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Finding fraud in large, diverse data sets
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Finding fraud in large, diverse data sets

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Fraud Detection Presentation from HP Discover Frankfurt, December 2012

Fraud Detection Presentation from HP Discover Frankfurt, December 2012

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  • 5% of gross business and government returns and operating expenses are lost to fraud This works out to be around $2.9 Trillion dollars – this study was global, done in 2009 by the Association of Certified Fraud ExaminersGlobally, fraudulent use of payment cards (including general purpose and private label credit cards, debit cards and prepaid payment cards) generated $7.6 billion in losses in 2010,up 10.2 percent from the previous year. The United States sustained a disproportionate share of those losses; while the U.S. registered 27 percent of worldwide payment card business in 2010, it reported nearly half (47 percent) of all losses, or $3.56 billion.But again, it’s not just a commercial industry problem, for example, in the US - As much as $26 billion could still be refunded to identity thieves in the next five years if the IRS does not do more to control the problem, the Treasury Inspector General estimated.KwekuAdeboli with Switzerland’s UBS bank was accused of stealing $2.03 billion through false accounting practices [but these large gross errors like Bernie Madoff are actually a drop in the bucket compared to overall fraud or the fact that the center of gravity or median value of a fraudulent scheme is $5K – so it’s really something that is perpetrated by average criminals and has detrimental impact on average individuals [NEXT SLIDE]
  • Most of the time fraudulent charges are passed on to us …Directly when someone’s identity is stolen and they have to jump through several costly time consuming hurdles to reestablish their identitiesAnd indirectly when the cost of our medical care, debt taken on because correct and expected levels of tax return revenues are not available and more has to be borrowed by governments – remember the size of the black market economy and tax evasion in Greece is one of the highest in the world, but even on the cost of goods either directly in their pricing or interest rates on our credit cards go up.
  • Traditional BI environments are often designed with proprietary technology that is expensive. They were not designed to provide the speed and agility required to integrate the variety of data types we are dealing with today, analyze data in real-time, and generate the intelligence required by the fast-paced demands of today’s changing business environment. Where near real time, iterative, automated and low cost data analytics are not required, these legacy platforms will likely meet business requirements. The question is … is that the world that you live in?
  • A vision is great, but technology is makes our vision a reality. Vertica’s innovative technology makes the difference because it was designed from the very first line of code for the new demands of near real time data analytics. RelationalSoftware platform to store, manage, and analyze informationNative COLUMNAR architecture is core, and enables better joins and fundamentally faster analyticsLoad and query simultaneously, dramatically increasing the velocityMPP- Highly scalable, elastic and fully PARALLEL, with commodity hardware and 90% less storage due to compression technology SQL & NoSQLanalytics capabilities Simpleinstallation & use with automaticsetup and tuning
  • Key Thoughts:Don’t dig in deep – just highlight that the core foundation of the product is MPP, Optimized HA, and a True Column StoreTMThe key idea is that everything ties into the innovation at the core of the product – every module, feature, function, connector, etc. The extensibility of the platform is ultimately due to the innovation at the coreMention in passing everyone will say “hey – we have a columnar db too”. But we are the only True Column StoreHighlight that our modular approach allows us to innovate more frequently than most- hence a new major release every 6-9 months.
