may be acquired, which can impact the customer relationship.

    The larger vendors are clearly responsive to user needs for integration with other systems, although that often centers on other products within each vendor's inventory. For example, SAP Predictive Analytics has improved integration with SAP HANA and BusinessObjects Cloud. SAS Enterprise Miner has added nodes to execute code in a SAS open, cloud-ready, in-memory Viya environment. Microsoft offers SQL Server R Services, an R installation that runs alongside SQL Server and enables users to integrate Microsoft R Server data with SQL Server and Microsoft's other business intelligence tools.

    Budget for licensing and maintenance

    Almost all of the vendors sell different versions or editions of their products, with a range of costs for acquisition and total cost of operation. IBM, Oracle, RapidMiner, Teradata and Microsoft sell editions at different tiers, with the license cost proportional to the features, capabilities and freedom from limitations in terms of the volumes of data to be analyzed or the number of processing nodes the product can use.

    KNIME and RapidMiner provide free and open source versions of their products, either charging for support services or for editions supporting enterprise-class applications. KNIME, RapidMiner and Alteryx have relatively low licensing costs for a smaller number of users. If you're considering SAS or SAP, you must contact them for pricing alternatives.

    The marketplace for big data analytics software can be a confusing place, but hopefully this article has helped you understand the benefits big data analytics software can provide your organization, and assisted you in differentiating between the specific tools examined here.


    Splunk: Paving The Way For IBM | killexams.com real questions and Pass4sure dumps

    No result found, try new keyword!With increased competition, Splunk will have to up marketing even more to differentiate its product. Valuation (Source: Own Calculations) (Source: Own Calculations) The key assumptions underlying the ...

    Using Big Data to Fight Fraud | killexams.com real questions and Pass4sure dumps

    Posted April 18, 2013 By Drew Robb     Feedback

    While Big Data can be useful in fighting fraud, traditional business intelligence tools aren't much help.

    If it was easy to detect fraud, there wouldn’t be much of it around. But these days, the sheer volume of information being generated makes it more difficult than ever to detect.

    “Organizations today typically have a fragmented approach to dealing with security and fraud risks, which leaves them vulnerable to even more attacks as criminals are quick to find and exploit any points of weakness,” said Ellen Joyner-Roberson, global marketing principal at SAS Security Intelligence Solutions. “As a result, security risks and financial crimes are increasing dramatically due to a weak global economy, growth in organized crime and the increasing sophistication of fraudulent schemes.”

    Not Your Usual Analytics

    Unfortunately, many current analytics systems do not support robust analytical modeling, making it difficult to process and analyze information fast enough to make a difference. Different departments often have disparate data sources and processes, so it’s tough to spot suspicious activity across the enterprise.

    In addition, there is a constant struggle to balance tougher anti-fraud efforts with a good customer experience. If vendors stop more transactions that are suspicious in an attempt to reduce fraud losses, they may anger customers trying to complete legitimate transactions.

    Banking, insurance, government, health care and retail are obvious targets for con artists. The banking industry, for example, has a big problem with identity theft and account takeover. There, it is critical that decisions are made in real time at the point of sale, which requires a monitoring and analytics approach that can instantly model for behavior changes and stop that transaction without getting it wrong too many times.

    Due to such demands, the consensus is that traditional business intelligence (BI) isn’t sophisticated enough. It needs to be backed up by domain-embedded rules and adaptive analytics that can be tailored to different types of fraud or financial crime. Context also plays a vital part, and integrating contextual information from mobile apps and other channels can heighten success. And geospatial and velocity data capture at transaction origination should be backed up by decision making in real time.

    “To fight fraud before it causes financial losses, companies are using real-time interdiction (definition: to forbid or debar) capabilities wherever they can, and with raw response times under 200 milliseconds,” said Joyner-Roberson.

    Her company developed SAS Security Intelligence to address this market. It comprises various SAS products to address fraud and improper payments, regulatory compliance and security. This includes SAS Fraud Management, SAS Network Analytics, SAS Financial Crimes Suite and SAS Fraud Framework.

    SAS Security Intelligence takes an enterprise approach to data management and consolidation, combining data integration, data quality and master data management into a unified environment that brings together cross-channel enterprise data on a single platform. It has the ability to score and decision all transactions in real-time and intercept suspect deals.

