Finding fraud in large, diverse data sets
•
1 j'aime•1,144 vues
Fraud Detection Presentation from HP Discover Frankfurt, December 2012
Recommandé
Recommandé
Contenu connexe
Tendances
Tendances (20)
Similaire à Finding fraud in large, diverse data sets
Similaire à Finding fraud in large, diverse data sets (20)
Dernier
Dernier (20)
Finding fraud in large, diverse data sets
- 1. Chris Selland VP Marketing HP Vertica © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
- 2. Finding fraud in large, diverse data sets using big data analytics for fraud detection and prevention Chris Selland, VP Marketing, HP Vertica Decemter, 2012 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
- 3. “If you need a machine and don’t buy it, you will ultimately find out that you have paid for it and don’t have it.” Henry Ford © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
- 4. Fraud is a parasite to business and government 5% of gross business and government returns and operating expenses are lost to fraud Approaching $3.5B annually in US for credit cards Medicare and Medicaid fraud over 10X credit card fraud The US Treasury expects $65B in tax fraud over the next 5 years One banker at UBS Bank Switzerland singlehandedly stole $2B But the average fraud attack is $5K 4 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
- 5. … but the toll on individuals is what really matters Every minute, 19 citizen’s identities are stolen The average victim spends 500 hours and $3,000 undoing the damage And we as individual … Taxpayers, Consumers, and Business owners … pay the rest of the bill 5 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
- 6. 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 billion 6 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
- 7. Detecting fraud is a game of pattern recognition … A simple foundation of … Applied to financial … with modern technology statistical 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 formulas Baysian … • Claims records … and detect fraudulent • Approval chains activities B1Xi1 + B2Xi2 … BnXin + ei = yi • … 7 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
- 8. But combating fraud is a game of cat and mouse The patterns keep changing Hackers are thwarted only to come back through a different security hole Novell scams are being thought up for loopholes and exceptions in business workflows And the playing field keeps growing Volume and velocity of transactions Digitization of workflow records and approvals Source and type of transactions 8 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
- 9. With Vertica, you get a better mouse trap Your proprietary systems can’t keep up Expensive to scale for today’s “Big Data” real- time transaction streams Difficult to modify legacy analytic code and architectures to keep up with changing patterns With Vertica you stay a step ahead Rapidly create real-time high-speed transactional record datamarts on inexpensive platforms Open analytics platform for deep predictive fraudulent pattern modeling 9 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
- 10. 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 more 10 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
- 11. Vertica architecture – every element purpose built for 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 availability 11 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
- 12. Communication Service Provider fraud Estimated impact of $40 Billion p.a. worldwide Service Providers continue to see increases in volume & variety Continuous improvement necessary to alleviate – constantly moving targets 12 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
- 13. © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
- 14. Banking services fraud detection use case scenario Identifying credit card fraud (skimming) Historical reference dataset • Credit card skimming record for merchants • Merchant characteristics (size of store, popularity) • Credit record for card holder Real-time transactional data • Credit card transaction and card status Result: Merchant probability of skimming Implemented for a large American bank on Vertica 14 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
- 15. Identifying healthcare public/private insurance reimbursement improper payments use case scenario Historical 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 status Result: Provider’s probability of improper payments Implemented for a large American insurance carrier on Vertica 15 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
- 16. Staying ahead of credit card skimming Ability to incorporate the latest algorithms without proprietary code, leveraging “Big Data” and social media • Front-end outlier detection in multivariate data streams • Neural networks • Social network analysis Implemented analytics in TWENTY lines of code Different use cases, data sources and industries handled with the same pattern recognition scenario 16 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
- 17. Conclusion Get 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 reactive Where to find more information • bit.ly/VerticaFraud • www.vertica.com • my.vertica.com/evaluate/ • cselland@vertica.com • +1.617.3864523 17 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
- 18. Download Now Get the Mobile App Download content from this session with the free Mobile App at: m.hp.com/events 18 © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
- 19. Thank you © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
Notes de l'éditeur
- 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.