  • Key Thoughts:Telstra, Vodaphone, Optus,TimeWarner, Shaw, Bell Mobility are just some of the newer customers to begin using VerticaRefer back to comcast and Trane use cases (network devices and sensors – capture a vast amount of the market with those 2 use cases)CHALLENGESCustomer and product churnCompetitive market with mix of high and low margin productsVolume of data eclipses capabilities of legacy infrastructuresSOLUTIONSAnalyze portfolio for insight into churn and satisfaction Prioritize infrastructure investments in high value, high margin infrastructure and applications via empirical dataStore, access, and monetize via new analytic paradigmBENEFITSHigher customer satisfaction, retention, and profitability Alleviate high cost low value products and servicesDynamically manage and scale portfolio without sacrificing details of any customer, transaction, or product
  • Key Thoughts:Telstra, Vodaphone, Optus,TimeWarner, Shaw, Bell Mobility are just some of the newer customers to begin using VerticaRefer back to comcast and Trane use cases (network devices and sensors – capture a vast amount of the market with those 2 use cases)CHALLENGESCustomer and product churnCompetitive market with mix of high and low margin productsVolume of data eclipses capabilities of legacy infrastructuresSOLUTIONSAnalyze portfolio for insight into churn and satisfaction Prioritize infrastructure investments in high value, high margin infrastructure and applications via empirical dataStore, access, and monetize via new analytic paradigmBENEFITSHigher customer satisfaction, retention, and profitability Alleviate high cost low value products and servicesDynamically manage and scale portfolio without sacrificing details of any customer, transaction, or product
  • Key Thoughts:Telstra, Vodaphone, Optus,TimeWarner, Shaw, Bell Mobility are just some of the newer customers to begin using VerticaRefer back to comcast and Trane use cases (network devices and sensors – capture a vast amount of the market with those 2 use cases)CHALLENGESCustomer and product churnCompetitive market with mix of high and low margin productsVolume of data eclipses capabilities of legacy infrastructuresSOLUTIONSAnalyze portfolio for insight into churn and satisfaction Prioritize infrastructure investments in high value, high margin infrastructure and applications via empirical dataStore, access, and monetize via new analytic paradigmBENEFITSHigher customer satisfaction, retention, and profitability Alleviate high cost low value products and servicesDynamically manage and scale portfolio without sacrificing details of any customer, transaction, or product
  • Key Thoughts:Telstra, Vodaphone, Optus,TimeWarner, Shaw, Bell Mobility are just some of the newer customers to begin using VerticaRefer back to comcast and Trane use cases (network devices and sensors – capture a vast amount of the market with those 2 use cases)CHALLENGESCustomer and product churnCompetitive market with mix of high and low margin productsVolume of data eclipses capabilities of legacy infrastructuresSOLUTIONSAnalyze portfolio for insight into churn and satisfaction Prioritize infrastructure investments in high value, high margin infrastructure and applications via empirical dataStore, access, and monetize via new analytic paradigmBENEFITSHigher customer satisfaction, retention, and profitability Alleviate high cost low value products and servicesDynamically manage and scale portfolio without sacrificing details of any customer, transaction, or product
  • Be proactive versus reactiveAnalysis of transaction data can provide a retroactive means of detecting fraud, but real-time use of transaction data can proactively step in to stop fraud.