    Haystacks, Needles and Big Data

    Big Data only magnifies the problems inherent in tracking down wrongdoers. Just how do you deal with capturing, processing, and storing massive amounts of real-time data so you can learn and adapt to changing behavior patterns?

    “The needle in the haystack analogy is particularly appropriate for fraud detection due to the small amount of fraud compared to the whole of legitimate Web traffic,” said Jesse McKenna, head of Threat Research for Silver Tail Systems, RSA. “However, as fraud patterns constantly shift and evolve, so does the needle you are looking for. Today it may be a needle, tomorrow a pushpin or a nail.”  

    The challenge is that you don’t know exactly what you are looking for other than it will be different than the rest of the pile. While Big Data means you may be able to derive more insights, finding the meaningful data and building detection algorithms that aren't brittle and won't quickly become irrelevant takes a deep understanding of fraud.

    That’s where behavioral analytics and adaptive detection methods become important. You need ways to isolate what is different that you may have never seen before.

    Analytics apps also have to be of value to fraud investigators, who are the ones with the experience of the real world. Machines can burn through the data mountain to locate unusual occurrences, but they are nowhere near as good as people at evaluating and understanding context. Therefore, the app has to facilitate the people or fraud units it serves, not burden them with delays or lengthy procedures.

    RSA’s Silver Tail Systems product suite (consisting of Forensics, Mitigator, and Profile Analyzer tools) provides behavioral analytics, real-time custom rules and mitigation capabilities, as well as a forensics interface for performing investigations.

    For example, by monitoring the details of every HTTP/S request that is sent to a website, it learns what normal behavior looks like for the website and can then identify criminal behavior and alert the appropriate teams for investigation. Alternately, it can communicate with other network devices to redirect or terminate the Web session in real-time.

    User-based behavioral analytics (in addition to the population-based ones) helps identify when a particular user is deviating from their normal behavior, as is the case with many fraud and account compromise scenarios.  

    “Silver Tail was created by ex-eBay and PayPal fraud professionals who lived the battle against fraud at a time when they were protecting the two most targeted brands on the Internet,” said McKenna. “Their experiences motivated them to keep up with shifting Web fraud attacks instead of playing catch-up.”

    Visual Aids

    IBM is also involved in fraud analytics via a combination of IBM SPSS Modeler, SPSS Statistics, SPSS Collaboration and Deployment Services, SPSS Data Collection, SPSS Decision Management, IBM Cognos BI, Cognos Express, Cognos Insight, Cognos Disclosure Management and Cognos Financial Statement Reporting.

    IBM believes that visualization is a vital part in making sense of analytics information, especially when it comes to facilitating the work of professionals in fraud detection.

    “As data volumes continue to go up, BI tasks can benefit greatly from the use of information visualization techniques,” said Don Campbell, CTO, IBM Business Analytics. “Good visual representations can help individuals intuitively understand complex data. Visualization can be a powerful presentation and information consumption interface. But it doesn't stand on its own.”

    Graphical representation is also a focus of 21CT, a provider of analytics for investigation and pattern detection. It has released enhanced graph search for its LYNXeon product to help spot cyber attackers and document criminal behavior. The rationale behind the product is that machine -based attacks can be caught by machines, but human-based attacks require specialized tools to help people detect them before the damage is done.

    “Health care fraud, notably Medicare, is an enormous problem, costing billions a year,” said Kyle Flaherty, vice president of Marketing at 21CT. “The challenge is not only the enormous amounts of data, but also in the disparate connections within the data. Additionally, fraud has been rampant in this arena and the malicious entities have become sophisticated.”

    While regular BI tools provide insight into one slice of the overall information, Flaherty said, they are not good enough for fraud detection, which depends on operational insight and the ability for a human to hunt through visualized data, discover links, and detect overall patterns of activity.

    “Fraud detection is a human versus human battle, and BI tools can spit out more data and graphs to help illustrate the problem,” said Flaherty. “In the end you need to be able to hop around the data and investigate.”

    Drew Robb is a freelance writer specializing in technology and engineering. Currently living in California, he is originally from Scotland, where he received a degree in geology and geography from the University of Strathclyde. He is the author of Server Disk Management in a Windows Environment (CRC Press).



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