Finding fraud in large, diverse data sets Finding fraud in large, diverse data sets Presentation Transcript

  • Chris SellandVP MarketingHP Vertica© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • Finding fraud in large,diverse data setsusing big data analytics for fraud detection and preventionChris Selland, VP Marketing, HP VerticaDecemter, 2012© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • “If you need a machine and don’tbuy it, you will ultimately find outthat you have paid for it and don’thave it.”Henry Ford© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • Fraud is a parasite to business and government5% of gross business and government returns and operating expenses are lost to fraudApproaching $3.5B annually in US forcredit cardsMedicare and Medicaid fraud over10X credit card fraudThe US Treasury expects $65B in taxfraud over the next 5 yearsOne banker at UBS Bank Switzerlandsinglehandedly stole $2BBut the average fraud attack is $5K4 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • … but the toll on individuals is what really mattersEvery minute, 19 citizen’s identities are stolenThe average victim spends 500 hoursand $3,000 undoing the damageAnd we as individual … Taxpayers, Consumers, and Business owners … pay the rest of the bill5 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • Big Data Application: Fraud Analysis The Problem: U.S. Government needed to detect patterns of fraud in federal health care programs The Solution: • Uses government supercomputer to detect fraud in near-real time on aggregated databases • Multiple petabytes of claims data (Medicare, Medicaid, DoD, Veterans Affairs, etc.) • Finds patterns to generate rules and identify anomalies • Boosted recovery of claims from $1 billion/year to $50 billion6 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • Detecting fraud is a game of pattern recognition …A simple foundation of … Applied to financial … with modern technologystatistical formulas … and workflow process we can … transactions, e.g. Capture transaction streams • Credit card bills Build historical track records • Supplier invoices and ID anomalies • Financial transactions Run analytics based on those • Call records 17th century formulasBaysian … • Claims records … and detect fraudulent • Approval chains activitiesB1Xi1 + B2Xi2 … BnXin + ei = yi • …7 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • But combating fraud is a game of cat and mouseThe patterns keep changingHackers are thwarted only to come backthrough a different security holeNovell scams are being thought up forloopholes and exceptions in business workflowsAnd the playing field keeps growingVolume and velocity of transactionsDigitization of workflow records and approvalsSource and type of transactions8 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • With Vertica, you get a better mouse trapYour proprietary systems can’t keep upExpensive to scale for today’s “Big Data” real-time transaction streamsDifficult to modify legacy analytic code andarchitectures to keep up with changing patternsWith Vertica you stay a step aheadRapidly create real-time high-speed transactionalrecord datamarts on inexpensive platformsOpen analytics platform for deep predictivefraudulent pattern modeling9 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • Designed for answers from the very first line of code,Vertica technology makes the difference Columnar storage Achieve best data query performance with unique Vertica column store and execution Clustering Add resources on the fly with linear scaling on the grid, commodity hardware Compression Store more data, provide more views, 90% less storage required Continuous Query and load 24x7 with zero administration performance Database design Advanced analytics Automated performance tuning Time-series, geospatial, click-stream and an SDK for more10 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • Vertica architecture – every element purpose builtfor pattern recognition in Big Data scenarios Industry Specific Fraud Detection & Prevention Use Case Scenarios SAS Hyperion MicroStrategy User- Business Cognos (Oracle) R-Function Defined Objects (IBM) Library Analytics (SAP) Next Generation administration, cluster architecture, Standard interfaces True column store – RDBMS w/ columnar compression, concurrent load/query Real time massively parallel processing, performance and high availability11 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • Communication Service Provider fraudEstimated impact of $40 Billionp.a. worldwideService Providers continue to seeincreases in volume & varietyContinuous improvementnecessary to alleviate –constantly moving targets12 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • Banking services fraud detection use case scenarioIdentifying credit card fraud (skimming)Historical reference dataset• Credit card skimming record for merchants• Merchant characteristics (size of store, popularity)• Credit record for card holderReal-time transactional data• Credit card transaction and card statusResult: Merchant probability of skimmingImplemented for a large American bank on Vertica14 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • Identifying healthcare public/private insurancereimbursement improper payments use case scenarioHistorical reference dataset• Reimbursement record for providers• Reimbursement record for patient• National, regional, local statistical analysis of treatment associated with reimbursement• Provider characteristics (size of provider, popularity, etc.)Real-time transactional data• Credit card transaction and card statusResult: Provider’s probability of improper paymentsImplemented for a large American insurance carrier on Vertica15 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • Staying ahead of credit card skimmingAbility to incorporate the latest algorithmswithout proprietary code, leveraging “Big Data”and social media• Front-end outlier detection in multivariate data streams• Neural networks• Social network analysisImplemented analytics in TWENTY lines of codeDifferent use cases, data sources and industrieshandled with the same pattern recognition scenario16 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • ConclusionGet in front of the games …We can help you combat fraud by enabling you to …• Incorporation of all necessary data sets• Ability to incorporate the latest algorithms without proprietary code, leveraging “Big Data” and social media• Be proactive versus reactiveWhere to find more information• bit.ly/VerticaFraud• www.vertica.com• my.vertica.com/evaluate/• cselland@vertica.com• +1.617.386452317 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • Download Now Get the Mobile App Download content from this session with the free Mobile App at: m.hp.com/events18 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  • Thank you© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.